diff --git a/devel/_images/univariate_univariate_using_fixed_variance_11_0.png b/devel/_images/univariate_univariate_using_fixed_variance_11_0.png
index 123a31a296..9d9d3b28af 100644
Binary files a/devel/_images/univariate_univariate_using_fixed_variance_11_0.png and b/devel/_images/univariate_univariate_using_fixed_variance_11_0.png differ
diff --git a/devel/_images/univariate_univariate_using_fixed_variance_6_0.png b/devel/_images/univariate_univariate_using_fixed_variance_6_0.png
index 2364d015f9..cf896222c5 100644
Binary files a/devel/_images/univariate_univariate_using_fixed_variance_6_0.png and b/devel/_images/univariate_univariate_using_fixed_variance_6_0.png differ
diff --git a/devel/_modules/arch/bootstrap/base.html b/devel/_modules/arch/bootstrap/base.html
index 0a5653c09b..7b7ecd2267 100644
--- a/devel/_modules/arch/bootstrap/base.html
+++ b/devel/_modules/arch/bootstrap/base.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.base - arch v6.2.1 (+21)</title>
+        <title>arch.bootstrap.base - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -87,7 +87,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -97,7 +97,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -184,10 +184,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -2375,7 +2375,7 @@ <h1>Source code for arch.bootstrap.base</h1><div class="highlight"><pre>
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/devel/_modules/arch/bootstrap/multiple_comparison.html b/devel/_modules/arch/bootstrap/multiple_comparison.html
index 1e98b7eaf5..aeaa9faeb0 100644
--- a/devel/_modules/arch/bootstrap/multiple_comparison.html
+++ b/devel/_modules/arch/bootstrap/multiple_comparison.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.multiple_comparison - arch v6.2.1 (+21)</title>
+        <title>arch.bootstrap.multiple_comparison - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
@@ -87,7 +87,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -97,7 +97,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -184,10 +184,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -1302,7 +1302,7 @@ <h1>Source code for arch.bootstrap.multiple_comparison</h1><div class="highlight
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/devel/_modules/arch/covariance/kernel.html b/devel/_modules/arch/covariance/kernel.html
index ee9f157cc9..3133b622fb 100644
--- a/devel/_modules/arch/covariance/kernel.html
+++ b/devel/_modules/arch/covariance/kernel.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.covariance.kernel - arch v6.2.1 (+21)</title>
+        <title>arch.covariance.kernel - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
@@ -87,7 +87,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -97,7 +97,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -184,10 +184,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -1193,7 +1193,7 @@ <h1>Source code for arch.covariance.kernel</h1><div class="highlight"><pre>
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/devel/_modules/arch/unitroot/_engle_granger.html b/devel/_modules/arch/unitroot/_engle_granger.html
index 23044c287d..9d4c16c873 100644
--- a/devel/_modules/arch/unitroot/_engle_granger.html
+++ b/devel/_modules/arch/unitroot/_engle_granger.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot._engle_granger - arch v6.2.1 (+21)</title>
+        <title>arch.unitroot._engle_granger - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
@@ -87,7 +87,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -97,7 +97,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -184,10 +184,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -767,7 +767,7 @@ <h1>Source code for arch.unitroot._engle_granger</h1><div class="highlight"><pre
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/devel/_modules/arch/unitroot/_phillips_ouliaris.html b/devel/_modules/arch/unitroot/_phillips_ouliaris.html
index 00905331a5..2e83709dcd 100644
--- a/devel/_modules/arch/unitroot/_phillips_ouliaris.html
+++ b/devel/_modules/arch/unitroot/_phillips_ouliaris.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot._phillips_ouliaris - arch v6.2.1 (+21)</title>
+        <title>arch.unitroot._phillips_ouliaris - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
@@ -87,7 +87,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -97,7 +97,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -184,10 +184,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -978,7 +978,7 @@ <h1>Source code for arch.unitroot._phillips_ouliaris</h1><div class="highlight">
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index 5e208615f5..bbade8ce52 100644
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@@ -97,7 +97,7 @@
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diff --git a/devel/_modules/arch/unitroot/cointegration.html b/devel/_modules/arch/unitroot/cointegration.html
index f3c6f9dc11..0420a07c22 100644
--- a/devel/_modules/arch/unitroot/cointegration.html
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@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration - arch v6.2.1 (+21)</title>
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@@ -47,7 +47,7 @@
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@@ -87,7 +87,7 @@
 
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@@ -97,7 +97,7 @@
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@@ -184,10 +184,10 @@
 
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@@ -1674,7 +1674,7 @@ <h1>Source code for arch.unitroot.cointegration</h1><div class="highlight"><pre>
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-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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diff --git a/devel/_modules/arch/unitroot/unitroot.html b/devel/_modules/arch/unitroot/unitroot.html
index 3e498f6977..9466816180 100644
--- a/devel/_modules/arch/unitroot/unitroot.html
+++ b/devel/_modules/arch/unitroot/unitroot.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.unitroot - arch v6.2.1 (+21)</title>
+        <title>arch.unitroot.unitroot - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
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@@ -87,7 +87,7 @@
 
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+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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@@ -97,7 +97,7 @@
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+            arch v6.2.1 (+23)
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@@ -184,10 +184,10 @@
 
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-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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@@ -2477,7 +2477,7 @@ <h1>Source code for arch.unitroot.unitroot</h1><div class="highlight"><pre>
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-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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diff --git a/devel/_modules/arch/univariate/base.html b/devel/_modules/arch/univariate/base.html
index 2378539f83..30ed3492d4 100644
--- a/devel/_modules/arch/univariate/base.html
+++ b/devel/_modules/arch/univariate/base.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.base - arch v6.2.1 (+21)</title>
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@@ -47,7 +47,7 @@
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@@ -87,7 +87,7 @@
 
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@@ -97,7 +97,7 @@
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-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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@@ -184,10 +184,10 @@
 
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-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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+    arch v6.2.1 (+23)
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@@ -2688,7 +2688,7 @@ <h1>Source code for arch.univariate.base</h1><div class="highlight"><pre>
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-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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diff --git a/devel/_modules/arch/univariate/distribution.html b/devel/_modules/arch/univariate/distribution.html
index ebbfc7f36f..9f2ddc31ae 100644
--- a/devel/_modules/arch/univariate/distribution.html
+++ b/devel/_modules/arch/univariate/distribution.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.distribution - arch v6.2.1 (+21)</title>
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@@ -47,7 +47,7 @@
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@@ -87,7 +87,7 @@
 
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@@ -97,7 +97,7 @@
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@@ -184,10 +184,10 @@
 
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-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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     <div class="md-nav__source">
@@ -1948,7 +1948,7 @@ <h1>Source code for arch.univariate.distribution</h1><div class="highlight"><pre
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/devel/_modules/arch/univariate/mean.html b/devel/_modules/arch/univariate/mean.html
index 64101ad9b1..29b0f500c0 100644
--- a/devel/_modules/arch/univariate/mean.html
+++ b/devel/_modules/arch/univariate/mean.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.mean - arch v6.2.1 (+21)</title>
+        <title>arch.univariate.mean - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -87,7 +87,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -97,7 +97,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -184,10 +184,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -2524,7 +2524,7 @@ <h1>Source code for arch.univariate.mean</h1><div class="highlight"><pre>
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/devel/_modules/arch/univariate/recursions_python.html b/devel/_modules/arch/univariate/recursions_python.html
index 3e5896d2cf..b1d01847b9 100644
--- a/devel/_modules/arch/univariate/recursions_python.html
+++ b/devel/_modules/arch/univariate/recursions_python.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.recursions_python - arch v6.2.1 (+21)</title>
+        <title>arch.univariate.recursions_python - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -87,7 +87,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -97,7 +97,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -184,10 +184,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -1565,7 +1565,7 @@ <h1>Source code for arch.univariate.recursions_python</h1><div class="highlight"
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/devel/_modules/arch/univariate/volatility.html b/devel/_modules/arch/univariate/volatility.html
index a271bfeee5..e9d575e97e 100644
--- a/devel/_modules/arch/univariate/volatility.html
+++ b/devel/_modules/arch/univariate/volatility.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.volatility - arch v6.2.1 (+21)</title>
+        <title>arch.univariate.volatility - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -87,7 +87,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -97,7 +97,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -184,10 +184,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -540,7 +540,7 @@ <h1>Source code for arch.univariate.volatility</h1><div class="highlight"><pre>
 
 
 <div class="viewcode-block" id="VolatilityProcess">
-<a class="viewcode-back" href="../../../univariate/generated/arch.univariate.volatility.VolatilityProcess.html#arch.univariate.VolatilityProcess">[docs]</a>
+<a class="viewcode-back" href="../../../univariate/generated/arch.univariate.volatility.VolatilityProcess.html#arch.univariate.volatility.VolatilityProcess">[docs]</a>
 <span class="k">class</span> <span class="nc">VolatilityProcess</span><span class="p">(</span><span class="n">metaclass</span><span class="o">=</span><span class="n">ABCMeta</span><span class="p">):</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Abstract base class for ARCH models.  Allows the conditional mean model to be</span>
@@ -620,7 +620,7 @@ <h1>Source code for arch.univariate.volatility</h1><div class="highlight"><pre>
         <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_volatility_updater</span>
 
 <div class="viewcode-block" id="VolatilityProcess.update">
-<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.volatility.VolatilityProcess.update.html#arch.univariate.VolatilityProcess.update">[docs]</a>
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.volatility.VolatilityProcess.update.html#arch.univariate.volatility.VolatilityProcess.update">[docs]</a>
     <span class="k">def</span> <span class="nf">update</span><span class="p">(</span>
         <span class="bp">self</span><span class="p">,</span>
         <span class="n">index</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
@@ -868,7 +868,7 @@ <h1>Source code for arch.univariate.volatility</h1><div class="highlight"><pre>
         <span class="p">)</span>
 
 <div class="viewcode-block" id="VolatilityProcess.variance_bounds">
-<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.volatility.VolatilityProcess.variance_bounds.html#arch.univariate.VolatilityProcess.variance_bounds">[docs]</a>
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.volatility.VolatilityProcess.variance_bounds.html#arch.univariate.volatility.VolatilityProcess.variance_bounds">[docs]</a>
     <span class="k">def</span> <span class="nf">variance_bounds</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">resids</span><span class="p">:</span> <span class="n">Float64Array</span><span class="p">,</span> <span class="n">power</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mf">2.0</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Float64Array</span><span class="p">:</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">        Construct loose bounds for conditional variances.</span>
@@ -915,7 +915,7 @@ <h1>Source code for arch.univariate.volatility</h1><div class="highlight"><pre>
 
 
 <div class="viewcode-block" id="VolatilityProcess.starting_values">
-<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.volatility.VolatilityProcess.starting_values.html#arch.univariate.VolatilityProcess.starting_values">[docs]</a>
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.volatility.VolatilityProcess.starting_values.html#arch.univariate.volatility.VolatilityProcess.starting_values">[docs]</a>
     <span class="nd">@abstractmethod</span>
     <span class="k">def</span> <span class="nf">starting_values</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">resids</span><span class="p">:</span> <span class="n">Float64Array</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Float64Array</span><span class="p">:</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
@@ -935,7 +935,7 @@ <h1>Source code for arch.univariate.volatility</h1><div class="highlight"><pre>
 
 
 <div class="viewcode-block" id="VolatilityProcess.backcast">
-<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.volatility.VolatilityProcess.backcast.html#arch.univariate.VolatilityProcess.backcast">[docs]</a>
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.volatility.VolatilityProcess.backcast.html#arch.univariate.volatility.VolatilityProcess.backcast">[docs]</a>
     <span class="k">def</span> <span class="nf">backcast</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">resids</span><span class="p">:</span> <span class="n">Float64Array</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">float</span> <span class="o">|</span> <span class="n">Float64Array</span><span class="p">:</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">        Construct values for backcasting to start the recursion</span>
@@ -958,7 +958,7 @@ <h1>Source code for arch.univariate.volatility</h1><div class="highlight"><pre>
 
 
 <div class="viewcode-block" id="VolatilityProcess.backcast_transform">
-<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.volatility.VolatilityProcess.backcast_transform.html#arch.univariate.VolatilityProcess.backcast_transform">[docs]</a>
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.volatility.VolatilityProcess.backcast_transform.html#arch.univariate.volatility.VolatilityProcess.backcast_transform">[docs]</a>
     <span class="k">def</span> <span class="nf">backcast_transform</span><span class="p">(</span>
         <span class="bp">self</span><span class="p">,</span> <span class="n">backcast</span><span class="p">:</span> <span class="nb">float</span> <span class="o">|</span> <span class="n">Float64Array</span>
     <span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">float</span> <span class="o">|</span> <span class="n">Float64Array</span><span class="p">:</span>
@@ -981,7 +981,7 @@ <h1>Source code for arch.univariate.volatility</h1><div class="highlight"><pre>
 
 
 <div class="viewcode-block" id="VolatilityProcess.bounds">
-<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.volatility.VolatilityProcess.bounds.html#arch.univariate.VolatilityProcess.bounds">[docs]</a>
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.volatility.VolatilityProcess.bounds.html#arch.univariate.volatility.VolatilityProcess.bounds">[docs]</a>
     <span class="nd">@abstractmethod</span>
     <span class="k">def</span> <span class="nf">bounds</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">resids</span><span class="p">:</span> <span class="n">Float64Array</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">list</span><span class="p">[</span><span class="nb">tuple</span><span class="p">[</span><span class="nb">float</span><span class="p">,</span> <span class="nb">float</span><span class="p">]]:</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
@@ -1000,7 +1000,7 @@ <h1>Source code for arch.univariate.volatility</h1><div class="highlight"><pre>
 
 
 <div class="viewcode-block" id="VolatilityProcess.compute_variance">
-<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.volatility.VolatilityProcess.compute_variance.html#arch.univariate.VolatilityProcess.compute_variance">[docs]</a>
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.volatility.VolatilityProcess.compute_variance.html#arch.univariate.volatility.VolatilityProcess.compute_variance">[docs]</a>
     <span class="nd">@abstractmethod</span>
     <span class="k">def</span> <span class="nf">compute_variance</span><span class="p">(</span>
         <span class="bp">self</span><span class="p">,</span>
@@ -1030,7 +1030,7 @@ <h1>Source code for arch.univariate.volatility</h1><div class="highlight"><pre>
 
 
 <div class="viewcode-block" id="VolatilityProcess.constraints">
-<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.volatility.VolatilityProcess.constraints.html#arch.univariate.VolatilityProcess.constraints">[docs]</a>
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.volatility.VolatilityProcess.constraints.html#arch.univariate.volatility.VolatilityProcess.constraints">[docs]</a>
     <span class="nd">@abstractmethod</span>
     <span class="k">def</span> <span class="nf">constraints</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">tuple</span><span class="p">[</span><span class="n">Float64Array</span><span class="p">,</span> <span class="n">Float64Array</span><span class="p">]:</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
@@ -1052,7 +1052,7 @@ <h1>Source code for arch.univariate.volatility</h1><div class="highlight"><pre>
 
 
 <div class="viewcode-block" id="VolatilityProcess.forecast">
-<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.volatility.VolatilityProcess.forecast.html#arch.univariate.VolatilityProcess.forecast">[docs]</a>
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.volatility.VolatilityProcess.forecast.html#arch.univariate.volatility.VolatilityProcess.forecast">[docs]</a>
     <span class="k">def</span> <span class="nf">forecast</span><span class="p">(</span>
         <span class="bp">self</span><span class="p">,</span>
         <span class="n">parameters</span><span class="p">:</span> <span class="n">ArrayLike1D</span><span class="p">,</span>
@@ -1162,7 +1162,7 @@ <h1>Source code for arch.univariate.volatility</h1><div class="highlight"><pre>
 
 
 <div class="viewcode-block" id="VolatilityProcess.simulate">
-<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.volatility.VolatilityProcess.simulate.html#arch.univariate.VolatilityProcess.simulate">[docs]</a>
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.volatility.VolatilityProcess.simulate.html#arch.univariate.volatility.VolatilityProcess.simulate">[docs]</a>
     <span class="nd">@abstractmethod</span>
     <span class="k">def</span> <span class="nf">simulate</span><span class="p">(</span>
         <span class="bp">self</span><span class="p">,</span>
@@ -1217,7 +1217,7 @@ <h1>Source code for arch.univariate.volatility</h1><div class="highlight"><pre>
         <span class="k">return</span> <span class="nb">float</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_normal</span><span class="o">.</span><span class="n">loglikelihood</span><span class="p">([],</span> <span class="n">resids</span><span class="p">,</span> <span class="n">sigma2</span><span class="p">))</span>
 
 <div class="viewcode-block" id="VolatilityProcess.parameter_names">
-<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.volatility.VolatilityProcess.parameter_names.html#arch.univariate.VolatilityProcess.parameter_names">[docs]</a>
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.volatility.VolatilityProcess.parameter_names.html#arch.univariate.volatility.VolatilityProcess.parameter_names">[docs]</a>
     <span class="nd">@abstractmethod</span>
     <span class="k">def</span> <span class="nf">parameter_names</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">list</span><span class="p">[</span><span class="nb">str</span><span class="p">]:</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
@@ -1233,7 +1233,7 @@ <h1>Source code for arch.univariate.volatility</h1><div class="highlight"><pre>
 
 
 <div class="viewcode-block" id="ConstantVariance">
-<a class="viewcode-back" href="../../../univariate/generated/arch.univariate.ConstantVariance.html#arch.univariate.ConstantVariance">[docs]</a>
+<a class="viewcode-back" href="../../../univariate/generated/arch.univariate.ConstantVariance.html#arch.univariate.volatility.ConstantVariance">[docs]</a>
 <span class="k">class</span> <span class="nc">ConstantVariance</span><span class="p">(</span><span class="n">VolatilityProcess</span><span class="p">,</span> <span class="n">metaclass</span><span class="o">=</span><span class="n">AbstractDocStringInheritor</span><span class="p">):</span>
 <span class="w">    </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Constant volatility process</span>
@@ -1250,7 +1250,7 @@ <h1>Source code for arch.univariate.volatility</h1><div class="highlight"><pre>
         <span class="bp">self</span><span class="o">.</span><span class="n">closed_form</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span>
 
 <div class="viewcode-block" id="ConstantVariance.compute_variance">
-<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.ConstantVariance.compute_variance.html#arch.univariate.ConstantVariance.compute_variance">[docs]</a>
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.ConstantVariance.compute_variance.html#arch.univariate.volatility.ConstantVariance.compute_variance">[docs]</a>
     <span class="k">def</span> <span class="nf">compute_variance</span><span class="p">(</span>
         <span class="bp">self</span><span class="p">,</span>
         <span class="n">parameters</span><span class="p">:</span> <span class="n">Float64Array</span><span class="p">,</span>
@@ -1264,13 +1264,13 @@ <h1>Source code for arch.univariate.volatility</h1><div class="highlight"><pre>
 
 
 <div class="viewcode-block" id="ConstantVariance.starting_values">
-<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.ConstantVariance.starting_values.html#arch.univariate.ConstantVariance.starting_values">[docs]</a>
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.ConstantVariance.starting_values.html#arch.univariate.volatility.ConstantVariance.starting_values">[docs]</a>
     <span class="k">def</span> <span class="nf">starting_values</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">resids</span><span class="p">:</span> <span class="n">Float64Array</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Float64Array</span><span class="p">:</span>
         <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="n">resids</span><span class="o">.</span><span class="n">var</span><span class="p">()])</span></div>
 
 
 <div class="viewcode-block" id="ConstantVariance.simulate">
-<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.ConstantVariance.simulate.html#arch.univariate.ConstantVariance.simulate">[docs]</a>
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.ConstantVariance.simulate.html#arch.univariate.volatility.ConstantVariance.simulate">[docs]</a>
     <span class="k">def</span> <span class="nf">simulate</span><span class="p">(</span>
         <span class="bp">self</span><span class="p">,</span>
         <span class="n">parameters</span><span class="p">:</span> <span class="n">Sequence</span><span class="p">[</span><span class="nb">int</span> <span class="o">|</span> <span class="nb">float</span><span class="p">]</span> <span class="o">|</span> <span class="n">ArrayLike1D</span><span class="p">,</span>
@@ -1287,13 +1287,13 @@ <h1>Source code for arch.univariate.volatility</h1><div class="highlight"><pre>
 
 
 <div class="viewcode-block" id="ConstantVariance.constraints">
-<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.ConstantVariance.constraints.html#arch.univariate.ConstantVariance.constraints">[docs]</a>
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.ConstantVariance.constraints.html#arch.univariate.volatility.ConstantVariance.constraints">[docs]</a>
     <span class="k">def</span> <span class="nf">constraints</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">tuple</span><span class="p">[</span><span class="n">Float64Array</span><span class="p">,</span> <span class="n">Float64Array</span><span class="p">]:</span>
         <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)),</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span></div>
 
 
 <div class="viewcode-block" id="ConstantVariance.backcast_transform">
-<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.ConstantVariance.backcast_transform.html#arch.univariate.ConstantVariance.backcast_transform">[docs]</a>
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.ConstantVariance.backcast_transform.html#arch.univariate.volatility.ConstantVariance.backcast_transform">[docs]</a>
     <span class="k">def</span> <span class="nf">backcast_transform</span><span class="p">(</span>
         <span class="bp">self</span><span class="p">,</span> <span class="n">backcast</span><span class="p">:</span> <span class="nb">float</span> <span class="o">|</span> <span class="n">Float64Array</span>
     <span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">float</span> <span class="o">|</span> <span class="n">Float64Array</span><span class="p">:</span>
@@ -1302,20 +1302,20 @@ <h1>Source code for arch.univariate.volatility</h1><div class="highlight"><pre>
 
 
 <div class="viewcode-block" id="ConstantVariance.backcast">
-<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.ConstantVariance.backcast.html#arch.univariate.ConstantVariance.backcast">[docs]</a>
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.ConstantVariance.backcast.html#arch.univariate.volatility.ConstantVariance.backcast">[docs]</a>
     <span class="k">def</span> <span class="nf">backcast</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">resids</span><span class="p">:</span> <span class="n">Float64Array</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">float</span> <span class="o">|</span> <span class="n">Float64Array</span><span class="p">:</span>
         <span class="k">return</span> <span class="nb">float</span><span class="p">(</span><span class="n">resids</span><span class="o">.</span><span class="n">var</span><span class="p">())</span></div>
 
 
 <div class="viewcode-block" id="ConstantVariance.bounds">
-<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.ConstantVariance.bounds.html#arch.univariate.ConstantVariance.bounds">[docs]</a>
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.ConstantVariance.bounds.html#arch.univariate.volatility.ConstantVariance.bounds">[docs]</a>
     <span class="k">def</span> <span class="nf">bounds</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">resids</span><span class="p">:</span> <span class="n">Float64Array</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">list</span><span class="p">[</span><span class="nb">tuple</span><span class="p">[</span><span class="nb">float</span><span class="p">,</span> <span class="nb">float</span><span class="p">]]:</span>
         <span class="n">v</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">resids</span><span class="o">.</span><span class="n">var</span><span class="p">())</span>
         <span class="k">return</span> <span class="p">[(</span><span class="n">v</span> <span class="o">/</span> <span class="mf">100000.0</span><span class="p">,</span> <span class="mf">10.0</span> <span class="o">*</span> <span class="p">(</span><span class="n">v</span> <span class="o">+</span> <span class="nb">float</span><span class="p">(</span><span class="n">resids</span><span class="o">.</span><span class="n">mean</span><span class="p">())</span> <span class="o">**</span> <span class="mf">2.0</span><span class="p">))]</span></div>
 
 
 <div class="viewcode-block" id="ConstantVariance.parameter_names">
-<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.ConstantVariance.parameter_names.html#arch.univariate.ConstantVariance.parameter_names">[docs]</a>
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.ConstantVariance.parameter_names.html#arch.univariate.volatility.ConstantVariance.parameter_names">[docs]</a>
     <span class="k">def</span> <span class="nf">parameter_names</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">list</span><span class="p">[</span><span class="nb">str</span><span class="p">]:</span>
         <span class="k">return</span> <span class="p">[</span><span class="s2">&quot;sigma2&quot;</span><span class="p">]</span></div>
 
@@ -1368,7 +1368,7 @@ <h1>Source code for arch.univariate.volatility</h1><div class="highlight"><pre>
 
 
 <div class="viewcode-block" id="GARCH">
-<a class="viewcode-back" href="../../../univariate/generated/arch.univariate.GARCH.html#arch.univariate.GARCH">[docs]</a>
+<a class="viewcode-back" href="../../../univariate/generated/arch.univariate.GARCH.html#arch.univariate.volatility.GARCH">[docs]</a>
 <span class="k">class</span> <span class="nc">GARCH</span><span class="p">(</span><span class="n">VolatilityProcess</span><span class="p">,</span> <span class="n">metaclass</span><span class="o">=</span><span class="n">AbstractDocStringInheritor</span><span class="p">):</span>
 <span class="w">    </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    GARCH and related model estimation</span>
@@ -1460,7 +1460,7 @@ <h1>Source code for arch.univariate.volatility</h1><div class="highlight"><pre>
         <span class="k">return</span> <span class="n">descr</span>
 
 <div class="viewcode-block" id="GARCH.variance_bounds">
-<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.GARCH.variance_bounds.html#arch.univariate.GARCH.variance_bounds">[docs]</a>
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.GARCH.variance_bounds.html#arch.univariate.volatility.GARCH.variance_bounds">[docs]</a>
     <span class="k">def</span> <span class="nf">variance_bounds</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">resids</span><span class="p">:</span> <span class="n">Float64Array</span><span class="p">,</span> <span class="n">power</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mf">2.0</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Float64Array</span><span class="p">:</span>
         <span class="k">return</span> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">variance_bounds</span><span class="p">(</span><span class="n">resids</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">power</span><span class="p">)</span></div>
 
@@ -1490,7 +1490,7 @@ <h1>Source code for arch.univariate.volatility</h1><div class="highlight"><pre>
                 <span class="k">return</span> <span class="sa">f</span><span class="s2">&quot;Asym. Power GARCH (power: </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">power</span><span class="si">:</span><span class="s2">0.1f</span><span class="si">}</span><span class="s2">)&quot;</span>
 
 <div class="viewcode-block" id="GARCH.bounds">
-<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.GARCH.bounds.html#arch.univariate.GARCH.bounds">[docs]</a>
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.GARCH.bounds.html#arch.univariate.volatility.GARCH.bounds">[docs]</a>
     <span class="k">def</span> <span class="nf">bounds</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">resids</span><span class="p">:</span> <span class="n">Float64Array</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">list</span><span class="p">[</span><span class="nb">tuple</span><span class="p">[</span><span class="nb">float</span><span class="p">,</span> <span class="nb">float</span><span class="p">]]:</span>
         <span class="n">v</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="nb">abs</span><span class="p">(</span><span class="n">resids</span><span class="p">)</span> <span class="o">**</span> <span class="bp">self</span><span class="o">.</span><span class="n">power</span><span class="p">))</span>
 
@@ -1508,7 +1508,7 @@ <h1>Source code for arch.univariate.volatility</h1><div class="highlight"><pre>
 
 
 <div class="viewcode-block" id="GARCH.constraints">
-<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.GARCH.constraints.html#arch.univariate.GARCH.constraints">[docs]</a>
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.GARCH.constraints.html#arch.univariate.volatility.GARCH.constraints">[docs]</a>
     <span class="k">def</span> <span class="nf">constraints</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">tuple</span><span class="p">[</span><span class="n">Float64Array</span><span class="p">,</span> <span class="n">Float64Array</span><span class="p">]:</span>
         <span class="n">p</span><span class="p">,</span> <span class="n">o</span><span class="p">,</span> <span class="n">q</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">p</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">o</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">q</span>
         <span class="n">k_arch</span> <span class="o">=</span> <span class="n">p</span> <span class="o">+</span> <span class="n">o</span> <span class="o">+</span> <span class="n">q</span>
@@ -1531,7 +1531,7 @@ <h1>Source code for arch.univariate.volatility</h1><div class="highlight"><pre>
 
 
 <div class="viewcode-block" id="GARCH.compute_variance">
-<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.GARCH.compute_variance.html#arch.univariate.GARCH.compute_variance">[docs]</a>
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.GARCH.compute_variance.html#arch.univariate.volatility.GARCH.compute_variance">[docs]</a>
     <span class="k">def</span> <span class="nf">compute_variance</span><span class="p">(</span>
         <span class="bp">self</span><span class="p">,</span>
         <span class="n">parameters</span><span class="p">:</span> <span class="n">Float64Array</span><span class="p">,</span>
@@ -1559,7 +1559,7 @@ <h1>Source code for arch.univariate.volatility</h1><div class="highlight"><pre>
 
 
 <div class="viewcode-block" id="GARCH.backcast_transform">
-<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.GARCH.backcast_transform.html#arch.univariate.GARCH.backcast_transform">[docs]</a>
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.GARCH.backcast_transform.html#arch.univariate.volatility.GARCH.backcast_transform">[docs]</a>
     <span class="k">def</span> <span class="nf">backcast_transform</span><span class="p">(</span>
         <span class="bp">self</span><span class="p">,</span> <span class="n">backcast</span><span class="p">:</span> <span class="nb">float</span> <span class="o">|</span> <span class="n">Float64Array</span>
     <span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">float</span> <span class="o">|</span> <span class="n">Float64Array</span><span class="p">:</span>
@@ -1568,7 +1568,7 @@ <h1>Source code for arch.univariate.volatility</h1><div class="highlight"><pre>
 
 
 <div class="viewcode-block" id="GARCH.backcast">
-<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.GARCH.backcast.html#arch.univariate.GARCH.backcast">[docs]</a>
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.GARCH.backcast.html#arch.univariate.volatility.GARCH.backcast">[docs]</a>
     <span class="k">def</span> <span class="nf">backcast</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">resids</span><span class="p">:</span> <span class="n">Float64Array</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">float</span> <span class="o">|</span> <span class="n">Float64Array</span><span class="p">:</span>
         <span class="n">power</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">power</span>
         <span class="n">tau</span> <span class="o">=</span> <span class="nb">min</span><span class="p">(</span><span class="mi">75</span><span class="p">,</span> <span class="n">resids</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
@@ -1580,7 +1580,7 @@ <h1>Source code for arch.univariate.volatility</h1><div class="highlight"><pre>
 
 
 <div class="viewcode-block" id="GARCH.simulate">
-<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.GARCH.simulate.html#arch.univariate.GARCH.simulate">[docs]</a>
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.GARCH.simulate.html#arch.univariate.volatility.GARCH.simulate">[docs]</a>
     <span class="k">def</span> <span class="nf">simulate</span><span class="p">(</span>
         <span class="bp">self</span><span class="p">,</span>
         <span class="n">parameters</span><span class="p">:</span> <span class="n">Sequence</span><span class="p">[</span><span class="nb">int</span> <span class="o">|</span> <span class="nb">float</span><span class="p">]</span> <span class="o">|</span> <span class="n">ArrayLike1D</span><span class="p">,</span>
@@ -1637,7 +1637,7 @@ <h1>Source code for arch.univariate.volatility</h1><div class="highlight"><pre>
 
 
 <div class="viewcode-block" id="GARCH.starting_values">
-<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.GARCH.starting_values.html#arch.univariate.GARCH.starting_values">[docs]</a>
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.GARCH.starting_values.html#arch.univariate.volatility.GARCH.starting_values">[docs]</a>
     <span class="k">def</span> <span class="nf">starting_values</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">resids</span><span class="p">:</span> <span class="n">Float64Array</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Float64Array</span><span class="p">:</span>
         <span class="n">p</span><span class="p">,</span> <span class="n">o</span><span class="p">,</span> <span class="n">q</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">p</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">o</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">q</span>
         <span class="n">power</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">power</span>
@@ -1673,7 +1673,7 @@ <h1>Source code for arch.univariate.volatility</h1><div class="highlight"><pre>
 
 
 <div class="viewcode-block" id="GARCH.parameter_names">
-<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.GARCH.parameter_names.html#arch.univariate.GARCH.parameter_names">[docs]</a>
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.GARCH.parameter_names.html#arch.univariate.volatility.GARCH.parameter_names">[docs]</a>
     <span class="k">def</span> <span class="nf">parameter_names</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">list</span><span class="p">[</span><span class="nb">str</span><span class="p">]:</span>
         <span class="k">return</span> <span class="n">_common_names</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">p</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">o</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">q</span><span class="p">)</span></div>
 
@@ -1862,7 +1862,7 @@ <h1>Source code for arch.univariate.volatility</h1><div class="highlight"><pre>
 
 
 <div class="viewcode-block" id="HARCH">
-<a class="viewcode-back" href="../../../univariate/generated/arch.univariate.HARCH.html#arch.univariate.HARCH">[docs]</a>
+<a class="viewcode-back" href="../../../univariate/generated/arch.univariate.HARCH.html#arch.univariate.volatility.HARCH">[docs]</a>
 <span class="k">class</span> <span class="nc">HARCH</span><span class="p">(</span><span class="n">VolatilityProcess</span><span class="p">,</span> <span class="n">metaclass</span><span class="o">=</span><span class="n">AbstractDocStringInheritor</span><span class="p">):</span>
 <span class="w">    </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Heterogeneous ARCH process</span>
@@ -1926,7 +1926,7 @@ <h1>Source code for arch.univariate.volatility</h1><div class="highlight"><pre>
         <span class="k">return</span> <span class="n">descr</span>
 
 <div class="viewcode-block" id="HARCH.bounds">
-<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.HARCH.bounds.html#arch.univariate.HARCH.bounds">[docs]</a>
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.HARCH.bounds.html#arch.univariate.volatility.HARCH.bounds">[docs]</a>
     <span class="k">def</span> <span class="nf">bounds</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">resids</span><span class="p">:</span> <span class="n">Float64Array</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">list</span><span class="p">[</span><span class="nb">tuple</span><span class="p">[</span><span class="nb">float</span><span class="p">,</span> <span class="nb">float</span><span class="p">]]:</span>
         <span class="n">lags</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">lags</span>
         <span class="n">k_arch</span> <span class="o">=</span> <span class="n">lags</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
@@ -1938,7 +1938,7 @@ <h1>Source code for arch.univariate.volatility</h1><div class="highlight"><pre>
 
 
 <div class="viewcode-block" id="HARCH.constraints">
-<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.HARCH.constraints.html#arch.univariate.HARCH.constraints">[docs]</a>
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.HARCH.constraints.html#arch.univariate.volatility.HARCH.constraints">[docs]</a>
     <span class="k">def</span> <span class="nf">constraints</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">tuple</span><span class="p">[</span><span class="n">Float64Array</span><span class="p">,</span> <span class="n">Float64Array</span><span class="p">]:</span>
         <span class="n">k_arch</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_num_lags</span>
         <span class="n">a</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">k_arch</span> <span class="o">+</span> <span class="mi">2</span><span class="p">,</span> <span class="n">k_arch</span> <span class="o">+</span> <span class="mi">1</span><span class="p">))</span>
@@ -1951,7 +1951,7 @@ <h1>Source code for arch.univariate.volatility</h1><div class="highlight"><pre>
 
 
 <div class="viewcode-block" id="HARCH.compute_variance">
-<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.HARCH.compute_variance.html#arch.univariate.HARCH.compute_variance">[docs]</a>
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.HARCH.compute_variance.html#arch.univariate.volatility.HARCH.compute_variance">[docs]</a>
     <span class="k">def</span> <span class="nf">compute_variance</span><span class="p">(</span>
         <span class="bp">self</span><span class="p">,</span>
         <span class="n">parameters</span><span class="p">:</span> <span class="n">Float64Array</span><span class="p">,</span>
@@ -1970,7 +1970,7 @@ <h1>Source code for arch.univariate.volatility</h1><div class="highlight"><pre>
 
 
 <div class="viewcode-block" id="HARCH.simulate">
-<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.HARCH.simulate.html#arch.univariate.HARCH.simulate">[docs]</a>
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.HARCH.simulate.html#arch.univariate.volatility.HARCH.simulate">[docs]</a>
     <span class="k">def</span> <span class="nf">simulate</span><span class="p">(</span>
         <span class="bp">self</span><span class="p">,</span>
         <span class="n">parameters</span><span class="p">:</span> <span class="n">Sequence</span><span class="p">[</span><span class="nb">int</span> <span class="o">|</span> <span class="nb">float</span><span class="p">]</span> <span class="o">|</span> <span class="n">ArrayLike1D</span><span class="p">,</span>
@@ -2007,7 +2007,7 @@ <h1>Source code for arch.univariate.volatility</h1><div class="highlight"><pre>
 
 
 <div class="viewcode-block" id="HARCH.starting_values">
-<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.HARCH.starting_values.html#arch.univariate.HARCH.starting_values">[docs]</a>
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.HARCH.starting_values.html#arch.univariate.volatility.HARCH.starting_values">[docs]</a>
     <span class="k">def</span> <span class="nf">starting_values</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">resids</span><span class="p">:</span> <span class="n">Float64Array</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Float64Array</span><span class="p">:</span>
         <span class="n">k_arch</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_num_lags</span>
 
@@ -2019,7 +2019,7 @@ <h1>Source code for arch.univariate.volatility</h1><div class="highlight"><pre>
 
 
 <div class="viewcode-block" id="HARCH.parameter_names">
-<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.HARCH.parameter_names.html#arch.univariate.HARCH.parameter_names">[docs]</a>
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.HARCH.parameter_names.html#arch.univariate.volatility.HARCH.parameter_names">[docs]</a>
     <span class="k">def</span> <span class="nf">parameter_names</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">list</span><span class="p">[</span><span class="nb">str</span><span class="p">]:</span>
         <span class="n">names</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;omega&quot;</span><span class="p">]</span>
         <span class="n">lags</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">lags</span>
@@ -2119,7 +2119,7 @@ <h1>Source code for arch.univariate.volatility</h1><div class="highlight"><pre>
 
 
 <div class="viewcode-block" id="MIDASHyperbolic">
-<a class="viewcode-back" href="../../../univariate/generated/arch.univariate.MIDASHyperbolic.html#arch.univariate.MIDASHyperbolic">[docs]</a>
+<a class="viewcode-back" href="../../../univariate/generated/arch.univariate.MIDASHyperbolic.html#arch.univariate.volatility.MIDASHyperbolic">[docs]</a>
 <span class="k">class</span> <span class="nc">MIDASHyperbolic</span><span class="p">(</span><span class="n">VolatilityProcess</span><span class="p">,</span> <span class="n">metaclass</span><span class="o">=</span><span class="n">AbstractDocStringInheritor</span><span class="p">):</span>
 <span class="w">    </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    MIDAS Hyperbolic ARCH process</span>
@@ -2188,7 +2188,7 @@ <h1>Source code for arch.univariate.volatility</h1><div class="highlight"><pre>
         <span class="k">return</span> <span class="n">descr</span>
 
 <div class="viewcode-block" id="MIDASHyperbolic.bounds">
-<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.MIDASHyperbolic.bounds.html#arch.univariate.MIDASHyperbolic.bounds">[docs]</a>
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.MIDASHyperbolic.bounds.html#arch.univariate.volatility.MIDASHyperbolic.bounds">[docs]</a>
     <span class="k">def</span> <span class="nf">bounds</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">resids</span><span class="p">:</span> <span class="n">Float64Array</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">list</span><span class="p">[</span><span class="nb">tuple</span><span class="p">[</span><span class="nb">float</span><span class="p">,</span> <span class="nb">float</span><span class="p">]]:</span>
         <span class="n">bounds</span> <span class="o">=</span> <span class="p">[(</span><span class="mf">0.0</span><span class="p">,</span> <span class="mi">10</span> <span class="o">*</span> <span class="nb">float</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">resids</span><span class="o">**</span><span class="mf">2.0</span><span class="p">)))]</span>  <span class="c1"># omega</span>
         <span class="n">bounds</span><span class="o">.</span><span class="n">extend</span><span class="p">([(</span><span class="mf">0.0</span><span class="p">,</span> <span class="mf">1.0</span><span class="p">)])</span>  <span class="c1"># 0 &lt;= alpha &lt; 1</span>
@@ -2200,7 +2200,7 @@ <h1>Source code for arch.univariate.volatility</h1><div class="highlight"><pre>
 
 
 <div class="viewcode-block" id="MIDASHyperbolic.constraints">
-<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.MIDASHyperbolic.constraints.html#arch.univariate.MIDASHyperbolic.constraints">[docs]</a>
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.MIDASHyperbolic.constraints.html#arch.univariate.volatility.MIDASHyperbolic.constraints">[docs]</a>
     <span class="k">def</span> <span class="nf">constraints</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">tuple</span><span class="p">[</span><span class="n">Float64Array</span><span class="p">,</span> <span class="n">Float64Array</span><span class="p">]:</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">        Constraints</span>
@@ -2241,7 +2241,7 @@ <h1>Source code for arch.univariate.volatility</h1><div class="highlight"><pre>
 
 
 <div class="viewcode-block" id="MIDASHyperbolic.compute_variance">
-<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.MIDASHyperbolic.compute_variance.html#arch.univariate.MIDASHyperbolic.compute_variance">[docs]</a>
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.MIDASHyperbolic.compute_variance.html#arch.univariate.volatility.MIDASHyperbolic.compute_variance">[docs]</a>
     <span class="k">def</span> <span class="nf">compute_variance</span><span class="p">(</span>
         <span class="bp">self</span><span class="p">,</span>
         <span class="n">parameters</span><span class="p">:</span> <span class="n">Float64Array</span><span class="p">,</span>
@@ -2263,7 +2263,7 @@ <h1>Source code for arch.univariate.volatility</h1><div class="highlight"><pre>
 
 
 <div class="viewcode-block" id="MIDASHyperbolic.simulate">
-<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.MIDASHyperbolic.simulate.html#arch.univariate.MIDASHyperbolic.simulate">[docs]</a>
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.MIDASHyperbolic.simulate.html#arch.univariate.volatility.MIDASHyperbolic.simulate">[docs]</a>
     <span class="k">def</span> <span class="nf">simulate</span><span class="p">(</span>
         <span class="bp">self</span><span class="p">,</span>
         <span class="n">parameters</span><span class="p">:</span> <span class="n">Sequence</span><span class="p">[</span><span class="nb">int</span> <span class="o">|</span> <span class="nb">float</span><span class="p">]</span> <span class="o">|</span> <span class="n">ArrayLike1D</span><span class="p">,</span>
@@ -2312,7 +2312,7 @@ <h1>Source code for arch.univariate.volatility</h1><div class="highlight"><pre>
 
 
 <div class="viewcode-block" id="MIDASHyperbolic.starting_values">
-<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.MIDASHyperbolic.starting_values.html#arch.univariate.MIDASHyperbolic.starting_values">[docs]</a>
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.MIDASHyperbolic.starting_values.html#arch.univariate.volatility.MIDASHyperbolic.starting_values">[docs]</a>
     <span class="k">def</span> <span class="nf">starting_values</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">resids</span><span class="p">:</span> <span class="n">Float64Array</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Float64Array</span><span class="p">:</span>
         <span class="n">theta</span> <span class="o">=</span> <span class="p">[</span><span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">,</span> <span class="mf">0.8</span><span class="p">,</span> <span class="mf">0.9</span><span class="p">]</span>
         <span class="n">alpha</span> <span class="o">=</span> <span class="p">[</span><span class="mf">0.8</span><span class="p">,</span> <span class="mf">0.9</span><span class="p">,</span> <span class="mf">0.95</span><span class="p">,</span> <span class="mf">0.98</span><span class="p">]</span>
@@ -2343,7 +2343,7 @@ <h1>Source code for arch.univariate.volatility</h1><div class="highlight"><pre>
 
 
 <div class="viewcode-block" id="MIDASHyperbolic.parameter_names">
-<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.MIDASHyperbolic.parameter_names.html#arch.univariate.MIDASHyperbolic.parameter_names">[docs]</a>
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.MIDASHyperbolic.parameter_names.html#arch.univariate.volatility.MIDASHyperbolic.parameter_names">[docs]</a>
     <span class="k">def</span> <span class="nf">parameter_names</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">list</span><span class="p">[</span><span class="nb">str</span><span class="p">]:</span>
         <span class="n">names</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;omega&quot;</span><span class="p">,</span> <span class="s2">&quot;alpha&quot;</span><span class="p">,</span> <span class="s2">&quot;theta&quot;</span><span class="p">]</span>
         <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_asym</span><span class="p">:</span>
@@ -2471,7 +2471,7 @@ <h1>Source code for arch.univariate.volatility</h1><div class="highlight"><pre>
 
 
 <div class="viewcode-block" id="ARCH">
-<a class="viewcode-back" href="../../../univariate/generated/arch.univariate.ARCH.html#arch.univariate.ARCH">[docs]</a>
+<a class="viewcode-back" href="../../../univariate/generated/arch.univariate.ARCH.html#arch.univariate.volatility.ARCH">[docs]</a>
 <span class="k">class</span> <span class="nc">ARCH</span><span class="p">(</span><span class="n">GARCH</span><span class="p">):</span>
 <span class="w">    </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    ARCH process</span>
@@ -2506,7 +2506,7 @@ <h1>Source code for arch.univariate.volatility</h1><div class="highlight"><pre>
         <span class="bp">self</span><span class="o">.</span><span class="n">_num_params</span> <span class="o">=</span> <span class="n">p</span> <span class="o">+</span> <span class="mi">1</span>
 
 <div class="viewcode-block" id="ARCH.starting_values">
-<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.ARCH.starting_values.html#arch.univariate.ARCH.starting_values">[docs]</a>
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.ARCH.starting_values.html#arch.univariate.volatility.ARCH.starting_values">[docs]</a>
     <span class="k">def</span> <span class="nf">starting_values</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">resids</span><span class="p">:</span> <span class="n">Float64Array</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Float64Array</span><span class="p">:</span>
         <span class="n">p</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">p</span>
 
@@ -2527,7 +2527,7 @@ <h1>Source code for arch.univariate.volatility</h1><div class="highlight"><pre>
 
 
 <div class="viewcode-block" id="EWMAVariance">
-<a class="viewcode-back" href="../../../univariate/generated/arch.univariate.EWMAVariance.html#arch.univariate.EWMAVariance">[docs]</a>
+<a class="viewcode-back" href="../../../univariate/generated/arch.univariate.EWMAVariance.html#arch.univariate.volatility.EWMAVariance">[docs]</a>
 <span class="k">class</span> <span class="nc">EWMAVariance</span><span class="p">(</span><span class="n">VolatilityProcess</span><span class="p">,</span> <span class="n">metaclass</span><span class="o">=</span><span class="n">AbstractDocStringInheritor</span><span class="p">):</span>
 <span class="w">    </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Exponentially Weighted Moving-Average (RiskMetrics) Variance process</span>
@@ -2577,7 +2577,7 @@ <h1>Source code for arch.univariate.volatility</h1><div class="highlight"><pre>
         <span class="k">return</span> <span class="n">descr</span>
 
 <div class="viewcode-block" id="EWMAVariance.starting_values">
-<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.EWMAVariance.starting_values.html#arch.univariate.EWMAVariance.starting_values">[docs]</a>
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.EWMAVariance.starting_values.html#arch.univariate.volatility.EWMAVariance.starting_values">[docs]</a>
     <span class="k">def</span> <span class="nf">starting_values</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">resids</span><span class="p">:</span> <span class="n">Float64Array</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Float64Array</span><span class="p">:</span>
         <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_estimate_lam</span><span class="p">:</span>
             <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mf">0.94</span><span class="p">])</span>
@@ -2585,7 +2585,7 @@ <h1>Source code for arch.univariate.volatility</h1><div class="highlight"><pre>
 
 
 <div class="viewcode-block" id="EWMAVariance.parameter_names">
-<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.EWMAVariance.parameter_names.html#arch.univariate.EWMAVariance.parameter_names">[docs]</a>
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.EWMAVariance.parameter_names.html#arch.univariate.volatility.EWMAVariance.parameter_names">[docs]</a>
     <span class="k">def</span> <span class="nf">parameter_names</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">list</span><span class="p">[</span><span class="nb">str</span><span class="p">]:</span>
         <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_estimate_lam</span><span class="p">:</span>
             <span class="k">return</span> <span class="p">[</span><span class="s2">&quot;lam&quot;</span><span class="p">]</span>
@@ -2593,7 +2593,7 @@ <h1>Source code for arch.univariate.volatility</h1><div class="highlight"><pre>
 
 
 <div class="viewcode-block" id="EWMAVariance.bounds">
-<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.EWMAVariance.bounds.html#arch.univariate.EWMAVariance.bounds">[docs]</a>
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.EWMAVariance.bounds.html#arch.univariate.volatility.EWMAVariance.bounds">[docs]</a>
     <span class="k">def</span> <span class="nf">bounds</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">resids</span><span class="p">:</span> <span class="n">Float64Array</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">list</span><span class="p">[</span><span class="nb">tuple</span><span class="p">[</span><span class="nb">float</span><span class="p">,</span> <span class="nb">float</span><span class="p">]]:</span>
         <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_estimate_lam</span><span class="p">:</span>
             <span class="k">return</span> <span class="p">[(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">)]</span>
@@ -2601,7 +2601,7 @@ <h1>Source code for arch.univariate.volatility</h1><div class="highlight"><pre>
 
 
 <div class="viewcode-block" id="EWMAVariance.compute_variance">
-<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.EWMAVariance.compute_variance.html#arch.univariate.EWMAVariance.compute_variance">[docs]</a>
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.EWMAVariance.compute_variance.html#arch.univariate.volatility.EWMAVariance.compute_variance">[docs]</a>
     <span class="k">def</span> <span class="nf">compute_variance</span><span class="p">(</span>
         <span class="bp">self</span><span class="p">,</span>
         <span class="n">parameters</span><span class="p">:</span> <span class="n">Float64Array</span><span class="p">,</span>
@@ -2615,7 +2615,7 @@ <h1>Source code for arch.univariate.volatility</h1><div class="highlight"><pre>
 
 
 <div class="viewcode-block" id="EWMAVariance.constraints">
-<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.EWMAVariance.constraints.html#arch.univariate.EWMAVariance.constraints">[docs]</a>
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.EWMAVariance.constraints.html#arch.univariate.volatility.EWMAVariance.constraints">[docs]</a>
     <span class="k">def</span> <span class="nf">constraints</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">tuple</span><span class="p">[</span><span class="n">Float64Array</span><span class="p">,</span> <span class="n">Float64Array</span><span class="p">]:</span>
         <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_estimate_lam</span><span class="p">:</span>
             <span class="n">a</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span>
@@ -2625,7 +2625,7 @@ <h1>Source code for arch.univariate.volatility</h1><div class="highlight"><pre>
 
 
 <div class="viewcode-block" id="EWMAVariance.simulate">
-<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.EWMAVariance.simulate.html#arch.univariate.EWMAVariance.simulate">[docs]</a>
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.EWMAVariance.simulate.html#arch.univariate.volatility.EWMAVariance.simulate">[docs]</a>
     <span class="k">def</span> <span class="nf">simulate</span><span class="p">(</span>
         <span class="bp">self</span><span class="p">,</span>
         <span class="n">parameters</span><span class="p">:</span> <span class="n">Sequence</span><span class="p">[</span><span class="nb">int</span> <span class="o">|</span> <span class="nb">float</span><span class="p">]</span> <span class="o">|</span> <span class="n">ArrayLike1D</span><span class="p">,</span>
@@ -2720,7 +2720,7 @@ <h1>Source code for arch.univariate.volatility</h1><div class="highlight"><pre>
 
 
 <div class="viewcode-block" id="RiskMetrics2006">
-<a class="viewcode-back" href="../../../univariate/generated/arch.univariate.RiskMetrics2006.html#arch.univariate.RiskMetrics2006">[docs]</a>
+<a class="viewcode-back" href="../../../univariate/generated/arch.univariate.RiskMetrics2006.html#arch.univariate.volatility.RiskMetrics2006">[docs]</a>
 <span class="k">class</span> <span class="nc">RiskMetrics2006</span><span class="p">(</span><span class="n">VolatilityProcess</span><span class="p">,</span> <span class="n">metaclass</span><span class="o">=</span><span class="n">AbstractDocStringInheritor</span><span class="p">):</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    RiskMetrics 2006 Variance process</span>
@@ -2811,7 +2811,7 @@ <h1>Source code for arch.univariate.volatility</h1><div class="highlight"><pre>
         <span class="k">return</span> <span class="n">mus</span>
 
 <div class="viewcode-block" id="RiskMetrics2006.backcast">
-<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.RiskMetrics2006.backcast.html#arch.univariate.RiskMetrics2006.backcast">[docs]</a>
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.RiskMetrics2006.backcast.html#arch.univariate.volatility.RiskMetrics2006.backcast">[docs]</a>
     <span class="k">def</span> <span class="nf">backcast</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">resids</span><span class="p">:</span> <span class="n">Float64Array</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">float</span> <span class="o">|</span> <span class="n">Float64Array</span><span class="p">:</span>
 <span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">        Construct values for backcasting to start the recursion</span>
@@ -2843,7 +2843,7 @@ <h1>Source code for arch.univariate.volatility</h1><div class="highlight"><pre>
 
 
 <div class="viewcode-block" id="RiskMetrics2006.backcast_transform">
-<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.RiskMetrics2006.backcast_transform.html#arch.univariate.RiskMetrics2006.backcast_transform">[docs]</a>
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.RiskMetrics2006.backcast_transform.html#arch.univariate.volatility.RiskMetrics2006.backcast_transform">[docs]</a>
     <span class="k">def</span> <span class="nf">backcast_transform</span><span class="p">(</span>
         <span class="bp">self</span><span class="p">,</span> <span class="n">backcast</span><span class="p">:</span> <span class="nb">float</span> <span class="o">|</span> <span class="n">Float64Array</span>
     <span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">float</span> <span class="o">|</span> <span class="n">Float64Array</span><span class="p">:</span>
@@ -2862,19 +2862,19 @@ <h1>Source code for arch.univariate.volatility</h1><div class="highlight"><pre>
 
 
 <div class="viewcode-block" id="RiskMetrics2006.starting_values">
-<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.RiskMetrics2006.starting_values.html#arch.univariate.RiskMetrics2006.starting_values">[docs]</a>
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.RiskMetrics2006.starting_values.html#arch.univariate.volatility.RiskMetrics2006.starting_values">[docs]</a>
     <span class="k">def</span> <span class="nf">starting_values</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">resids</span><span class="p">:</span> <span class="n">Float64Array</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Float64Array</span><span class="p">:</span>
         <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">empty</span><span class="p">((</span><span class="mi">0</span><span class="p">,))</span></div>
 
 
 <div class="viewcode-block" id="RiskMetrics2006.parameter_names">
-<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.RiskMetrics2006.parameter_names.html#arch.univariate.RiskMetrics2006.parameter_names">[docs]</a>
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.RiskMetrics2006.parameter_names.html#arch.univariate.volatility.RiskMetrics2006.parameter_names">[docs]</a>
     <span class="k">def</span> <span class="nf">parameter_names</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">list</span><span class="p">[</span><span class="nb">str</span><span class="p">]:</span>
         <span class="k">return</span> <span class="p">[]</span></div>
 
 
 <div class="viewcode-block" id="RiskMetrics2006.variance_bounds">
-<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.RiskMetrics2006.variance_bounds.html#arch.univariate.RiskMetrics2006.variance_bounds">[docs]</a>
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.RiskMetrics2006.variance_bounds.html#arch.univariate.volatility.RiskMetrics2006.variance_bounds">[docs]</a>
     <span class="k">def</span> <span class="nf">variance_bounds</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">resids</span><span class="p">:</span> <span class="n">Float64Array</span><span class="p">,</span> <span class="n">power</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mf">2.0</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Float64Array</span><span class="p">:</span>
         <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="n">resids</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="mi">1</span><span class="p">))</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span>
             <span class="p">[</span><span class="o">-</span><span class="mf">1.0</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">finfo</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">float64</span><span class="p">)</span><span class="o">.</span><span class="n">max</span><span class="p">]</span>
@@ -2882,19 +2882,19 @@ <h1>Source code for arch.univariate.volatility</h1><div class="highlight"><pre>
 
 
 <div class="viewcode-block" id="RiskMetrics2006.bounds">
-<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.RiskMetrics2006.bounds.html#arch.univariate.RiskMetrics2006.bounds">[docs]</a>
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.RiskMetrics2006.bounds.html#arch.univariate.volatility.RiskMetrics2006.bounds">[docs]</a>
     <span class="k">def</span> <span class="nf">bounds</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">resids</span><span class="p">:</span> <span class="n">Float64Array</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">list</span><span class="p">[</span><span class="nb">tuple</span><span class="p">[</span><span class="nb">float</span><span class="p">,</span> <span class="nb">float</span><span class="p">]]:</span>
         <span class="k">return</span> <span class="p">[]</span></div>
 
 
 <div class="viewcode-block" id="RiskMetrics2006.constraints">
-<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.RiskMetrics2006.constraints.html#arch.univariate.RiskMetrics2006.constraints">[docs]</a>
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.RiskMetrics2006.constraints.html#arch.univariate.volatility.RiskMetrics2006.constraints">[docs]</a>
     <span class="k">def</span> <span class="nf">constraints</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">tuple</span><span class="p">[</span><span class="n">Float64Array</span><span class="p">,</span> <span class="n">Float64Array</span><span class="p">]:</span>
         <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">empty</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">)),</span> <span class="n">np</span><span class="o">.</span><span class="n">empty</span><span class="p">((</span><span class="mi">0</span><span class="p">,))</span></div>
 
 
 <div class="viewcode-block" id="RiskMetrics2006.compute_variance">
-<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.RiskMetrics2006.compute_variance.html#arch.univariate.RiskMetrics2006.compute_variance">[docs]</a>
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.RiskMetrics2006.compute_variance.html#arch.univariate.volatility.RiskMetrics2006.compute_variance">[docs]</a>
     <span class="k">def</span> <span class="nf">compute_variance</span><span class="p">(</span>
         <span class="bp">self</span><span class="p">,</span>
         <span class="n">parameters</span><span class="p">:</span> <span class="n">Float64Array</span><span class="p">,</span>
@@ -2922,7 +2922,7 @@ <h1>Source code for arch.univariate.volatility</h1><div class="highlight"><pre>
 
 
 <div class="viewcode-block" id="RiskMetrics2006.simulate">
-<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.RiskMetrics2006.simulate.html#arch.univariate.RiskMetrics2006.simulate">[docs]</a>
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.RiskMetrics2006.simulate.html#arch.univariate.volatility.RiskMetrics2006.simulate">[docs]</a>
     <span class="k">def</span> <span class="nf">simulate</span><span class="p">(</span>
         <span class="bp">self</span><span class="p">,</span>
         <span class="n">parameters</span><span class="p">:</span> <span class="n">Sequence</span><span class="p">[</span><span class="nb">int</span> <span class="o">|</span> <span class="nb">float</span><span class="p">]</span> <span class="o">|</span> <span class="n">ArrayLike1D</span><span class="p">,</span>
@@ -3028,7 +3028,7 @@ <h1>Source code for arch.univariate.volatility</h1><div class="highlight"><pre>
 
 
 <div class="viewcode-block" id="EGARCH">
-<a class="viewcode-back" href="../../../univariate/generated/arch.univariate.EGARCH.html#arch.univariate.EGARCH">[docs]</a>
+<a class="viewcode-back" href="../../../univariate/generated/arch.univariate.EGARCH.html#arch.univariate.volatility.EGARCH">[docs]</a>
 <span class="k">class</span> <span class="nc">EGARCH</span><span class="p">(</span><span class="n">VolatilityProcess</span><span class="p">,</span> <span class="n">metaclass</span><span class="o">=</span><span class="n">AbstractDocStringInheritor</span><span class="p">):</span>
 <span class="w">    </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    EGARCH model estimation</span>
@@ -3097,13 +3097,13 @@ <h1>Source code for arch.univariate.volatility</h1><div class="highlight"><pre>
         <span class="k">return</span> <span class="n">descr</span>
 
 <div class="viewcode-block" id="EGARCH.variance_bounds">
-<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.EGARCH.variance_bounds.html#arch.univariate.EGARCH.variance_bounds">[docs]</a>
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.EGARCH.variance_bounds.html#arch.univariate.volatility.EGARCH.variance_bounds">[docs]</a>
     <span class="k">def</span> <span class="nf">variance_bounds</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">resids</span><span class="p">:</span> <span class="n">Float64Array</span><span class="p">,</span> <span class="n">power</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mf">2.0</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Float64Array</span><span class="p">:</span>
         <span class="k">return</span> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">variance_bounds</span><span class="p">(</span><span class="n">resids</span><span class="p">,</span> <span class="mf">2.0</span><span class="p">)</span></div>
 
 
 <div class="viewcode-block" id="EGARCH.bounds">
-<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.EGARCH.bounds.html#arch.univariate.EGARCH.bounds">[docs]</a>
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.EGARCH.bounds.html#arch.univariate.volatility.EGARCH.bounds">[docs]</a>
     <span class="k">def</span> <span class="nf">bounds</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">resids</span><span class="p">:</span> <span class="n">Float64Array</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">list</span><span class="p">[</span><span class="nb">tuple</span><span class="p">[</span><span class="nb">float</span><span class="p">,</span> <span class="nb">float</span><span class="p">]]:</span>
         <span class="n">v</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">resids</span><span class="o">**</span><span class="mf">2.0</span><span class="p">)</span>
         <span class="n">log_const</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="mf">10000.0</span><span class="p">)</span>
@@ -3116,7 +3116,7 @@ <h1>Source code for arch.univariate.volatility</h1><div class="highlight"><pre>
 
 
 <div class="viewcode-block" id="EGARCH.constraints">
-<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.EGARCH.constraints.html#arch.univariate.EGARCH.constraints">[docs]</a>
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.EGARCH.constraints.html#arch.univariate.volatility.EGARCH.constraints">[docs]</a>
     <span class="k">def</span> <span class="nf">constraints</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">tuple</span><span class="p">[</span><span class="n">Float64Array</span><span class="p">,</span> <span class="n">Float64Array</span><span class="p">]:</span>
         <span class="n">p</span><span class="p">,</span> <span class="n">o</span><span class="p">,</span> <span class="n">q</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">p</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">o</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">q</span>
         <span class="n">k_arch</span> <span class="o">=</span> <span class="n">p</span> <span class="o">+</span> <span class="n">o</span> <span class="o">+</span> <span class="n">q</span>
@@ -3128,7 +3128,7 @@ <h1>Source code for arch.univariate.volatility</h1><div class="highlight"><pre>
 
 
 <div class="viewcode-block" id="EGARCH.compute_variance">
-<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.EGARCH.compute_variance.html#arch.univariate.EGARCH.compute_variance">[docs]</a>
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.EGARCH.compute_variance.html#arch.univariate.volatility.EGARCH.compute_variance">[docs]</a>
     <span class="k">def</span> <span class="nf">compute_variance</span><span class="p">(</span>
         <span class="bp">self</span><span class="p">,</span>
         <span class="n">parameters</span><span class="p">:</span> <span class="n">Float64Array</span><span class="p">,</span>
@@ -3166,7 +3166,7 @@ <h1>Source code for arch.univariate.volatility</h1><div class="highlight"><pre>
 
 
 <div class="viewcode-block" id="EGARCH.backcast_transform">
-<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.EGARCH.backcast_transform.html#arch.univariate.EGARCH.backcast_transform">[docs]</a>
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.EGARCH.backcast_transform.html#arch.univariate.volatility.EGARCH.backcast_transform">[docs]</a>
     <span class="k">def</span> <span class="nf">backcast_transform</span><span class="p">(</span>
         <span class="bp">self</span><span class="p">,</span> <span class="n">backcast</span><span class="p">:</span> <span class="nb">float</span> <span class="o">|</span> <span class="n">Float64Array</span>
     <span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">float</span> <span class="o">|</span> <span class="n">Float64Array</span><span class="p">:</span>
@@ -3175,13 +3175,13 @@ <h1>Source code for arch.univariate.volatility</h1><div class="highlight"><pre>
 
 
 <div class="viewcode-block" id="EGARCH.backcast">
-<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.EGARCH.backcast.html#arch.univariate.EGARCH.backcast">[docs]</a>
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.EGARCH.backcast.html#arch.univariate.volatility.EGARCH.backcast">[docs]</a>
     <span class="k">def</span> <span class="nf">backcast</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">resids</span><span class="p">:</span> <span class="n">Float64Array</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">float</span> <span class="o">|</span> <span class="n">Float64Array</span><span class="p">:</span>
         <span class="k">return</span> <span class="nb">float</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">backcast</span><span class="p">(</span><span class="n">resids</span><span class="p">)))</span></div>
 
 
 <div class="viewcode-block" id="EGARCH.simulate">
-<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.EGARCH.simulate.html#arch.univariate.EGARCH.simulate">[docs]</a>
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.EGARCH.simulate.html#arch.univariate.volatility.EGARCH.simulate">[docs]</a>
     <span class="k">def</span> <span class="nf">simulate</span><span class="p">(</span>
         <span class="bp">self</span><span class="p">,</span>
         <span class="n">parameters</span><span class="p">:</span> <span class="n">Sequence</span><span class="p">[</span><span class="nb">int</span> <span class="o">|</span> <span class="nb">float</span><span class="p">]</span> <span class="o">|</span> <span class="n">ArrayLike1D</span><span class="p">,</span>
@@ -3238,7 +3238,7 @@ <h1>Source code for arch.univariate.volatility</h1><div class="highlight"><pre>
 
 
 <div class="viewcode-block" id="EGARCH.starting_values">
-<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.EGARCH.starting_values.html#arch.univariate.EGARCH.starting_values">[docs]</a>
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.EGARCH.starting_values.html#arch.univariate.volatility.EGARCH.starting_values">[docs]</a>
     <span class="k">def</span> <span class="nf">starting_values</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">resids</span><span class="p">:</span> <span class="n">Float64Array</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Float64Array</span><span class="p">:</span>
         <span class="n">p</span><span class="p">,</span> <span class="n">o</span><span class="p">,</span> <span class="n">q</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">p</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">o</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">q</span>
         <span class="n">alphas</span> <span class="o">=</span> <span class="p">[</span><span class="mf">0.01</span><span class="p">,</span> <span class="mf">0.05</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.2</span><span class="p">]</span>
@@ -3269,7 +3269,7 @@ <h1>Source code for arch.univariate.volatility</h1><div class="highlight"><pre>
 
 
 <div class="viewcode-block" id="EGARCH.parameter_names">
-<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.EGARCH.parameter_names.html#arch.univariate.EGARCH.parameter_names">[docs]</a>
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.EGARCH.parameter_names.html#arch.univariate.volatility.EGARCH.parameter_names">[docs]</a>
     <span class="k">def</span> <span class="nf">parameter_names</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">list</span><span class="p">[</span><span class="nb">str</span><span class="p">]:</span>
         <span class="k">return</span> <span class="n">_common_names</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">p</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">o</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">q</span><span class="p">)</span></div>
 
@@ -3366,7 +3366,7 @@ <h1>Source code for arch.univariate.volatility</h1><div class="highlight"><pre>
 
 
 <div class="viewcode-block" id="FixedVariance">
-<a class="viewcode-back" href="../../../univariate/generated/arch.univariate.FixedVariance.html#arch.univariate.FixedVariance">[docs]</a>
+<a class="viewcode-back" href="../../../univariate/generated/arch.univariate.FixedVariance.html#arch.univariate.volatility.FixedVariance">[docs]</a>
 <span class="k">class</span> <span class="nc">FixedVariance</span><span class="p">(</span><span class="n">VolatilityProcess</span><span class="p">,</span> <span class="n">metaclass</span><span class="o">=</span><span class="n">AbstractDocStringInheritor</span><span class="p">):</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Fixed volatility process</span>
@@ -3397,7 +3397,7 @@ <h1>Source code for arch.univariate.volatility</h1><div class="highlight"><pre>
         <span class="bp">self</span><span class="o">.</span><span class="n">_variance</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">variance</span><span class="p">)</span>
 
 <div class="viewcode-block" id="FixedVariance.compute_variance">
-<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.FixedVariance.compute_variance.html#arch.univariate.FixedVariance.compute_variance">[docs]</a>
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.FixedVariance.compute_variance.html#arch.univariate.volatility.FixedVariance.compute_variance">[docs]</a>
     <span class="k">def</span> <span class="nf">compute_variance</span><span class="p">(</span>
         <span class="bp">self</span><span class="p">,</span>
         <span class="n">parameters</span><span class="p">:</span> <span class="n">Float64Array</span><span class="p">,</span>
@@ -3415,7 +3415,7 @@ <h1>Source code for arch.univariate.volatility</h1><div class="highlight"><pre>
 
 
 <div class="viewcode-block" id="FixedVariance.starting_values">
-<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.FixedVariance.starting_values.html#arch.univariate.FixedVariance.starting_values">[docs]</a>
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.FixedVariance.starting_values.html#arch.univariate.volatility.FixedVariance.starting_values">[docs]</a>
     <span class="k">def</span> <span class="nf">starting_values</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">resids</span><span class="p">:</span> <span class="n">Float64Array</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Float64Array</span><span class="p">:</span>
         <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">_unit_scale</span><span class="p">:</span>
             <span class="n">_resids</span> <span class="o">=</span> <span class="n">resids</span> <span class="o">/</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_variance</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">_start</span> <span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">_stop</span><span class="p">])</span>
@@ -3424,7 +3424,7 @@ <h1>Source code for arch.univariate.volatility</h1><div class="highlight"><pre>
 
 
 <div class="viewcode-block" id="FixedVariance.simulate">
-<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.FixedVariance.simulate.html#arch.univariate.FixedVariance.simulate">[docs]</a>
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.FixedVariance.simulate.html#arch.univariate.volatility.FixedVariance.simulate">[docs]</a>
     <span class="k">def</span> <span class="nf">simulate</span><span class="p">(</span>
         <span class="bp">self</span><span class="p">,</span>
         <span class="n">parameters</span><span class="p">:</span> <span class="n">Sequence</span><span class="p">[</span><span class="nb">int</span> <span class="o">|</span> <span class="nb">float</span><span class="p">]</span> <span class="o">|</span> <span class="n">ArrayLike1D</span><span class="p">,</span>
@@ -3437,7 +3437,7 @@ <h1>Source code for arch.univariate.volatility</h1><div class="highlight"><pre>
 
 
 <div class="viewcode-block" id="FixedVariance.constraints">
-<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.FixedVariance.constraints.html#arch.univariate.FixedVariance.constraints">[docs]</a>
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.FixedVariance.constraints.html#arch.univariate.volatility.FixedVariance.constraints">[docs]</a>
     <span class="k">def</span> <span class="nf">constraints</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">tuple</span><span class="p">[</span><span class="n">Float64Array</span><span class="p">,</span> <span class="n">Float64Array</span><span class="p">]:</span>
         <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">_unit_scale</span><span class="p">:</span>
             <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)),</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
@@ -3446,13 +3446,13 @@ <h1>Source code for arch.univariate.volatility</h1><div class="highlight"><pre>
 
 
 <div class="viewcode-block" id="FixedVariance.backcast">
-<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.FixedVariance.backcast.html#arch.univariate.FixedVariance.backcast">[docs]</a>
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.FixedVariance.backcast.html#arch.univariate.volatility.FixedVariance.backcast">[docs]</a>
     <span class="k">def</span> <span class="nf">backcast</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">resids</span><span class="p">:</span> <span class="n">Float64Array</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">float</span> <span class="o">|</span> <span class="n">Float64Array</span><span class="p">:</span>
         <span class="k">return</span> <span class="mf">1.0</span></div>
 
 
 <div class="viewcode-block" id="FixedVariance.bounds">
-<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.FixedVariance.bounds.html#arch.univariate.FixedVariance.bounds">[docs]</a>
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.FixedVariance.bounds.html#arch.univariate.volatility.FixedVariance.bounds">[docs]</a>
     <span class="k">def</span> <span class="nf">bounds</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">resids</span><span class="p">:</span> <span class="n">Float64Array</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">list</span><span class="p">[</span><span class="nb">tuple</span><span class="p">[</span><span class="nb">float</span><span class="p">,</span> <span class="nb">float</span><span class="p">]]:</span>
         <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">_unit_scale</span><span class="p">:</span>
             <span class="n">v</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">squeeze</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">starting_values</span><span class="p">(</span><span class="n">resids</span><span class="p">)))</span>
@@ -3463,7 +3463,7 @@ <h1>Source code for arch.univariate.volatility</h1><div class="highlight"><pre>
 
 
 <div class="viewcode-block" id="FixedVariance.parameter_names">
-<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.FixedVariance.parameter_names.html#arch.univariate.FixedVariance.parameter_names">[docs]</a>
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.FixedVariance.parameter_names.html#arch.univariate.volatility.FixedVariance.parameter_names">[docs]</a>
     <span class="k">def</span> <span class="nf">parameter_names</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">list</span><span class="p">[</span><span class="nb">str</span><span class="p">]:</span>
         <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">_unit_scale</span><span class="p">:</span>
             <span class="k">return</span> <span class="p">[</span><span class="s2">&quot;scale&quot;</span><span class="p">]</span>
@@ -3511,7 +3511,7 @@ <h1>Source code for arch.univariate.volatility</h1><div class="highlight"><pre>
 
 
 <div class="viewcode-block" id="FIGARCH">
-<a class="viewcode-back" href="../../../univariate/generated/arch.univariate.FIGARCH.html#arch.univariate.FIGARCH">[docs]</a>
+<a class="viewcode-back" href="../../../univariate/generated/arch.univariate.FIGARCH.html#arch.univariate.volatility.FIGARCH">[docs]</a>
 <span class="k">class</span> <span class="nc">FIGARCH</span><span class="p">(</span><span class="n">VolatilityProcess</span><span class="p">,</span> <span class="n">metaclass</span><span class="o">=</span><span class="n">AbstractDocStringInheritor</span><span class="p">):</span>
 <span class="w">    </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    FIGARCH model</span>
@@ -3621,7 +3621,7 @@ <h1>Source code for arch.univariate.volatility</h1><div class="highlight"><pre>
         <span class="k">return</span> <span class="n">descr</span>
 
 <div class="viewcode-block" id="FIGARCH.variance_bounds">
-<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.FIGARCH.variance_bounds.html#arch.univariate.FIGARCH.variance_bounds">[docs]</a>
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.FIGARCH.variance_bounds.html#arch.univariate.volatility.FIGARCH.variance_bounds">[docs]</a>
     <span class="k">def</span> <span class="nf">variance_bounds</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">resids</span><span class="p">:</span> <span class="n">Float64Array</span><span class="p">,</span> <span class="n">power</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mf">2.0</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Float64Array</span><span class="p">:</span>
         <span class="k">return</span> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">variance_bounds</span><span class="p">(</span><span class="n">resids</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">power</span><span class="p">)</span></div>
 
@@ -3645,7 +3645,7 @@ <h1>Source code for arch.univariate.volatility</h1><div class="highlight"><pre>
                 <span class="k">return</span> <span class="sa">f</span><span class="s2">&quot;Power FIGARCH (power: </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">power</span><span class="si">:</span><span class="s2">0.1f</span><span class="si">}</span><span class="s2">)&quot;</span>
 
 <div class="viewcode-block" id="FIGARCH.bounds">
-<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.FIGARCH.bounds.html#arch.univariate.FIGARCH.bounds">[docs]</a>
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.FIGARCH.bounds.html#arch.univariate.volatility.FIGARCH.bounds">[docs]</a>
     <span class="k">def</span> <span class="nf">bounds</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">resids</span><span class="p">:</span> <span class="n">Float64Array</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">list</span><span class="p">[</span><span class="nb">tuple</span><span class="p">[</span><span class="nb">float</span><span class="p">,</span> <span class="nb">float</span><span class="p">]]:</span>
         <span class="n">eps_half</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">finfo</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">float64</span><span class="p">)</span><span class="o">.</span><span class="n">eps</span><span class="p">)</span>
         <span class="n">v</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="nb">abs</span><span class="p">(</span><span class="n">resids</span><span class="p">)</span> <span class="o">**</span> <span class="bp">self</span><span class="o">.</span><span class="n">power</span><span class="p">)</span>
@@ -3659,7 +3659,7 @@ <h1>Source code for arch.univariate.volatility</h1><div class="highlight"><pre>
 
 
 <div class="viewcode-block" id="FIGARCH.constraints">
-<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.FIGARCH.constraints.html#arch.univariate.FIGARCH.constraints">[docs]</a>
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.FIGARCH.constraints.html#arch.univariate.volatility.FIGARCH.constraints">[docs]</a>
     <span class="k">def</span> <span class="nf">constraints</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">tuple</span><span class="p">[</span><span class="n">Float64Array</span><span class="p">,</span> <span class="n">Float64Array</span><span class="p">]:</span>
         <span class="c1"># omega &gt; 0 &lt;- 1</span>
         <span class="c1"># 0 &lt;= d &lt;= 1 &lt;- 2</span>
@@ -3690,7 +3690,7 @@ <h1>Source code for arch.univariate.volatility</h1><div class="highlight"><pre>
 
 
 <div class="viewcode-block" id="FIGARCH.compute_variance">
-<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.FIGARCH.compute_variance.html#arch.univariate.FIGARCH.compute_variance">[docs]</a>
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.FIGARCH.compute_variance.html#arch.univariate.volatility.FIGARCH.compute_variance">[docs]</a>
     <span class="k">def</span> <span class="nf">compute_variance</span><span class="p">(</span>
         <span class="bp">self</span><span class="p">,</span>
         <span class="n">parameters</span><span class="p">:</span> <span class="n">Float64Array</span><span class="p">,</span>
@@ -3716,7 +3716,7 @@ <h1>Source code for arch.univariate.volatility</h1><div class="highlight"><pre>
 
 
 <div class="viewcode-block" id="FIGARCH.backcast_transform">
-<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.FIGARCH.backcast_transform.html#arch.univariate.FIGARCH.backcast_transform">[docs]</a>
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.FIGARCH.backcast_transform.html#arch.univariate.volatility.FIGARCH.backcast_transform">[docs]</a>
     <span class="k">def</span> <span class="nf">backcast_transform</span><span class="p">(</span>
         <span class="bp">self</span><span class="p">,</span> <span class="n">backcast</span><span class="p">:</span> <span class="nb">float</span> <span class="o">|</span> <span class="n">Float64Array</span>
     <span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">float</span> <span class="o">|</span> <span class="n">Float64Array</span><span class="p">:</span>
@@ -3725,7 +3725,7 @@ <h1>Source code for arch.univariate.volatility</h1><div class="highlight"><pre>
 
 
 <div class="viewcode-block" id="FIGARCH.backcast">
-<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.FIGARCH.backcast.html#arch.univariate.FIGARCH.backcast">[docs]</a>
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.FIGARCH.backcast.html#arch.univariate.volatility.FIGARCH.backcast">[docs]</a>
     <span class="k">def</span> <span class="nf">backcast</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">resids</span><span class="p">:</span> <span class="n">Float64Array</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">float</span> <span class="o">|</span> <span class="n">Float64Array</span><span class="p">:</span>
         <span class="n">power</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">power</span>
         <span class="n">tau</span> <span class="o">=</span> <span class="nb">min</span><span class="p">(</span><span class="mi">75</span><span class="p">,</span> <span class="n">resids</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
@@ -3737,7 +3737,7 @@ <h1>Source code for arch.univariate.volatility</h1><div class="highlight"><pre>
 
 
 <div class="viewcode-block" id="FIGARCH.simulate">
-<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.FIGARCH.simulate.html#arch.univariate.FIGARCH.simulate">[docs]</a>
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.FIGARCH.simulate.html#arch.univariate.volatility.FIGARCH.simulate">[docs]</a>
     <span class="k">def</span> <span class="nf">simulate</span><span class="p">(</span>
         <span class="bp">self</span><span class="p">,</span>
         <span class="n">parameters</span><span class="p">:</span> <span class="n">Sequence</span><span class="p">[</span><span class="nb">int</span> <span class="o">|</span> <span class="nb">float</span><span class="p">]</span> <span class="o">|</span> <span class="n">ArrayLike1D</span><span class="p">,</span>
@@ -3787,7 +3787,7 @@ <h1>Source code for arch.univariate.volatility</h1><div class="highlight"><pre>
 
 
 <div class="viewcode-block" id="FIGARCH.starting_values">
-<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.FIGARCH.starting_values.html#arch.univariate.FIGARCH.starting_values">[docs]</a>
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.FIGARCH.starting_values.html#arch.univariate.volatility.FIGARCH.starting_values">[docs]</a>
     <span class="k">def</span> <span class="nf">starting_values</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">resids</span><span class="p">:</span> <span class="n">Float64Array</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Float64Array</span><span class="p">:</span>
         <span class="n">truncation</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">truncation</span>
         <span class="n">ds</span> <span class="o">=</span> <span class="p">[</span><span class="mf">0.2</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">,</span> <span class="mf">0.7</span><span class="p">]</span>
@@ -3827,7 +3827,7 @@ <h1>Source code for arch.univariate.volatility</h1><div class="highlight"><pre>
 
 
 <div class="viewcode-block" id="FIGARCH.parameter_names">
-<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.FIGARCH.parameter_names.html#arch.univariate.FIGARCH.parameter_names">[docs]</a>
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.FIGARCH.parameter_names.html#arch.univariate.volatility.FIGARCH.parameter_names">[docs]</a>
     <span class="k">def</span> <span class="nf">parameter_names</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">list</span><span class="p">[</span><span class="nb">str</span><span class="p">]:</span>
         <span class="n">names</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;omega&quot;</span><span class="p">]</span>
         <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">p</span><span class="p">:</span>
@@ -3950,7 +3950,7 @@ <h1>Source code for arch.univariate.volatility</h1><div class="highlight"><pre>
 
 
 <div class="viewcode-block" id="APARCH">
-<a class="viewcode-back" href="../../../univariate/generated/arch.univariate.APARCH.html#arch.univariate.APARCH">[docs]</a>
+<a class="viewcode-back" href="../../../univariate/generated/arch.univariate.APARCH.html#arch.univariate.volatility.APARCH">[docs]</a>
 <span class="k">class</span> <span class="nc">APARCH</span><span class="p">(</span><span class="n">VolatilityProcess</span><span class="p">,</span> <span class="n">metaclass</span><span class="o">=</span><span class="n">AbstractDocStringInheritor</span><span class="p">):</span>
 <span class="w">    </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">    Asymmetric Power ARCH (APARCH) volatility process</span>
@@ -4066,7 +4066,7 @@ <h1>Source code for arch.univariate.volatility</h1><div class="highlight"><pre>
         <span class="k">return</span> <span class="n">_parameters</span>
 
 <div class="viewcode-block" id="APARCH.compute_variance">
-<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.APARCH.compute_variance.html#arch.univariate.APARCH.compute_variance">[docs]</a>
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.APARCH.compute_variance.html#arch.univariate.volatility.APARCH.compute_variance">[docs]</a>
     <span class="k">def</span> <span class="nf">compute_variance</span><span class="p">(</span>
         <span class="bp">self</span><span class="p">,</span>
         <span class="n">parameters</span><span class="p">:</span> <span class="n">Float64Array</span><span class="p">,</span>
@@ -4100,7 +4100,7 @@ <h1>Source code for arch.univariate.volatility</h1><div class="highlight"><pre>
 
 
 <div class="viewcode-block" id="APARCH.bounds">
-<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.APARCH.bounds.html#arch.univariate.APARCH.bounds">[docs]</a>
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.APARCH.bounds.html#arch.univariate.volatility.APARCH.bounds">[docs]</a>
     <span class="k">def</span> <span class="nf">bounds</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">resids</span><span class="p">:</span> <span class="n">Float64Array</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">list</span><span class="p">[</span><span class="nb">tuple</span><span class="p">[</span><span class="nb">float</span><span class="p">,</span> <span class="nb">float</span><span class="p">]]:</span>
         <span class="n">v</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="nb">float</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="nb">abs</span><span class="p">(</span><span class="n">resids</span><span class="p">)</span> <span class="o">**</span> <span class="mf">0.5</span><span class="p">)),</span> <span class="nb">float</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">resids</span><span class="o">**</span><span class="mi">2</span><span class="p">)))</span>
 
@@ -4116,7 +4116,7 @@ <h1>Source code for arch.univariate.volatility</h1><div class="highlight"><pre>
 
 
 <div class="viewcode-block" id="APARCH.starting_values">
-<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.APARCH.starting_values.html#arch.univariate.APARCH.starting_values">[docs]</a>
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.APARCH.starting_values.html#arch.univariate.volatility.APARCH.starting_values">[docs]</a>
     <span class="k">def</span> <span class="nf">starting_values</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">resids</span><span class="p">:</span> <span class="n">Float64Array</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Float64Array</span><span class="p">:</span>
         <span class="n">p</span><span class="p">,</span> <span class="n">o</span><span class="p">,</span> <span class="n">q</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">p</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">o</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">q</span>
         <span class="n">alphas</span> <span class="o">=</span> <span class="p">[</span><span class="mf">0.03</span><span class="p">,</span> <span class="mf">0.05</span><span class="p">,</span> <span class="mf">0.08</span><span class="p">,</span> <span class="mf">0.15</span><span class="p">]</span>
@@ -4156,7 +4156,7 @@ <h1>Source code for arch.univariate.volatility</h1><div class="highlight"><pre>
 
 
 <div class="viewcode-block" id="APARCH.constraints">
-<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.APARCH.constraints.html#arch.univariate.APARCH.constraints">[docs]</a>
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.APARCH.constraints.html#arch.univariate.volatility.APARCH.constraints">[docs]</a>
     <span class="k">def</span> <span class="nf">constraints</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">tuple</span><span class="p">[</span><span class="n">Float64Array</span><span class="p">,</span> <span class="n">Float64Array</span><span class="p">]:</span>
         <span class="n">p</span><span class="p">,</span> <span class="n">o</span><span class="p">,</span> <span class="n">q</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">p</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">o</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">q</span>
         <span class="n">o</span> <span class="o">=</span> <span class="mi">1</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_common_asym</span> <span class="k">else</span> <span class="n">o</span>
@@ -4192,7 +4192,7 @@ <h1>Source code for arch.univariate.volatility</h1><div class="highlight"><pre>
 
 
 <div class="viewcode-block" id="APARCH.parameter_names">
-<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.APARCH.parameter_names.html#arch.univariate.APARCH.parameter_names">[docs]</a>
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.APARCH.parameter_names.html#arch.univariate.volatility.APARCH.parameter_names">[docs]</a>
     <span class="k">def</span> <span class="nf">parameter_names</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">list</span><span class="p">[</span><span class="nb">str</span><span class="p">]:</span>
         <span class="n">names</span> <span class="o">=</span> <span class="n">_common_names</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">p</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">o</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">q</span><span class="p">)</span>
         <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_common_asym</span><span class="p">:</span>
@@ -4203,7 +4203,7 @@ <h1>Source code for arch.univariate.volatility</h1><div class="highlight"><pre>
 
 
 <div class="viewcode-block" id="APARCH.simulate">
-<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.APARCH.simulate.html#arch.univariate.APARCH.simulate">[docs]</a>
+<a class="viewcode-back" href="../../../univariate/generated/generated/arch.univariate.APARCH.simulate.html#arch.univariate.volatility.APARCH.simulate">[docs]</a>
     <span class="k">def</span> <span class="nf">simulate</span><span class="p">(</span>
         <span class="bp">self</span><span class="p">,</span>
         <span class="n">parameters</span><span class="p">:</span> <span class="n">Sequence</span><span class="p">[</span><span class="nb">int</span> <span class="o">|</span> <span class="nb">float</span><span class="p">]</span> <span class="o">|</span> <span class="n">ArrayLike1D</span><span class="p">,</span>
@@ -4466,7 +4466,7 @@ <h1>Source code for arch.univariate.volatility</h1><div class="highlight"><pre>
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/devel/_modules/arch/utility/testing.html b/devel/_modules/arch/utility/testing.html
index d561d972c5..d119717ddb 100644
--- a/devel/_modules/arch/utility/testing.html
+++ b/devel/_modules/arch/utility/testing.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.utility.testing - arch v6.2.1 (+21)</title>
+        <title>arch.utility.testing - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
@@ -87,7 +87,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -97,7 +97,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -184,10 +184,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -531,7 +531,7 @@ <h1>Source code for arch.utility.testing</h1><div class="highlight"><pre>
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/devel/_modules/functools.html b/devel/_modules/functools.html
index 85f8e1bc1f..5dc3c393a4 100644
--- a/devel/_modules/functools.html
+++ b/devel/_modules/functools.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>functools - arch v6.2.1 (+21)</title>
+        <title>functools - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
@@ -87,7 +87,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -97,7 +97,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -184,10 +184,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -1434,7 +1434,7 @@ <h1>Source code for functools</h1><div class="highlight"><pre>
     <script id="__config" type="application/json">{"base": "..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/devel/_modules/index.html b/devel/_modules/index.html
index 3b0b0caa2d..b22aa203de 100644
--- a/devel/_modules/index.html
+++ b/devel/_modules/index.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>Overview: module code - arch v6.2.1 (+21)</title>
+        <title>Overview: module code - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
@@ -87,7 +87,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -97,7 +97,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -184,10 +184,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -369,7 +369,6 @@ <h1>All modules for which code is available</h1>
 <li><a href="arch/covariance/kernel.html">arch.covariance.kernel</a></li>
 <li><a href="arch/unitroot/_engle_granger.html">arch.unitroot._engle_granger</a></li>
 <li><a href="arch/unitroot/_phillips_ouliaris.html">arch.unitroot._phillips_ouliaris</a></li>
-<li><a href="arch/unitroot/_shared.html">arch.unitroot._shared</a></li>
 <li><a href="arch/unitroot/cointegration.html">arch.unitroot.cointegration</a></li>
 <li><a href="arch/unitroot/unitroot.html">arch.unitroot.unitroot</a></li>
 <li><a href="arch/univariate/base.html">arch.univariate.base</a></li>
@@ -378,7 +377,6 @@ <h1>All modules for which code is available</h1>
 <li><a href="arch/univariate/recursions_python.html">arch.univariate.recursions_python</a></li>
 <li><a href="arch/univariate/volatility.html">arch.univariate.volatility</a></li>
 <li><a href="arch/utility/testing.html">arch.utility.testing</a></li>
-<li><a href="functools.html">functools</a></li>
 </ul>
 
 
@@ -457,7 +455,7 @@ <h1>All modules for which code is available</h1>
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diff --git a/devel/_static/mathjax/a11y/assistive-mml.js b/devel/_static/mathjax/a11y/assistive-mml.js
new file mode 100644
index 0000000000..3657d77709
--- /dev/null
+++ b/devel/_static/mathjax/a11y/assistive-mml.js
@@ -0,0 +1 @@
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diff --git a/devel/_static/mathjax/a11y/complexity.js b/devel/_static/mathjax/a11y/complexity.js
new file mode 100644
index 0000000000..92a2fc1915
--- /dev/null
+++ b/devel/_static/mathjax/a11y/complexity.js
@@ -0,0 +1 @@
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diff --git a/devel/_static/mathjax/a11y/explorer.js b/devel/_static/mathjax/a11y/explorer.js
new file mode 100644
index 0000000000..f79ffbc1fe
--- /dev/null
+++ b/devel/_static/mathjax/a11y/explorer.js
@@ -0,0 +1 @@
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s(e,t),e.prototype.Clear=function(){this.Update(""),this.inner.style.top="",this.inner.style.backgroundColor=""},e.prototype.Update=function(t){this.inner.textContent="",this.inner.textContent=t},e.prototype.position=function(t){this.stackRegions(t)},e.prototype.highlight=function(t){var e=t.colorString();this.inner.style.backgroundColor=e.background,this.inner.style.color=e.foreground},e}(c);e.StringRegion=h;var p=function(t){function e(){return null!==t&&t.apply(this,arguments)||this}return s(e,t),e.className="MJX_ToolTip",e.style=new l.CssStyles(((n={})["."+e.className]={position:"absolute",display:"inline-block",height:"1px",width:"1px"},n["."+e.className+"_Show"]={width:"auto",height:"auto",opacity:1,"text-align":"center","border-radius":"6px",padding:"0px 0px","border-bottom":"1px dotted black",position:"absolute","z-index":202},n)),e}(h);e.ToolTip=p;var f=function(t){function e(e){var o=t.call(this,e)||this;return o.document=e,o.div.setAttribute("aria-live","assertive"),o}return s(e,t),e.className="MJX_LiveRegion",e.style=new l.CssStyles(((i={})["."+e.className]={position:"absolute",top:"0",height:"1px",width:"1px",padding:"1px",overflow:"hidden"},i["."+e.className+"_Show"]={top:"0",position:"absolute",width:"auto",height:"auto",padding:"0px 0px",opacity:1,"z-index":"202",left:0,right:0,margin:"0 auto","background-color":"rgba(0, 0, 255, 0.2)","box-shadow":"0px 10px 20px #888",border:"2px solid #CCCCCC"},i)),e}(h);e.LiveRegion=f;var d=function(t){function e(e){var o=t.call(this,e)||this;return o.document=e,o.inner.style.lineHeight="0",o}return s(e,t),e.prototype.position=function(t){var e,o=t.getBoundingClientRect(),r=this.div.getBoundingClientRect(),n=o.left+o.width/2-r.width/2;switch(n=n<0?0:n,n+=window.pageXOffset,this.document.options.a11y.align){case"top":e=o.top-r.height-10;break;case"bottom":e=o.bottom+10;break;default:e=o.top+o.height/2-r.height/2}e=(e+=window.pageYOffset)<0?0:e,this.div.style.top=e+"px",this.div.style.left=n+"px"},e.prototype.highlight=function(t){if(!this.inner.firstChild||this.inner.firstChild.hasAttribute("sre-highlight")){var e=t.colorString();this.inner.style.backgroundColor=e.background,this.inner.style.color=e.foreground}},e.prototype.Show=function(e,o){this.div.style.fontSize=this.document.options.a11y.magnify,this.Update(e),t.prototype.Show.call(this,e,o)},e.prototype.Clear=function(){this.inner.textContent="",this.inner.style.top="",this.inner.style.backgroundColor=""},e.prototype.Update=function(t){this.Clear();var e=this.cloneNode(t);this.inner.appendChild(e)},e.prototype.cloneNode=function(t){var e=t.cloneNode(!0);if("MJX-CONTAINER"!==e.nodeName){"g"!==e.nodeName&&(e.style.marginLeft=e.style.marginRight="0");for(var o=t;o&&"MJX-CONTAINER"!==o.nodeName;)o=o.parentNode;if("MJX-MATH"!==e.nodeName&&"svg"!==e.nodeName)if("svg"===(e=o.firstChild.cloneNode(!1).appendChild(e).parentNode).nodeName){e.firstChild.setAttribute("transform","matrix(1 0 0 -1 0 0)");var r=parseFloat(e.getAttribute("viewBox").split(/ /)[2]),n=parseFloat(e.getAttribute("width")),i=t.getBBox(),a=i.x,s=i.y,l=i.width,c=i.height;e.setAttribute("viewBox",[a,-(s+c),l,c].join(" ")),e.removeAttribute("style"),e.setAttribute("width",n/r*l+"ex"),e.setAttribute("height",n/r*c+"ex"),o.setAttribute("sre-highlight","false")}(e=o.cloneNode(!1).appendChild(e).parentNode).style.margin="0"}return e},e.className="MJX_HoverRegion",e.style=new l.CssStyles(((a={})["."+e.className]={position:"absolute",height:"1px",width:"1px",padding:"1px",overflow:"hidden"},a["."+e.className+"_Show"]={position:"absolute",width:"max-content",height:"auto",padding:"0px 0px",opacity:1,"z-index":"202",margin:"0 auto","background-color":"rgba(0, 0, 255, 0.2)","box-shadow":"0px 10px 20px #888",border:"2px solid #CCCCCC"},a)),e}(c);e.HoverRegion=d},854:function(t,e,o){var r,n=this&&this.__extends||(r=function(t,e){return r=Object.setPrototypeOf||{__proto__:[]}instanceof Array&&function(t,e){t.__proto__=e}||function(t,e){for(var o in e)Object.prototype.hasOwnProperty.call(e,o)&&(t[o]=e[o])},r(t,e)},function(t,e){if("function"!=typeof e&&null!==e)throw new TypeError("Class extends value "+String(e)+" is not a constructor or null");function o(){this.constructor=t}r(t,e),t.prototype=null===e?Object.create(e):(o.prototype=e.prototype,new o)}),i=this&&this.__importDefault||function(t){return 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\ No newline at end of file
diff --git a/devel/_static/mathjax/a11y/semantic-enrich.js b/devel/_static/mathjax/a11y/semantic-enrich.js
new file mode 100644
index 0000000000..99095e91df
--- /dev/null
+++ b/devel/_static/mathjax/a11y/semantic-enrich.js
@@ -0,0 +1 @@
+!function(){"use strict";var t={433:function(t,e,r){var n,a=this&&this.__extends||(n=function(t,e){return n=Object.setPrototypeOf||{__proto__:[]}instanceof Array&&function(t,e){t.__proto__=e}||function(t,e){for(var r in e)Object.prototype.hasOwnProperty.call(e,r)&&(t[r]=e[r])},n(t,e)},function(t,e){if("function"!=typeof e&&null!==e)throw new TypeError("Class extends value "+String(e)+" is not a constructor or null");function r(){this.constructor=t}n(t,e),t.prototype=null===e?Object.create(e):(r.prototype=e.prototype,new r)}),i=this&&this.__assign||function(){return i=Object.assign||function(t){for(var e,r=1,n=arguments.length;r<n;r++)for(var a in e=arguments[r])Object.prototype.hasOwnProperty.call(e,a)&&(t[a]=e[a]);return t},i.apply(this,arguments)},o=this&&this.__values||function(t){var e="function"==typeof Symbol&&Symbol.iterator,r=e&&t[e],n=0;if(r)return r.call(t);if(t&&"number"==typeof t.length)return{next:function(){return t&&n>=t.length&&(t=void 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null!==t&&t.apply(this,arguments)||this}return a(n,t),n.prototype.serializeMml=function(t){if("outerHTML"in t)return t.outerHTML;if("undefined"!=typeof Element&&"undefined"!=typeof window&&t instanceof Element){var e=window.document.createElement("div");return e.appendChild(t),e.innerHTML}return t.toString()},n.prototype.enrich=function(t,n){if(void 0===n&&(n=!1),!(this.state()>=u.STATE.ENRICHED)){if(!this.isEscaped&&(t.options.enableEnrichment||n)){t.options.sre.speech!==y&&(y=t.options.sre.speech,h.mathjax.retryAfter(d.default.setupEngine(t.options.sre).then((function(){return d.default.sreReady()}))));var a=new t.options.MathItem("",e);try{var i=this.inputData.originalMml=r(this.root);a.math=this.serializeMml(d.default.toEnriched(i)),a.display=this.display,a.compile(t),this.root=a.root,this.inputData.enrichedMml=a.math}catch(e){t.options.enrichError(t,this,e)}}this.state(u.STATE.ENRICHED)}},n.prototype.attachSpeech=function(t){var e,r;if(!(this.state()>=u.STATE.ATTACHSPEECH)){var n=this.root.attributes.get("aria-label")||this.getSpeech(this.root);if(n){var a=t.adaptor,i=this.typesetRoot;a.setAttribute(i,"aria-label",n);try{for(var s=o(a.childNodes(i)),c=s.next();!c.done;c=s.next()){var l=c.value;a.setAttribute(l,"aria-hidden","true")}}catch(t){e={error:t}}finally{try{c&&!c.done&&(r=s.return)&&r.call(s)}finally{if(e)throw e.error}}}this.state(u.STATE.ATTACHSPEECH)}},n.prototype.getSpeech=function(t){var e,r,n=t.attributes;if(!n)return"";var a=n.getExplicit("data-semantic-speech");if(!n.getExplicit("data-semantic-parent")&&a)return a;try{for(var i=o(t.childNodes),s=i.next();!s.done;s=i.next()){var c=s.value,l=this.getSpeech(c);if(null!=l)return l}}catch(t){e={error:t}}finally{try{s&&!s.done&&(r=i.return)&&r.call(i)}finally{if(e)throw e.error}}return""},n}(t)}function m(t,e){var r;return r=function(t){function r(){for(var r=[],n=0;n<arguments.length;n++)r[n]=arguments[n];var a=t.apply(this,c([],s(r),!1))||this;e.setMmlFactory(a.mmlFactory);var i=a.constructor.ProcessBits;i.has("enriched")||(i.allocate("enriched"),i.allocate("attach-speech"));var o=new p.SerializedMmlVisitor(a.mmlFactory),l=function(t){return o.visitTree(t)};return a.options.MathItem=M(a.options.MathItem,e,l),a}return a(r,t),r.prototype.attachSpeech=function(){var t,e;if(!this.processed.isSet("attach-speech")){try{for(var r=o(this.math),n=r.next();!n.done;n=r.next()){n.value.attachSpeech(this)}}catch(e){t={error:e}}finally{try{n&&!n.done&&(e=r.return)&&e.call(r)}finally{if(t)throw t.error}}this.processed.set("attach-speech")}return this},r.prototype.enrich=function(){var t,e;if(!this.processed.isSet("enriched")){if(this.options.enableEnrichment)try{for(var r=o(this.math),n=r.next();!n.done;n=r.next()){n.value.enrich(this)}}catch(e){t={error:e}}finally{try{n&&!n.done&&(e=r.return)&&e.call(r)}finally{if(t)throw t.error}}this.processed.set("enriched")}return this},r.prototype.enrichError=function(t,e,r){console.warn("Enrichment 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r(n){var a=e[n];if(void 0!==a)return a.exports;var i=e[n]={exports:{}};return t[n].call(i.exports,i,i.exports,r),i.exports}!function(){var t=r(723),e=r(306),n=r(433);MathJax.loader&&MathJax.loader.checkVersion("a11y/semantic-enrich",e.q,"a11y"),(0,t.r8)({_:{a11y:{"semantic-enrich":n}}});var a=r(712),i=r(475);MathJax.loader&&(0,t.PV)(MathJax.config.loader,"a11y/semantic-enrich",{checkReady:function(){return a.default.sreReady()}}),MathJax.startup&&MathJax.startup.extendHandler((function(t){return(0,n.EnrichHandler)(t,new i.K)}))}()}();
\ No newline at end of file
diff --git a/devel/_static/mathjax/a11y/sre.js b/devel/_static/mathjax/a11y/sre.js
new file mode 100644
index 0000000000..612a8cd7e9
--- /dev/null
+++ b/devel/_static/mathjax/a11y/sre.js
@@ -0,0 +1 @@
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n=r.createElement("mrow");if(r.replaceNode(this.fencedMml,n),this.fencedMmlNodes.forEach((e=>n.appendChild(e))),n.insertBefore(e,this.fencedMml),n.appendChild(t),!n.parentNode){const e=r.createElement("mrow");for(;n.childNodes.length>0;)e.appendChild(n.childNodes[0]);n.appendChild(e),n=e}return n}fullFence(e){const t=this.fencedMml.parentNode;let n=e;for(;n.parentNode&&n.parentNode!==t;)n=n.parentNode;return n}cleanupParents_(){this.parentCleanup.forEach((function(e){const t=e.childNodes[1].getAttribute(u.Attribute.PARENT);e.childNodes[0].setAttribute(u.Attribute.PARENT,t)}))}}t.CaseEmbellished=d},9763:function(e,t,n){Object.defineProperty(t,"__esModule",{value:!0}),t.CaseLimit=void 0;const r=n(6671),i=n(9341),o=n(8672),s=n(8171);class a extends i.AbstractEnrichCase{constructor(e){super(e),this.mml=e.mathmlTree}static test(e){if(!e.mathmlTree||!e.childNodes.length)return!1;const t=r.tagName(e.mathmlTree),n=e.type;return("limupper"===n||"limlower"===n)&&("MSUBSUP"===t||"MUNDEROVER"===t)||"limboth"===n&&("MSUB"===t||"MUNDER"===t||"MSUP"===t||"MOVER"===t)}static walkTree_(e){e&&o.walkTree(e)}getMathml(){const e=this.semantic.childNodes;return"limboth"!==this.semantic.type&&this.mml.childNodes.length>=3&&(this.mml=o.introduceNewLayer([this.mml],this.semantic)),(0,s.setAttributes)(this.mml,this.semantic),e[0].mathmlTree||(e[0].mathmlTree=this.semantic.mathmlTree),e.forEach(a.walkTree_),this.mml}}t.CaseLimit=a},7978:function(e,t,n){Object.defineProperty(t,"__esModule",{value:!0}),t.CaseLine=void 0;const r=n(9341),i=n(8672),o=n(8171);class s extends r.AbstractEnrichCase{constructor(e){super(e),this.mml=e.mathmlTree}static test(e){return!!e.mathmlTree&&"line"===e.type}getMathml(){return this.semantic.contentNodes.length&&i.walkTree(this.semantic.contentNodes[0]),this.semantic.childNodes.length&&i.walkTree(this.semantic.childNodes[0]),(0,o.setAttributes)(this.mml,this.semantic),this.mml}}t.CaseLine=s},2124:function(e,t,n){Object.defineProperty(t,"__esModule",{value:!0}),t.CaseMultiindex=void 0;const r=n(6671),i=n(9341),o=n(8672),s=n(8171);class a extends i.AbstractEnrichCase{constructor(e){super(e),this.mml=e.mathmlTree}static multiscriptIndex(e){return"punctuated"===e.type&&"dummy"===e.contentNodes[0].role?o.collapsePunctuated(e):(o.walkTree(e),e.id)}static createNone_(e){const t=r.createElement("none");return e&&(0,s.setAttributes)(t,e),t.setAttribute(s.Attribute.ADDED,"true"),t}completeMultiscript(e,t){const n=r.toArray(this.mml.childNodes).slice(1);let i=0;const c=e=>{for(let t,r=0;t=e[r];r++){const e=n[i];if(e&&t===parseInt(o.getInnerNode(e).getAttribute(s.Attribute.ID)))o.getInnerNode(e).setAttribute(s.Attribute.PARENT,this.semantic.id.toString()),i++;else{const n=this.semantic.querySelectorAll((e=>e.id===t));this.mml.insertBefore(a.createNone_(n[0]),e||null)}}};c(e),n[i]&&"MPRESCRIPTS"!==r.tagName(n[i])?this.mml.insertBefore(n[i],r.createElement("mprescripts")):i++,c(t)}}t.CaseMultiindex=a},4308:function(e,t,n){Object.defineProperty(t,"__esModule",{value:!0}),t.CaseMultiscripts=void 0;const r=n(6671),i=n(7984),o=n(2124),s=n(8672),a=n(8171);class c extends o.CaseMultiindex{static test(e){if(!e.mathmlTree)return!1;return"MMULTISCRIPTS"===r.tagName(e.mathmlTree)&&("superscript"===e.type||"subscript"===e.type)}constructor(e){super(e)}getMathml(){let e,t,n;if((0,a.setAttributes)(this.mml,this.semantic),this.semantic.childNodes[0]&&"subsup"===this.semantic.childNodes[0].role){const r=this.semantic.childNodes[0];e=r.childNodes[0],t=o.CaseMultiindex.multiscriptIndex(this.semantic.childNodes[1]),n=o.CaseMultiindex.multiscriptIndex(r.childNodes[1]);const c=[this.semantic.id,[r.id,e.id,n],t];s.addCollapsedAttribute(this.mml,c),this.mml.setAttribute(a.Attribute.TYPE,r.role),this.completeMultiscript(i.SemanticSkeleton.interleaveIds(n,t),[])}else{e=this.semantic.childNodes[0],t=o.CaseMultiindex.multiscriptIndex(this.semantic.childNodes[1]);const n=[this.semantic.id,e.id,t];s.addCollapsedAttribute(this.mml,n)}const r=i.SemanticSkeleton.collapsedLeafs(n||[],t),c=s.walkTree(e);return s.getInnerNode(c).setAttribute(a.Attribute.PARENT,this.semantic.id.toString()),r.unshift(e.id),this.mml.setAttribute(a.Attribute.CHILDREN,r.join(",")),this.mml}}t.CaseMultiscripts=c},5326:function(e,t,n){Object.defineProperty(t,"__esModule",{value:!0}),t.CaseProof=void 0;const r=n(9341),i=n(8672),o=n(8171);class s extends r.AbstractEnrichCase{constructor(e){super(e),this.mml=e.mathmlTree}static test(e){return!!e.mathmlTree&&("inference"===e.type||"premises"===e.type)}getMathml(){return this.semantic.childNodes.length?(this.semantic.contentNodes.forEach((function(e){i.walkTree(e),(0,o.setAttributes)(e.mathmlTree,e)})),this.semantic.childNodes.forEach((function(e){i.walkTree(e)})),(0,o.setAttributes)(this.mml,this.semantic),this.mml.getAttribute("data-semantic-id")===this.mml.getAttribute("data-semantic-parent")&&this.mml.removeAttribute("data-semantic-parent"),this.mml):this.mml}}t.CaseProof=s},6998:function(e,t,n){Object.defineProperty(t,"__esModule",{value:!0}),t.CaseTable=void 0;const r=n(6671),i=n(9341),o=n(8672),s=n(8171);class a extends i.AbstractEnrichCase{constructor(e){super(e),this.inner=[],this.mml=e.mathmlTree}static test(e){return"matrix"===e.type||"vector"===e.type||"cases"===e.type}getMathml(){const e=o.cloneContentNode(this.semantic.contentNodes[0]),t=this.semantic.contentNodes[1]?o.cloneContentNode(this.semantic.contentNodes[1]):null;if(this.inner=this.semantic.childNodes.map(o.walkTree),this.mml)if("MFENCED"===r.tagName(this.mml)){const n=this.mml.childNodes;this.mml.insertBefore(e,n[0]||null),t&&this.mml.appendChild(t),this.mml=o.rewriteMfenced(this.mml)}else{const n=[e,this.mml];t&&n.push(t),this.mml=o.introduceNewLayer(n,this.semantic)}else this.mml=o.introduceNewLayer([e].concat(this.inner,[t]),this.semantic);return(0,s.setAttributes)(this.mml,this.semantic),this.mml}}t.CaseTable=a},439:function(e,t,n){Object.defineProperty(t,"__esModule",{value:!0}),t.CaseTensor=void 0;const r=n(7984),i=n(2124),o=n(8672),s=n(8171);class a extends i.CaseMultiindex{static test(e){return!!e.mathmlTree&&"tensor"===e.type}constructor(e){super(e)}getMathml(){o.walkTree(this.semantic.childNodes[0]);const e=i.CaseMultiindex.multiscriptIndex(this.semantic.childNodes[1]),t=i.CaseMultiindex.multiscriptIndex(this.semantic.childNodes[2]),n=i.CaseMultiindex.multiscriptIndex(this.semantic.childNodes[3]),a=i.CaseMultiindex.multiscriptIndex(this.semantic.childNodes[4]);(0,s.setAttributes)(this.mml,this.semantic);const c=[this.semantic.id,this.semantic.childNodes[0].id,e,t,n,a];o.addCollapsedAttribute(this.mml,c);const l=r.SemanticSkeleton.collapsedLeafs(e,t,n,a);return l.unshift(this.semantic.childNodes[0].id),this.mml.setAttribute(s.Attribute.CHILDREN,l.join(",")),this.completeMultiscript(r.SemanticSkeleton.interleaveIds(n,a),r.SemanticSkeleton.interleaveIds(e,t)),this.mml}}t.CaseTensor=a},598:function(e,t,n){Object.defineProperty(t,"__esModule",{value:!0}),t.CaseText=void 0;const r=n(9341),i=n(8672),o=n(8171);class s extends r.AbstractEnrichCase{constructor(e){super(e),this.mml=e.mathmlTree}static test(e){return"punctuated"===e.type&&("text"===e.role||e.contentNodes.every((e=>"dummy"===e.role)))}getMathml(){const e=[],t=i.collapsePunctuated(this.semantic,e);return this.mml=i.introduceNewLayer(e,this.semantic),(0,o.setAttributes)(this.mml,this.semantic),this.mml.removeAttribute(o.Attribute.CONTENT),i.addCollapsedAttribute(this.mml,t),this.mml}}t.CaseText=s},7450:function(e,t,n){Object.defineProperty(t,"__esModule",{value:!0}),t.prepareMmlString=t.testTranslation__=t.semanticMathml=t.semanticMathmlSync=t.semanticMathmlNode=void 0;const r=n(1984),i=n(6671),o=n(4886),s=n(1939),a=n(8672),c=n(8171);function l(e){const t=i.cloneNode(e),n=s.getTree(t);return a.enrich(t,n)}function u(e){return l(i.parseInput(e))}function d(e){return e.match(/^<math/)||(e="<math>"+e),e.match(/\/math>$/)||(e+="</math>"),e}n(7813),t.semanticMathmlNode=l,t.semanticMathmlSync=u,t.semanticMathml=function(e,t){o.EnginePromise.getall().then((()=>{const n=i.parseInput(e);t(l(n))}))},t.testTranslation__=function(e){r.Debugger.getInstance().init();const t=u(d(e)).toString();return(0,c.removeAttributePrefix)(t),r.Debugger.getInstance().exit(),t},t.prepareMmlString=d},8171:function(e,t){var n;function r(e){return e.map((function(e){return e.id})).join(",")}function i(e,t){const r=[];"mglyph"===t.role&&r.push("image"),t.attributes.href&&r.push("link"),r.length&&e.setAttribute(n.POSTFIX,r.join(" "))}Object.defineProperty(t,"__esModule",{value:!0}),t.addPrefix=t.removeAttributePrefix=t.setPostfix=t.setAttributes=t.makeIdList=t.EnrichAttributes=t.Attribute=t.Prefix=void 0,t.Prefix="data-semantic-",function(e){e.ADDED="data-semantic-added",e.ALTERNATIVE="data-semantic-alternative",e.CHILDREN="data-semantic-children",e.COLLAPSED="data-semantic-collapsed",e.CONTENT="data-semantic-content",e.EMBELLISHED="data-semantic-embellished",e.FENCEPOINTER="data-semantic-fencepointer",e.FONT="data-semantic-font",e.ID="data-semantic-id",e.ANNOTATION="data-semantic-annotation",e.OPERATOR="data-semantic-operator",e.OWNS="data-semantic-owns",e.PARENT="data-semantic-parent",e.POSTFIX="data-semantic-postfix",e.PREFIX="data-semantic-prefix",e.ROLE="data-semantic-role",e.SPEECH="data-semantic-speech",e.STRUCTURE="data-semantic-structure",e.TYPE="data-semantic-type"}(n=t.Attribute||(t.Attribute={})),t.EnrichAttributes=[n.ADDED,n.ALTERNATIVE,n.CHILDREN,n.COLLAPSED,n.CONTENT,n.EMBELLISHED,n.FENCEPOINTER,n.FONT,n.ID,n.ANNOTATION,n.OPERATOR,n.OWNS,n.PARENT,n.POSTFIX,n.PREFIX,n.ROLE,n.SPEECH,n.STRUCTURE,n.TYPE],t.makeIdList=r,t.setAttributes=function(e,o){e.setAttribute(n.TYPE,o.type);const s=o.allAttributes();for(let n,r=0;n=s[r];r++)e.setAttribute(t.Prefix+n[0].toLowerCase(),n[1]);o.childNodes.length&&e.setAttribute(n.CHILDREN,r(o.childNodes)),o.contentNodes.length&&e.setAttribute(n.CONTENT,r(o.contentNodes)),o.parent&&e.setAttribute(n.PARENT,o.parent.id.toString()),i(e,o)},t.setPostfix=i,t.removeAttributePrefix=function(e){return e.toString().replace(new RegExp(t.Prefix,"g"),"")},t.addPrefix=function(e){return t.Prefix+e}},9775:function(e,t){Object.defineProperty(t,"__esModule",{value:!0}),t.factory=t.getCase=void 0,t.getCase=function(e){for(let n,r=0;n=t.factory[r];r++)if(n.test(e))return n.constr(e);return null},t.factory=[]},7813:function(e,t,n){Object.defineProperty(t,"__esModule",{value:!0});const r=n(4306),i=n(8871),o=n(928),s=n(9763),a=n(7978),c=n(4308),l=n(5326),u=n(6998),d=n(439),h=n(598);n(9775).factory.push({test:s.CaseLimit.test,constr:e=>new s.CaseLimit(e)},{test:o.CaseEmbellished.test,constr:e=>new o.CaseEmbellished(e)},{test:i.CaseDoubleScript.test,constr:e=>new i.CaseDoubleScript(e)},{test:d.CaseTensor.test,constr:e=>new d.CaseTensor(e)},{test:c.CaseMultiscripts.test,constr:e=>new c.CaseMultiscripts(e)},{test:a.CaseLine.test,constr:e=>new a.CaseLine(e)},{test:r.CaseBinomial.test,constr:e=>new r.CaseBinomial(e)},{test:l.CaseProof.test,constr:e=>new l.CaseProof(e)},{test:u.CaseTable.test,constr:e=>new u.CaseTable(e)},{test:h.CaseText.test,constr:e=>new h.CaseText(e)})},8672:function(e,t,n){Object.defineProperty(t,"__esModule",{value:!0}),t.printNodeList__=t.collapsePunctuated=t.formattedOutput_=t.formattedOutput=t.getInnerNode=t.setOperatorAttribute_=t.createInvisibleOperator_=t.rewriteMfenced=t.cloneContentNode=t.addCollapsedAttribute=t.parentNode_=t.isIgnorable_=t.unitChild_=t.descendNode_=t.ascendNewNode=t.validLca_=t.pathToRoot_=t.attachedElement_=t.prunePath_=t.mathmlLca_=t.lcaType=t.functionApplication_=t.isDescendant_=t.insertNewChild_=t.mergeChildren_=t.collectChildNodes_=t.collateChildNodes_=t.childrenSubset_=t.moveSemanticAttributes_=t.introduceLayerAboveLca=t.introduceNewLayer=t.walkTree=t.enrich=t.SETTINGS=void 0;const r=n(1984),i=n(6671),o=n(4886),s=n(4020),a=n(2721),c=n(7984),l=n(8901),u=n(8171),d=n(9775);function h(e){const t=(0,d.getCase)(e);let n;if(t)return n=t.getMathml(),I(n);if(1===e.mathml.length)return r.Debugger.getInstance().output("Walktree Case 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a{constructor(){this.context=new s.SpeechRuleContext,this.parseOrder=i.DynamicCstr.DEFAULT_ORDER,this.parser=new i.DynamicCstrParser(this.parseOrder),this.locale=i.DynamicCstr.DEFAULT_VALUES[i.Axis.LOCALE],this.modality=i.DynamicCstr.DEFAULT_VALUES[i.Axis.MODALITY],this.domain="",this.initialized=!1,this.inherits=null,this.kind="standard",this.customTranscriptions={},this.preconditions=new Map,this.speechRules_=[],this.rank=0,this.parseMethods={Rule:this.defineRule,Generator:this.generateRules,Action:this.defineAction,Precondition:this.definePrecondition,Ignore:this.ignoreRules}}static compareStaticConstraints_(e,t){if(e.length!==t.length)return!1;for(let n,r=0;n=e[r];r++)if(-1===t.indexOf(n))return!1;return!0}static comparePreconditions_(e,t){const n=e.precondition,r=t.precondition;return n.query===r.query&&a.compareStaticConstraints_(n.constraints,r.constraints)}defineRule(e,t,n,r,...i){const s=this.parseAction(n),a=this.parsePrecondition(r,i),c=this.parseCstr(t);if(!(s&&a&&c))return console.error(`Rule Error: ${r}, (${t}): ${n}`),null;const l=new o.SpeechRule(e,c,a,s);return l.precondition.rank=this.rank++,this.addRule(l),l}addRule(e){e.context=this.context,this.speechRules_.unshift(e)}deleteRule(e){const t=this.speechRules_.indexOf(e);-1!==t&&this.speechRules_.splice(t,1)}findRule(e){for(let t,n=0;t=this.speechRules_[n];n++)if(e(t))return t;return null}findAllRules(e){return this.speechRules_.filter(e)}evaluateDefault(e){const t=e.textContent.slice(0);return t.match(/^\s+$/)?this.evaluateWhitespace(t):this.evaluateString(t)}evaluateWhitespace(e){return[]}evaluateCustom(e){const t=this.customTranscriptions[e];return void 0!==t?r.AuditoryDescription.create({text:t},{adjust:!0,translate:!1}):null}evaluateCharacter(e){return this.evaluateCustom(e)||r.AuditoryDescription.create({text:e},{adjust:!0,translate:!0})}removeDuplicates(e){for(let t,n=this.speechRules_.length-1;t=this.speechRules_[n];n--)t!==e&&e.dynamicCstr.equal(t.dynamicCstr)&&a.comparePreconditions_(t,e)&&this.speechRules_.splice(n,1)}getSpeechRules(){return this.speechRules_}setSpeechRules(e){this.speechRules_=e}getPreconditions(){return this.preconditions}parseCstr(e){try{return this.parser.parse(this.locale+"."+this.modality+(this.domain?"."+this.domain:"")+"."+e)}catch(t){if("RuleError"===t.name)return console.error("Rule Error ",`Illegal Dynamic Constraint: ${e}.`,t.message),null;throw t}}parsePrecondition(e,t){try{const n=this.parsePrecondition_(e);e=n[0];let r=n.slice(1);for(const e of t)r=r.concat(this.parsePrecondition_(e));return new o.Precondition(e,...r)}catch(n){if("RuleError"===n.name)return console.error("Rule Error ",`Illegal preconditions: ${e}, ${t}.`,n.message),null;throw n}}parseAction(e){try{return o.Action.fromString(e)}catch(t){if("RuleError"===t.name)return console.error("Rule Error ",`Illegal action: ${e}.`,t.message),null;throw t}}parse(e){this.modality=e.modality||this.modality,this.locale=e.locale||this.locale,this.domain=e.domain||this.domain,this.context.parse(e.functions||[]),"actions"!==e.kind&&(this.kind=e.kind||this.kind,this.inheritRules()),this.parseRules(e.rules||[])}parseRules(e){for(let t,n=0;t=e[n];n++){const e=t[0],n=this.parseMethods[e];e&&n&&n.apply(this,t.slice(1))}}generateRules(e){const t=this.context.customGenerators.lookup(e);t&&t(this)}defineAction(e,t){let n;try{n=o.Action.fromString(t)}catch(e){if("RuleError"===e.name)return void console.error("Action Error ",t,e.message);throw e}const r=this.getFullPreconditions(e);if(!r)return void console.error(`Action Error: No precondition for action ${e}`);this.ignoreRules(e);const i=new RegExp("^\\w+\\.\\w+\\."+(this.domain?"\\w+\\.":""));r.conditions.forEach((([t,r])=>{const s=this.parseCstr(t.toString().replace(i,""));this.addRule(new o.SpeechRule(e,s,r,n))}))}getFullPreconditions(e){const t=this.preconditions.get(e);return t||!this.inherits?t:this.inherits.getFullPreconditions(e)}definePrecondition(e,t,n,...r){const i=this.parsePrecondition(n,r),o=this.parseCstr(t);i&&o?(i.rank=this.rank++,this.preconditions.set(e,new c(o,i))):console.error(`Precondition Error: ${n}, (${t})`)}inheritRules(){if(!this.inherits||!this.inherits.getSpeechRules().length)return;const e=new RegExp("^\\w+\\.\\w+\\."+(this.domain?"\\w+\\.":""));this.inherits.getSpeechRules().forEach((t=>{const n=this.parseCstr(t.dynamicCstr.toString().replace(e,""));this.addRule(new o.SpeechRule(t.name,n,t.precondition,t.action))}))}ignoreRules(e,...t){let n=this.findAllRules((t=>t.name===e));if(!t.length)return void n.forEach(this.deleteRule.bind(this));let r=[];for(const e of t){const t=this.parseCstr(e);for(const e of n)t.equal(e.dynamicCstr)?this.deleteRule(e):r.push(e);n=r,r=[]}}parsePrecondition_(e){const t=this.context.customGenerators.lookup(e);return t?t():[e]}}t.BaseRuleStore=a;class c{constructor(e,t){this.base=e,this._conditions=[],this.constraints=[],this.allCstr={},this.constraints.push(e),this.addCondition(e,t)}get conditions(){return this._conditions}addConstraint(e){if(this.constraints.filter((t=>t.equal(e))).length)return;this.constraints.push(e);const t=[];for(const[n,r]of this.conditions)this.base.equal(n)&&t.push([e,r]);this._conditions=this._conditions.concat(t)}addBaseCondition(e){this.addCondition(this.base,e)}addFullCondition(e){this.constraints.forEach((t=>this.addCondition(t,e)))}addCondition(e,t){const n=e.toString()+" "+t.toString();this.allCstr.condStr||(this.allCstr[n]=!0,this._conditions.push([e,t]))}}t.Condition=c},1462:function(e,t,n){Object.defineProperty(t,"__esModule",{value:!0}),t.BrailleStore=void 0;const r=n(4036),i=n(7478);class o extends i.MathStore{constructor(){super(...arguments),this.modality="braille",this.customTranscriptions={"\u22ca":"\u2808\u2821\u2833"}}evaluateString(e){const t=[],n=Array.from(e);for(let e=0;e<n.length;e++)t.push(this.evaluateCharacter(n[e]));return t}annotations(){for(let e,t=0;e=this.annotators[t];t++)(0,r.activate)(this.locale,e)}}t.BrailleStore=o},8310:function(e,t){var n;Object.defineProperty(t,"__esModule",{value:!0}),t.DefaultComparator=t.DynamicCstrParser=t.DynamicCstr=t.DynamicProperties=t.Axis=void 0,function(e){e.DOMAIN="domain",e.STYLE="style",e.LOCALE="locale",e.TOPIC="topic",e.MODALITY="modality"}(n=t.Axis||(t.Axis={}));class r{constructor(e,t=Object.keys(e)){this.properties=e,this.order=t}static createProp(...e){const t=i.DEFAULT_ORDER,n={};for(let r=0,i=e.length,o=t.length;r<i&&r<o;r++)n[t[r]]=e[r];return new r(n)}getProperties(){return this.properties}getOrder(){return this.order}getAxes(){return this.order}getProperty(e){return this.properties[e]}updateProperties(e){this.properties=e}allProperties(){const e=[];return this.order.forEach((t=>e.push(this.getProperty(t).slice()))),e}toString(){const e=[];return this.order.forEach((t=>e.push(t+": "+this.getProperty(t).toString()))),e.join("\n")}}t.DynamicProperties=r;class i extends r{constructor(e,t){const n={};for(const[t,r]of Object.entries(e))n[t]=[r];super(n,t),this.components=e}static createCstr(...e){const t=i.DEFAULT_ORDER,n={};for(let r=0,i=e.length,o=t.length;r<i&&r<o;r++)n[t[r]]=e[r];return new i(n)}static defaultCstr(){return i.createCstr.apply(null,i.DEFAULT_ORDER.map((function(e){return i.DEFAULT_VALUES[e]})))}static validOrder(e){const t=i.DEFAULT_ORDER.slice();return e.every((e=>{const n=t.indexOf(e);return-1!==n&&t.splice(n,1)}))}getComponents(){return this.components}getValue(e){return this.components[e]}getValues(){return this.order.map((e=>this.getValue(e)))}allProperties(){const e=super.allProperties();for(let t,n,r=0;t=e[r],n=this.order[r];r++){const e=this.getValue(n);-1===t.indexOf(e)&&t.unshift(e)}return e}toString(){return this.getValues().join(".")}equal(e){const t=e.getAxes();if(this.order.length!==t.length)return!1;for(let n,r=0;n=t[r];r++){const t=this.getValue(n);if(!t||e.getValue(n)!==t)return!1}return!0}}t.DynamicCstr=i,i.DEFAULT_ORDER=[n.LOCALE,n.MODALITY,n.DOMAIN,n.STYLE,n.TOPIC],i.BASE_LOCALE="base",i.DEFAULT_VALUE="default",i.DEFAULT_VALUES={[n.LOCALE]:"en",[n.DOMAIN]:i.DEFAULT_VALUE,[n.STYLE]:i.DEFAULT_VALUE,[n.TOPIC]:i.DEFAULT_VALUE,[n.MODALITY]:"speech"};t.DynamicCstrParser=class{constructor(e){this.order=e}parse(e){const t=e.split("."),n={};if(t.length>this.order.length)throw new Error("Invalid dynamic constraint: "+n);let r=0;for(let e,i=0;e=this.order[i],t.length;i++,r++){const r=t.shift();n[e]=r}return new i(n,this.order.slice(0,r))}};t.DefaultComparator=class{constructor(e,t=new r(e.getProperties(),e.getOrder())){this.reference=e,this.fallback=t,this.order=this.reference.getOrder()}getReference(){return this.reference}setReference(e,t){this.reference=e,this.fallback=t||new r(e.getProperties(),e.getOrder()),this.order=this.reference.getOrder()}match(e){const t=e.getAxes();return t.length===this.reference.getAxes().length&&t.every((t=>{const n=e.getValue(t);return n===this.reference.getValue(t)||-1!==this.fallback.getProperty(t).indexOf(n)}))}compare(e,t){let n=!1;for(let r,i=0;r=this.order[i];i++){const i=e.getValue(r),o=t.getValue(r);if(!n){const e=this.reference.getValue(r);if(e===i&&e!==o)return-1;if(e===o&&e!==i)return 1;if(e===i&&e===o)continue;e!==i&&e!==o&&(n=!0)}const s=this.fallback.getProperty(r),a=s.indexOf(i),c=s.indexOf(o);if(a<c)return-1;if(c<a)return 1}return 0}toString(){return this.reference.toString()+"\n"+this.fallback.toString()}}},1058:function(e,t,n){Object.defineProperty(t,"__esModule",{value:!0}),t.numbersToAlpha=t.Grammar=t.ATTRIBUTE=void 0;const r=n(6671),i=n(4886),o=n(3319),s=n(4524);t.ATTRIBUTE="grammar";class a{constructor(){this.currentFlags={},this.parameters_={},this.corrections_={},this.preprocessors_={},this.stateStack_=[]}static getInstance(){return a.instance=a.instance||new a,a.instance}static parseInput(e){const t={},n=e.split(":");for(let e=0,r=n.length;e<r;e++){const r=n[e].split("="),i=r[0].trim();r[1]?t[i]=r[1].trim():i.match(/^!/)?t[i.slice(1)]=!1:t[i]=!0}return t}static parseState(e){const t={},n=e.split(" ");for(let e=0,r=n.length;e<r;e++){const r=n[e].split(":"),i=r[0],o=r[1];t[i]=o||!0}return t}static translateString_(e){if(e.match(/:unit$/))return a.translateUnit_(e);const t=i.default.getInstance();let n=t.evaluator(e,t.dynamicCstr);return n=null===n?e:n,a.getInstance().getParameter("plural")&&(n=s.LOCALE.FUNCTIONS.plural(n)),n}static translateUnit_(e){e=a.prepareUnit_(e);const t=i.default.getInstance(),n=a.getInstance().getParameter("plural"),r=t.strict,o=`${t.locale}.${t.modality}.default`;let c,l;return t.strict=!0,n&&(c=t.defaultParser.parse(o+".plural"),l=t.evaluator(e,c)),l?(t.strict=r,l):(c=t.defaultParser.parse(o+".default"),l=t.evaluator(e,c),t.strict=r,l?(n&&(l=s.LOCALE.FUNCTIONS.plural(l)),l):a.cleanUnit_(e))}static prepareUnit_(e){const t=e.match(/:unit$/);return t?e.slice(0,t.index).replace(/\s+/g," ")+e.slice(t.index):e}static cleanUnit_(e){return e.match(/:unit$/)?e.replace(/:unit$/,""):e}clear(){this.parameters_={},this.stateStack_=[]}setParameter(e,t){const n=this.parameters_[e];return t?this.parameters_[e]=t:delete this.parameters_[e],n}getParameter(e){return this.parameters_[e]}setCorrection(e,t){this.corrections_[e]=t}setPreprocessor(e,t){this.preprocessors_[e]=t}getCorrection(e){return this.corrections_[e]}getState(){const e=[];for(const t in this.parameters_){const n=this.parameters_[t];e.push("string"==typeof n?t+":"+n:t)}return e.join(" ")}pushState(e){for(const t in e)e[t]=this.setParameter(t,e[t]);this.stateStack_.push(e)}popState(){const e=this.stateStack_.pop();for(const t in e)this.setParameter(t,e[t])}setAttribute(e){if(e&&e.nodeType===r.NodeType.ELEMENT_NODE){const n=this.getState();n&&e.setAttribute(t.ATTRIBUTE,n)}}preprocess(e){return this.runProcessors_(e,this.preprocessors_)}correct(e){return this.runProcessors_(e,this.corrections_)}apply(e,t){return this.currentFlags=t||{},e=this.currentFlags.adjust||this.currentFlags.preprocess?a.getInstance().preprocess(e):e,(this.parameters_.translate||this.currentFlags.translate)&&(e=a.translateString_(e)),e=this.currentFlags.adjust||this.currentFlags.correct?a.getInstance().correct(e):e,this.currentFlags={},e}runProcessors_(e,t){for(const n in this.parameters_){const r=t[n];if(!r)continue;const i=this.parameters_[n];e=!0===i?r(e):r(e,i)}return e}}function c(e,t){if(!t||!e)return e;const n=s.LOCALE.FUNCTIONS.fontRegexp(o.localFont(t));return e.replace(n,"")}function l(e){return e.match(/\d+/)?s.LOCALE.NUMBERS.numberToWords(parseInt(e,10)):e}t.Grammar=a,t.numbersToAlpha=l,a.getInstance().setCorrection("localFont",o.localFont),a.getInstance().setCorrection("localRole",o.localRole),a.getInstance().setCorrection("localEnclose",o.localEnclose),a.getInstance().setCorrection("ignoreFont",c),a.getInstance().setPreprocessor("annotation",(function(e,t){return e+":"+t})),a.getInstance().setPreprocessor("noTranslateText",(function(e){return e.match(new RegExp("^["+s.LOCALE.MESSAGES.regexp.TEXT+"]+$"))&&(a.getInstance().currentFlags.translate=!1),e})),a.getInstance().setCorrection("ignoreCaps",(function(e){let t=s.LOCALE.ALPHABETS.capPrefix[i.default.getInstance().domain];return void 0===t&&(t=s.LOCALE.ALPHABETS.capPrefix.default),c(e,t)})),a.getInstance().setPreprocessor("numbers2alpha",l)},4161:function(e,t,n){Object.defineProperty(t,"__esModule",{value:!0}),t.enumerate=t.lookupString=t.lookupCategory=t.lookupRule=t.addSiUnitRules=t.addUnitRules=t.addFunctionRules=t.addSymbolRules=t.defineRule=t.defineRules=t.setSiPrefixes=void 0;const r=n(1984),i=n(4886),o=n(2371),s=n(4974),a=n(8310);let c=a.DynamicCstr.DEFAULT_VALUES[a.Axis.LOCALE],l=a.DynamicCstr.DEFAULT_VALUES[a.Axis.MODALITY],u={};t.setSiPrefixes=function(e){u=e};const d={};function h(e,t,n,r){const i=E(t);y(i,n),i.defineRulesFromMappings(e,c,l,t,r)}function p(e){if(N(e))return;const t=e.names,n=e.mappings,r=e.category;for(let e,i=0;e=t[i];i++)h(e,e,r,n)}function f(e){for(const t of Object.keys(u)){const n=Object.assign({},e);n.mappings={};const r=u[t];n.key=t+n.key,n.names=n.names.map((function(e){return t+e}));for(const t of Object.keys(e.mappings)){n.mappings[t]={};for(const i of Object.keys(e.mappings[t]))n.mappings[t][i]=o.locales[c]().FUNCTIONS.si(r,e.mappings[t][i])}b(n)}b(e)}function m(e,t){const n=d[e];return n?n.lookupRule(null,t):null}function g(e,t){const n=m(e,t);return n?n.action:null}function S(e,t){return t=t||{},e.length?(t[e[0]]=S(e.slice(1),t[e[0]]),t):t}function b(e){const t=e.names;t&&(e.names=t.map((function(e){return e+":unit"}))),p(e)}function N(e){return!(!e.locale&&!e.modality)&&(c=e.locale||c,l=e.modality||l,!0)}function E(e){let t=d[e];return t?(r.Debugger.getInstance().output("Store exists! 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r.default.getInstance().strict?t.cstr.equal(e):r.default.getInstance().comparator.match(t.cstr)}defineRulesFromMappings(e,t,n,r,i){for(const o in i)for(const s in i[o]){const a=i[o][s];this.defineRuleFromStrings(e,t,n,o,s,r,a)}}getRules(e){let t=this.rules.get(e);return t||(t=[],this.rules.set(e,t)),t}defineRuleFromStrings(e,t,n,i,o,s,a){let c=this.getRules(t);const l=r.default.getInstance().parsers[i]||r.default.getInstance().defaultParser,u=r.default.getInstance().comparators[i],d=`${t}.${n}.${i}.${o}`,h=l.parse(d),p=u?u():r.default.getInstance().comparator,f=p.getReference();p.setReference(h);const m={cstr:h,action:a};c=c.filter((e=>!h.equal(e.cstr))),c.push(m),this.rules.set(t,c),p.setReference(f)}lookupRule(e,t){let n=this.getRules(t.getValue(i.Axis.LOCALE));return n=n.filter((function(e){return o.testDynamicConstraints_(t,e)})),1===n.length?n[0]:n.length?n.sort(((e,t)=>r.default.getInstance().comparator.compare(e.cstr,t.cstr)))[0]:null}}t.MathSimpleStore=o},7478:function(e,t,n){Object.defineProperty(t,"__esModule",{value:!0}),t.MathStore=void 0;const r=n(1426),i=n(4524),o=n(4036),s=n(1970),a=n(7039);class c extends s.BaseRuleStore{constructor(){super(),this.annotators=[],this.parseMethods.Alias=this.defineAlias,this.parseMethods.SpecializedRule=this.defineSpecializedRule,this.parseMethods.Specialized=this.defineSpecialized}initialize(){this.initialized||(this.annotations(),this.initialized=!0)}annotations(){for(let e,t=0;e=this.annotators[t];t++)(0,o.activate)(this.domain,e)}defineAlias(e,t,...n){const r=this.parsePrecondition(t,n);if(!r)return void console.error(`Precondition Error: ${t} ${n}`);const i=this.preconditions.get(e);i?i.addFullCondition(r):console.error(`Alias Error: No precondition by the name of ${e}`)}defineRulesAlias(e,t,...n){const r=this.findAllRules((function(t){return t.name===e}));if(0===r.length)throw new a.OutputError("Rule with name "+e+" does not exist.");const i=[];r.forEach((e=>{(e=>{const t=e.dynamicCstr.toString(),n=e.action.toString();for(let e,r=0;e=i[r];r++)if(e.action===n&&e.cstr===t)return!1;return i.push({cstr:t,action:n}),!0})(e)&&this.addAlias_(e,t,n)}))}defineSpecializedRule(e,t,n,r){const i=this.parseCstr(t),o=this.findRule((t=>t.name===e&&i.equal(t.dynamicCstr))),s=this.parseCstr(n);if(!o&&r)throw new a.OutputError("Rule named "+e+" with style "+t+" does not exist.");const c=r?a.Action.fromString(r):o.action,l=new a.SpeechRule(o.name,s,o.precondition,c);this.addRule(l)}defineSpecialized(e,t,n){const r=this.parseCstr(n);if(!r)return void console.error(`Dynamic Constraint Error: ${n}`);const i=this.preconditions.get(e);i?i.addConstraint(r):console.error(`Alias Error: No precondition by the name of ${e}`)}evaluateString(e){const t=[];if(e.match(/^\s+$/))return t;let n=this.matchNumber_(e);if(n&&n.length===e.length)return t.push(this.evaluateCharacter(n.number)),t;const o=r.removeEmpty(e.replace(/\s/g," ").split(" "));for(let e,r=0;e=o[r];r++)if(1===e.length)t.push(this.evaluateCharacter(e));else if(e.match(new RegExp("^["+i.LOCALE.MESSAGES.regexp.TEXT+"]+$")))t.push(this.evaluateCharacter(e));else{let r=e;for(;r;){n=this.matchNumber_(r);const e=r.match(new RegExp("^["+i.LOCALE.MESSAGES.regexp.TEXT+"]+"));if(n)t.push(this.evaluateCharacter(n.number)),r=r.substring(n.length);else if(e)t.push(this.evaluateCharacter(e[0])),r=r.substring(e[0].length);else{const e=Array.from(r),n=e[0];t.push(this.evaluateCharacter(n)),r=e.slice(1).join("")}}}return t}parse(e){super.parse(e),this.annotators=e.annotators||[]}addAlias_(e,t,n){const r=this.parsePrecondition(t,n),i=new a.SpeechRule(e.name,e.dynamicCstr,r,e.action);i.name=e.name,this.addRule(i)}matchNumber_(e){const t=e.match(new RegExp("^"+i.LOCALE.MESSAGES.regexp.NUMBER)),n=e.match(new RegExp("^"+c.regexp.NUMBER));if(!t&&!n)return null;const r=n&&n[0]===e;if(t&&t[0]===e||!r)return t?{number:t[0],length:t[0].length}:null;return{number:n[0].replace(new RegExp(c.regexp.DIGIT_GROUP,"g"),"X").replace(new RegExp(c.regexp.DECIMAL_MARK,"g"),i.LOCALE.MESSAGES.regexp.DECIMAL_MARK).replace(/X/g,i.LOCALE.MESSAGES.regexp.DIGIT_GROUP.replace(/\\/g,"")),length:n[0].length}}}t.MathStore=c,c.regexp={NUMBER:"((\\d{1,3})(?=(,| ))((,| )\\d{3})*(\\.\\d+)?)|^\\d*\\.\\d+|^\\d+",DECIMAL_MARK:"\\.",DIGIT_GROUP:","}},7039:function(e,t,n){Object.defineProperty(t,"__esModule",{value:!0}),t.OutputError=t.Precondition=t.Action=t.Component=t.ActionType=t.SpeechRule=void 0;const r=n(4886),i=n(1058);var o;function s(e){switch(e){case"[n]":return o.NODE;case"[m]":return o.MULTI;case"[t]":return o.TEXT;case"[p]":return o.PERSONALITY;default:throw"Parse error: "+e}}t.SpeechRule=class{constructor(e,t,n,r){this.name=e,this.dynamicCstr=t,this.precondition=n,this.action=r,this.context=null}toString(){return this.name+" | "+this.dynamicCstr.toString()+" | "+this.precondition.toString()+" ==> "+this.action.toString()}},function(e){e.NODE="NODE",e.MULTI="MULTI",e.TEXT="TEXT",e.PERSONALITY="PERSONALITY"}(o=t.ActionType||(t.ActionType={}));class a{constructor({type:e,content:t,attributes:n,grammar:r}){this.type=e,this.content=t,this.attributes=n,this.grammar=r}static grammarFromString(e){return i.Grammar.parseInput(e)}static fromString(e){const t={type:s(e.substring(0,3))};let n=e.slice(3).trim();if(!n)throw new u("Missing content.");switch(t.type){case o.TEXT:if('"'===n[0]){const e=d(n,"\\(")[0].trim();if('"'!==e.slice(-1))throw new u("Invalid string syntax.");t.content=e,n=n.slice(e.length).trim(),-1===n.indexOf("(")&&(n="");break}case o.NODE:case o.MULTI:{const e=n.indexOf(" (");if(-1===e){t.content=n.trim(),n="";break}t.content=n.substring(0,e).trim(),n=n.slice(e).trim()}}if(n){const e=a.attributesFromString(n);e.grammar&&(t.grammar=e.grammar,delete e.grammar),Object.keys(e).length&&(t.attributes=e)}return new a(t)}static attributesFromString(e){if("("!==e[0]||")"!==e.slice(-1))throw new u("Invalid attribute expression: "+e);const t={},n=d(e.slice(1,-1),",");for(let e=0,r=n.length;e<r;e++){const r=n[e],o=r.indexOf(":");if(-1===o)t[r.trim()]="true";else{const e=r.substring(0,o).trim(),n=r.slice(o+1).trim();t[e]=e===i.ATTRIBUTE?a.grammarFromString(n):n}}return t}toString(){let e="";e+=function(e){switch(e){case o.NODE:return"[n]";case o.MULTI:return"[m]";case o.TEXT:return"[t]";case o.PERSONALITY:return"[p]";default:throw"Unknown type error: "+e}}(this.type),e+=this.content?" "+this.content:"";const t=this.attributesToString();return e+=t?" "+t:"",e}grammarToString(){return this.getGrammar().join(":")}getGrammar(){const e=[];for(const t in this.grammar)!0===this.grammar[t]?e.push(t):!1===this.grammar[t]?e.push("!"+t):e.push(t+"="+this.grammar[t]);return e}attributesToString(){const e=this.getAttributes(),t=this.grammarToString();return t&&e.push("grammar:"+t),e.length>0?"("+e.join(", ")+")":""}getAttributes(){const e=[];for(const t in this.attributes){const n=this.attributes[t];"true"===n?e.push(t):e.push(t+":"+n)}return e}}t.Component=a;class c{constructor(e){this.components=e}static fromString(e){const t=d(e,";").filter((function(e){return e.match(/\S/)})).map((function(e){return e.trim()})),n=[];for(let e=0,r=t.length;e<r;e++){const r=a.fromString(t[e]);r&&n.push(r)}return new c(n)}toString(){return this.components.map((function(e){return e.toString()})).join("; ")}}t.Action=c;class l{constructor(e,...t){this.query=e,this.constraints=t;const[n,r]=this.presetPriority();this.priority=n?r:this.calculatePriority()}static constraintValue(e,t){for(let n,r=0;n=t[r];r++)if(e.match(n))return++r;return 0}toString(){const e=this.constraints.join(", ");return`${this.query}, ${e} (${this.priority}, ${this.rank})`}calculatePriority(){const e=l.constraintValue(this.query,l.queryPriorities);if(!e)return 0;const t=this.query.match(/^self::.+\[(.+)\]/)[1];return 100*e+10*l.constraintValue(t,l.attributePriorities)}presetPriority(){if(!this.constraints.length)return[!1,0];const e=this.constraints[this.constraints.length-1].match(/^priority=(.*$)/);if(!e)return[!1,0];this.constraints.pop();const t=parseFloat(e[1]);return[!0,isNaN(t)?0:t]}}t.Precondition=l,l.queryPriorities=[/^self::\*\[.+\]$/,/^self::[\w-]+\[.+\]$/],l.attributePriorities=[/^@[\w-]+$/,/^@[\w-]+!=".+"$/,/^not\(contains\(@[\w-]+,\s*".+"\)\)$/,/^contains\(@[\w-]+,".+"\)$/,/^@[\w-]+=".+"$/];class u extends r.SREError{constructor(e){super(e),this.name="RuleError"}}function d(e,t){const n=[];let r="";for(;""!==e;){const i=e.search(t);if(-1===i){if((e.match(/"/g)||[]).length%2!=0)throw new u("Invalid string in expression: "+e);n.push(r+e),r="",e=""}else if((e.substring(0,i).match(/"/g)||[]).length%2==0)n.push(r+e.substring(0,i)),r="",e=e.substring(i+1);else{const t=e.substring(i).search('"');if(-1===t)throw new u("Invalid string in expression: "+e);r+=e.substring(0,i+t+1),e=e.substring(i+t+1)}}return r&&n.push(r),n}t.OutputError=u},443:function(e,t,n){Object.defineProperty(t,"__esModule",{value:!0}),t.SpeechRuleContext=void 0;const r=n(5024),i=n(3686);t.SpeechRuleContext=class{constructor(){this.customQueries=new i.CustomQueries,this.customStrings=new i.CustomStrings,this.contextFunctions=new i.ContextFunctions,this.customGenerators=new i.CustomGenerators}applyCustomQuery(e,t){const n=this.customQueries.lookup(t);return n?n(e):null}applySelector(e,t){return this.applyCustomQuery(e,t)||r.evalXPath(t,e)}applyQuery(e,t){const n=this.applySelector(e,t);return n.length>0?n[0]:null}applyConstraint(e,t){return!!this.applyQuery(e,t)||r.evaluateBoolean(t,e)}constructString(e,t){if(!t)return"";if('"'===t.charAt(0))return t.slice(1,-1);const n=this.customStrings.lookup(t);return n?n(e):r.evaluateString(t,e)}parse(e){const t=Array.isArray(e)?e:Object.entries(e);for(let e,n=0;e=t[n];n++){switch(e[0].slice(0,3)){case"CQF":this.customQueries.add(e[0],e[1]);break;case"CSF":this.customStrings.add(e[0],e[1]);break;case"CTF":this.contextFunctions.add(e[0],e[1]);break;case"CGF":this.customGenerators.add(e[0],e[1]);break;default:console.error("FunctionError: Invalid function name "+e[0])}}}}},6060:function(e,t,n){Object.defineProperty(t,"__esModule",{value:!0}),t.storeFactory=t.SpeechRuleEngine=void 0;const r=n(4148),i=n(1984),o=n(6671),s=n(4886),a=n(4998),c=n(5024),l=n(7278),u=n(9805),d=n(1462),h=n(8310),p=n(1058),f=n(7478),m=n(7039),g=n(2803);class S{constructor(){this.trie=null,this.evaluators_={},this.trie=new g.Trie}static getInstance(){return S.instance=S.instance||new S,S.instance}static debugSpeechRule(e,t){const n=e.precondition,r=e.context.applyQuery(t,n.query);i.Debugger.getInstance().output(n.query,r?r.toString():r),n.constraints.forEach((n=>i.Debugger.getInstance().output(n,e.context.applyConstraint(t,n))))}static debugNamedSpeechRule(e,t){const n=S.getInstance().trie.collectRules().filter((t=>t.name==e));for(let r,o=0;r=n[o];o++)i.Debugger.getInstance().output("Rule",e,"DynamicCstr:",r.dynamicCstr.toString(),"number",o),S.debugSpeechRule(r,t)}evaluateNode(e){(0,c.updateEvaluator)(e);const t=(new Date).getTime();let n=[];try{n=this.evaluateNode_(e)}catch(e){console.error("Something went wrong computing speech."),i.Debugger.getInstance().output(e)}const r=(new Date).getTime();return i.Debugger.getInstance().output("Time:",r-t),n}toString(){return this.trie.collectRules().map((e=>e.toString())).join("\n")}runInSetting(e,t){const n=s.default.getInstance(),r={};for(const t in e)r[t]=n[t],n[t]=e[t];n.setDynamicCstr();const i=t();for(const e in r)n[e]=r[e];return n.setDynamicCstr(),i}addStore(e){const t=N(e);"abstract"!==t.kind&&t.getSpeechRules().forEach((e=>this.trie.addRule(e))),this.addEvaluator(t)}processGrammar(e,t,n){const r={};for(const i in n){const o=n[i];r[i]="string"==typeof o?e.constructString(t,o):o}p.Grammar.getInstance().pushState(r)}addEvaluator(e){const t=e.evaluateDefault.bind(e),n=this.evaluators_[e.locale];if(n)return void(n[e.modality]=t);const r={};r[e.modality]=t,this.evaluators_[e.locale]=r}getEvaluator(e,t){const n=this.evaluators_[e]||this.evaluators_[h.DynamicCstr.DEFAULT_VALUES[h.Axis.LOCALE]];return n[t]||n[h.DynamicCstr.DEFAULT_VALUES[h.Axis.MODALITY]]}enumerate(e){return this.trie.enumerate(e)}evaluateNode_(e){return e?(this.updateConstraint_(),this.evaluateTree_(e)):[]}evaluateTree_(e){const t=s.default.getInstance();let n;i.Debugger.getInstance().output(t.mode!==a.Mode.HTTP?e.toString():e),p.Grammar.getInstance().setAttribute(e);const o=this.lookupRule(e,t.dynamicCstr);if(!o)return t.strict?[]:(n=this.getEvaluator(t.locale,t.modality)(e),e.attributes&&this.addPersonality_(n,{},!1,e),n);i.Debugger.getInstance().generateOutput((()=>["Apply Rule:",o.name,o.dynamicCstr.toString(),(t.mode,a.Mode.HTTP,e).toString()]));const l=o.context,u=o.action.components;n=[];for(let t,i=0;t=u[i];i++){let i=[];const o=t.content||"",a=t.attributes||{};let u=!1;t.grammar&&this.processGrammar(l,e,t.grammar);let d=null;if(a.engine){d=s.default.getInstance().dynamicCstr.getComponents();const e=p.Grammar.parseInput(a.engine);s.default.getInstance().setDynamicCstr(e)}switch(t.type){case m.ActionType.NODE:{const t=l.applyQuery(e,o);t&&(i=this.evaluateTree_(t))}break;case m.ActionType.MULTI:{u=!0;const t=l.applySelector(e,o);t.length>0&&(i=this.evaluateNodeList_(l,t,a.sepFunc,l.constructString(e,a.separator),a.ctxtFunc,l.constructString(e,a.context)))}break;case m.ActionType.TEXT:{const t=a.span,n={};if(t){const r=(0,c.evalXPath)(t,e);r.length&&(n.extid=r[0].getAttribute("extid"))}const s=l.constructString(e,o);(s||""===s)&&(i=Array.isArray(s)?s.map((function(e){return r.AuditoryDescription.create({text:e.speech,attributes:e.attributes},{adjust:!0})})):[r.AuditoryDescription.create({text:s,attributes:n},{adjust:!0})])}break;case m.ActionType.PERSONALITY:default:i=[r.AuditoryDescription.create({text:o})]}i[0]&&!u&&(a.context&&(i[0].context=l.constructString(e,a.context)+(i[0].context||"")),a.annotation&&(i[0].annotation=a.annotation)),this.addLayout(i,a,u),t.grammar&&p.Grammar.getInstance().popState(),n=n.concat(this.addPersonality_(i,a,u,e)),d&&s.default.getInstance().setDynamicCstr(d)}return n}evaluateNodeList_(e,t,n,i,o,s){if(!t.length)return[];const a=i||"",c=s||"",l=e.contextFunctions.lookup(o),u=l?l(t,c):function(){return c},d=e.contextFunctions.lookup(n),h=d?d(t,a):function(){return[r.AuditoryDescription.create({text:a},{translate:!0})]};let p=[];for(let e,n=0;e=t[n];n++){const r=this.evaluateTree_(e);if(r.length>0&&(r[0].context=u()+(r[0].context||""),p=p.concat(r),n<t.length-1)){const e=h();p=p.concat(e)}}return p}addLayout(e,t,n){const i=t.layout;i&&(i.match(/^begin/)?e.unshift(new r.AuditoryDescription({text:"",layout:i})):i.match(/^end/)?e.push(new r.AuditoryDescription({text:"",layout:i})):(e.unshift(new r.AuditoryDescription({text:"",layout:`begin${i}`})),e.push(new r.AuditoryDescription({text:"",layout:`end${i}`}))))}addPersonality_(e,t,n,i){const o={};let s=null;for(const e of a.personalityPropList){const n=t[e];if(void 0===n)continue;const r=parseFloat(n),i=isNaN(r)?'"'===n.charAt(0)?n.slice(1,-1):n:r;e===a.personalityProps.PAUSE?s=i:o[e]=i}for(let t,n=0;t=e[n];n++)this.addRelativePersonality_(t,o),this.addExternalAttributes_(t,i);if(n&&e.length&&delete e[e.length-1].personality[a.personalityProps.JOIN],s&&e.length){const t=e[e.length-1];t.text||Object.keys(t.personality).length?e.push(r.AuditoryDescription.create({text:"",personality:{pause:s}})):t.personality[a.personalityProps.PAUSE]=s}return e}addExternalAttributes_(e,t){if(t.hasAttributes()){const n=t.attributes;for(let t=n.length-1;t>=0;t--){const r=n[t].name;!e.attributes[r]&&r.match(/^ext/)&&(e.attributes[r]=n[t].value)}}}addRelativePersonality_(e,t){if(!e.personality)return e.personality=t,e;const n=e.personality;for(const e in t)n[e]&&"number"==typeof n[e]&&"number"==typeof t[e]?n[e]=n[e]+t[e]:n[e]||(n[e]=t[e]);return e}updateConstraint_(){const e=s.default.getInstance().dynamicCstr,t=s.default.getInstance().strict,n=this.trie,r={};let i=e.getValue(h.Axis.LOCALE),o=e.getValue(h.Axis.MODALITY),a=e.getValue(h.Axis.DOMAIN);n.hasSubtrie([i,o,a])||(a=h.DynamicCstr.DEFAULT_VALUES[h.Axis.DOMAIN],n.hasSubtrie([i,o,a])||(o=h.DynamicCstr.DEFAULT_VALUES[h.Axis.MODALITY],n.hasSubtrie([i,o,a])||(i=h.DynamicCstr.DEFAULT_VALUES[h.Axis.LOCALE]))),r[h.Axis.LOCALE]=[i],r[h.Axis.MODALITY]=["summary"!==o?o:h.DynamicCstr.DEFAULT_VALUES[h.Axis.MODALITY]],r[h.Axis.DOMAIN]=["speech"!==o?h.DynamicCstr.DEFAULT_VALUES[h.Axis.DOMAIN]:a];const c=e.getOrder();for(let n,i=0;n=c[i];i++)if(!r[n]){const i=e.getValue(n),o=this.makeSet_(i,e.preference),s=h.DynamicCstr.DEFAULT_VALUES[n];t||i===s||o.push(s),r[n]=o}e.updateProperties(r)}makeSet_(e,t){return t&&Object.keys(t).length?e.split(":"):[e]}lookupRule(e,t){if(!e||e.nodeType!==o.NodeType.ELEMENT_NODE&&e.nodeType!==o.NodeType.TEXT_NODE)return null;const n=this.lookupRules(e,t);return n.length>0?this.pickMostConstraint_(t,n):null}lookupRules(e,t){return this.trie.lookupRules(e,t.allProperties())}pickMostConstraint_(e,t){const n=s.default.getInstance().comparator;return t.sort((function(e,t){return n.compare(e.dynamicCstr,t.dynamicCstr)||t.precondition.priority-e.precondition.priority||t.precondition.constraints.length-e.precondition.constraints.length||t.precondition.rank-e.precondition.rank})),i.Debugger.getInstance().generateOutput((()=>t.map((e=>e.name+"("+e.dynamicCstr.toString()+")"))).bind(this)),t[0]}}t.SpeechRuleEngine=S;const b=new Map;function N(e){const t=`${e.locale}.${e.modality}.${e.domain}`;if("actions"===e.kind){const n=b.get(t);return n.parse(e),n}u.init(),e&&!e.functions&&(e.functions=l.getStore(e.locale,e.modality,e.domain));const n="braille"===e.modality?new d.BrailleStore:new f.MathStore;return b.set(t,n),e.inherits&&(n.inherits=b.get(`${e.inherits}.${e.modality}.${e.domain}`)),n.parse(e),n.initialize(),n}t.storeFactory=N,s.default.nodeEvaluator=S.getInstance().evaluateNode.bind(S.getInstance())},3686:function(e,t){Object.defineProperty(t,"__esModule",{value:!0}),t.CustomGenerators=t.ContextFunctions=t.CustomStrings=t.CustomQueries=void 0;class n{constructor(e,t){this.prefix=e,this.store=t}add(e,t){this.checkCustomFunctionSyntax_(e)&&(this.store[e]=t)}addStore(e){const t=Object.keys(e.store);for(let n,r=0;n=t[r];r++)this.add(n,e.store[n])}lookup(e){return this.store[e]}checkCustomFunctionSyntax_(e){const t=new RegExp("^"+this.prefix);return!!e.match(t)||(console.error("FunctionError: Invalid function name. Expected prefix "+this.prefix),!1)}}t.CustomQueries=class extends n{constructor(){super("CQF",{})}};t.CustomStrings=class extends n{constructor(){super("CSF",{})}};t.ContextFunctions=class extends n{constructor(){super("CTF",{})}};t.CustomGenerators=class extends n{constructor(){super("CGF",{})}}},931:function(e,t,n){Object.defineProperty(t,"__esModule",{value:!0}),t.contentIterator=t.pauseSeparator=t.nodeCounter=void 0;const r=n(4148),i=n(5024),o=n(4886);t.nodeCounter=function(e,t){const n=e.length;let r=0,i=t;return t||(i=""),function(){return r<n&&(r+=1),i+" "+r}},t.pauseSeparator=function(e,t){const n=parseFloat(t),i=isNaN(n)?t:n;return function(){return[r.AuditoryDescription.create({text:"",personality:{pause:i}})]}},t.contentIterator=function(e,t){let n;return n=e.length>0?i.evalXPath("../../content/*",e[0]):[],function(){const e=n.shift(),i=t?[r.AuditoryDescription.create({text:t},{translate:!0})]:[];if(!e)return i;const s=o.default.evaluateNode(e);return 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r=e.comp.shift(),i=null,o=[];for(;e.comp.length;)if(o=[],r.length)i&&t.push(i),n.push(r),i=e.rel.shift(),r=e.comp.shift();else{for(i&&o.push(i);!r.length&&e.comp.length;)r=e.comp.shift(),o.push(e.rel.shift());i=d(o,r,n)}o.length||r.length?(t.push(i),n.push(r)):(o.push(i),d(o,r,n));return{rel:t,comp:n}}(t):t,e=t.comp[0];for(let n,r,i=1;n=t.comp[i],r=t.rel[i-1];i++)e.push(r),e=e.concat(n);return t=u.partitionNodes(e,(function(e){return e.textContent===o.invisibleTimes()&&("operator"===e.type||"infixop"===e.type)})),t.rel.length?h(t.comp.shift(),t.rel,t.comp):e}))),s.add(new a.SemanticTreeHeuristic("simple2prefix",(e=>(e.textContent.length>1&&!e.textContent[0].match(/[A-Z]/)&&(e.role="prefix function"),e)),(e=>"braille"===i.default.getInstance().modality&&"identifier"===e.type))),s.add(new a.SemanticTreeHeuristic("detect_cycle",(e=>{e.type="matrix",e.role="cycle";const t=e.childNodes[0];return t.type="row",t.role="cycle",t.textContent="",t.contentNodes=[],e}),(e=>"fenced"===e.type&&"infixop"===e.childNodes[0].type&&"implicit"===e.childNodes[0].role&&e.childNodes[0].childNodes.every((function(e){return"number"===e.type}))&&e.childNodes[0].contentNodes.every((function(e){return"space"===e.role})))))},8122:function(e,t,n){Object.defineProperty(t,"__esModule",{value:!0}),t.SemanticMathml=void 0;const r=n(6671),i=n(6098),o=n(6161),s=n(7793),a=n(8901);class c extends i.SemanticAbstractParser{constructor(){super("MathML"),this.parseMap_={SEMANTICS:this.semantics_.bind(this),MATH:this.rows_.bind(this),MROW:this.rows_.bind(this),MPADDED:this.rows_.bind(this),MSTYLE:this.rows_.bind(this),MFRAC:this.fraction_.bind(this),MSUB:this.limits_.bind(this),MSUP:this.limits_.bind(this),MSUBSUP:this.limits_.bind(this),MOVER:this.limits_.bind(this),MUNDER:this.limits_.bind(this),MUNDEROVER:this.limits_.bind(this),MROOT:this.root_.bind(this),MSQRT:this.sqrt_.bind(this),MTABLE:this.table_.bind(this),MLABELEDTR:this.tableLabeledRow_.bind(this),MTR:this.tableRow_.bind(this),MTD:this.tableCell_.bind(this),MS:this.text_.bind(this),MTEXT:this.text_.bind(this),MSPACE:this.space_.bind(this),"ANNOTATION-XML":this.text_.bind(this),MI:this.identifier_.bind(this),MN:this.number_.bind(this),MO:this.operator_.bind(this),MFENCED:this.fenced_.bind(this),MENCLOSE:this.enclosed_.bind(this),MMULTISCRIPTS:this.multiscripts_.bind(this),ANNOTATION:this.empty_.bind(this),NONE:this.empty_.bind(this),MACTION:this.action_.bind(this)};const e={type:"identifier",role:"numbersetletter",font:"double-struck"};["C","H","N","P","Q","R","Z","\u2102","\u210d","\u2115","\u2119","\u211a","\u211d","\u2124"].forEach((t=>this.getFactory().defaultMap.add(t,e)).bind(this))}static getAttribute_(e,t,n){if(!e.hasAttribute(t))return n;const r=e.getAttribute(t);return r.match(/^\s*$/)?null:r}parse(e){s.default.getInstance().setNodeFactory(this.getFactory());const t=r.toArray(e.childNodes),n=r.tagName(e),i=this.parseMap_[n],o=(i||this.dummy_.bind(this))(e,t);return a.addAttributes(o,e),-1!==["MATH","MROW","MPADDED","MSTYLE","SEMANTICS"].indexOf(n)||(o.mathml.unshift(e),o.mathmlTree=e),o}semantics_(e,t){return t.length?this.parse(t[0]):this.getFactory().makeEmptyNode()}rows_(e,t){const n=e.getAttribute("semantics");if(n&&n.match("bspr_"))return s.default.proof(e,n,this.parseList.bind(this));let r;return 1===(t=a.purgeNodes(t)).length?(r=this.parse(t[0]),"empty"!==r.type||r.mathmlTree||(r.mathmlTree=e)):r=s.default.getInstance().row(this.parseList(t)),r.mathml.unshift(e),r}fraction_(e,t){if(!t.length)return this.getFactory().makeEmptyNode();const n=this.parse(t[0]),r=t[1]?this.parse(t[1]):this.getFactory().makeEmptyNode();return s.default.getInstance().fractionLikeNode(n,r,e.getAttribute("linethickness"),"true"===e.getAttribute("bevelled"))}limits_(e,t){return s.default.getInstance().limitNode(r.tagName(e),this.parseList(t))}root_(e,t){return t[1]?this.getFactory().makeBranchNode("root",[this.parse(t[1]),this.parse(t[0])],[]):this.sqrt_(e,t)}sqrt_(e,t){const n=this.parseList(a.purgeNodes(t));return this.getFactory().makeBranchNode("sqrt",[s.default.getInstance().row(n)],[])}table_(e,t){const n=e.getAttribute("semantics");if(n&&n.match("bspr_"))return s.default.proof(e,n,this.parseList.bind(this));const r=this.getFactory().makeBranchNode("table",this.parseList(t),[]);return r.mathmlTree=e,s.default.tableToMultiline(r),r}tableRow_(e,t){const n=this.getFactory().makeBranchNode("row",this.parseList(t),[]);return n.role="table",n}tableLabeledRow_(e,t){if(!t.length)return this.tableRow_(e,t);const n=this.parse(t[0]);n.role="label";const r=this.getFactory().makeBranchNode("row",this.parseList(t.slice(1)),[n]);return r.role="table",r}tableCell_(e,t){const n=this.parseList(a.purgeNodes(t));let r;r=n.length?1===n.length&&o.isType(n[0],"empty")?n:[s.default.getInstance().row(n)]:[];const i=this.getFactory().makeBranchNode("cell",r,[]);return i.role="table",i}space_(e,t){const n=e.getAttribute("width"),i=n&&n.match(/[a-z]*$/);if(!i)return this.empty_(e,t);const o=i[0],a=parseFloat(n.slice(0,i.index)),c={cm:.4,pc:.5,em:.5,ex:1,in:.15,pt:5,mm:5}[o];if(!c||isNaN(a)||a<c)return this.empty_(e,t);const l=this.getFactory().makeUnprocessed(e);return s.default.getInstance().text(l,r.tagName(e))}text_(e,t){const n=this.leaf_(e,t);return e.textContent?(n.updateContent(e.textContent,!0),s.default.getInstance().text(n,r.tagName(e))):n}identifier_(e,t){const n=this.leaf_(e,t);return s.default.getInstance().identifierNode(n,s.default.getInstance().font(e.getAttribute("mathvariant")),e.getAttribute("class"))}number_(e,t){const n=this.leaf_(e,t);return s.default.number(n),n}operator_(e,t){const n=this.leaf_(e,t);return s.default.getInstance().operatorNode(n),n}fenced_(e,t){const n=this.parseList(a.purgeNodes(t)),r=c.getAttribute_(e,"separators",","),i=c.getAttribute_(e,"open","("),o=c.getAttribute_(e,"close",")"),l=s.default.getInstance().mfenced(i,o,r,n);return s.default.getInstance().tablesInRow([l])[0]}enclosed_(e,t){const n=this.parseList(a.purgeNodes(t)),r=this.getFactory().makeBranchNode("enclose",[s.default.getInstance().row(n)],[]);return r.role=e.getAttribute("notation")||"unknown",r}multiscripts_(e,t){if(!t.length)return this.getFactory().makeEmptyNode();const n=this.parse(t.shift());if(!t.length)return n;const i=[],o=[],c=[],l=[];let u=!1,d=0;for(let e,n=0;e=t[n];n++)"MPRESCRIPTS"!==r.tagName(e)?(u?1&d?i.push(e):o.push(e):1&d?c.push(e):l.push(e),d++):(u=!0,d=0);return a.purgeNodes(i).length||a.purgeNodes(o).length?s.default.getInstance().tensor(n,this.parseList(o),this.parseList(i),this.parseList(l),this.parseList(c)):s.default.getInstance().pseudoTensor(n,this.parseList(l),this.parseList(c))}empty_(e,t){return this.getFactory().makeEmptyNode()}action_(e,t){return t.length>1?this.parse(t[1]):this.getFactory().makeUnprocessed(e)}dummy_(e,t){const n=this.getFactory().makeUnprocessed(e);return n.role=e.tagName,n.textContent=e.textContent,n}leaf_(e,t){if(1===t.length&&t[0].nodeType!==r.NodeType.TEXT_NODE){const n=this.getFactory().makeUnprocessed(e);return n.role=t[0].tagName,a.addAttributes(n,t[0]),n}return this.getFactory().makeLeafNode(e.textContent,s.default.getInstance().font(e.getAttribute("mathvariant")))}}t.SemanticMathml=c},9444:function(e,t,n){Object.defineProperty(t,"__esModule",{value:!0}),t.SemanticNode=void 0;const r=n(6671),i=n(4020),o=n(8901);class s{constructor(e){this.id=e,this.mathml=[],this.parent=null,this.type="unknown",this.role="unknown",this.font="unknown",this.embellished=null,this.fencePointer="",this.childNodes=[],this.textContent="",this.mathmlTree=null,this.contentNodes=[],this.annotation={},this.attributes={},this.nobreaking=!1}static fromXml(e){const t=parseInt(e.getAttribute("id"),10),n=new s(t);return n.type=e.tagName,s.setAttribute(n,e,"role"),s.setAttribute(n,e,"font"),s.setAttribute(n,e,"embellished"),s.setAttribute(n,e,"fencepointer","fencePointer"),e.getAttribute("annotation")&&n.parseAnnotation(e.getAttribute("annotation")),o.addAttributes(n,e),s.processChildren(n,e),n}static setAttribute(e,t,n,r){r=r||n;const i=t.getAttribute(n);i&&(e[r]=i)}static processChildren(e,t){for(const n of r.toArray(t.childNodes)){if(n.nodeType===r.NodeType.TEXT_NODE){e.textContent=n.textContent;continue}const t=r.toArray(n.childNodes).map(s.fromXml);t.forEach((t=>t.parent=e)),"CONTENT"===r.tagName(n)?e.contentNodes=t:e.childNodes=t}}querySelectorAll(e){let t=[];for(let n,r=0;n=this.childNodes[r];r++)t=t.concat(n.querySelectorAll(e));for(let n,r=0;n=this.contentNodes[r];r++)t=t.concat(n.querySelectorAll(e));return e(this)&&t.unshift(this),t}xml(e,t){const n=function(n,r){const i=r.map((function(n){return n.xml(e,t)})),o=e.createElementNS("",n);for(let e,t=0;e=i[t];t++)o.appendChild(e);return o},r=e.createElementNS("",this.type);return t||this.xmlAttributes(r),r.textContent=this.textContent,this.contentNodes.length>0&&r.appendChild(n("content",this.contentNodes)),this.childNodes.length>0&&r.appendChild(n("children",this.childNodes)),r}toString(e=!1){const t=r.parseInput("<snode/>");return r.serializeXml(this.xml(t,e))}allAttributes(){const e=[];return e.push(["role",this.role]),"unknown"!==this.font&&e.push(["font",this.font]),Object.keys(this.annotation).length&&e.push(["annotation",this.xmlAnnotation()]),this.embellished&&e.push(["embellished",this.embellished]),this.fencePointer&&e.push(["fencepointer",this.fencePointer]),e.push(["id",this.id.toString()]),e}xmlAnnotation(){const e=[];for(const t in this.annotation)this.annotation[t].forEach((function(n){e.push(t+":"+n)}));return e.join(";")}toJson(){const e={};e.type=this.type;const t=this.allAttributes();for(let n,r=0;n=t[r];r++)e[n[0]]=n[1].toString();return this.textContent&&(e.$t=this.textContent),this.childNodes.length&&(e.children=this.childNodes.map((function(e){return e.toJson()}))),this.contentNodes.length&&(e.content=this.contentNodes.map((function(e){return e.toJson()}))),e}updateContent(e,t){const n=t?e.replace(/^[ \f\n\r\t\v\u200b]*/,"").replace(/[ \f\n\r\t\v\u200b]*$/,""):e.trim();if(e=e&&!n?e:n,this.textContent===e)return;const r=(0,i.lookupMeaning)(e);this.textContent=e,this.role=r.role,this.type=r.type,this.font=r.font}addMathmlNodes(e){for(let t,n=0;t=e[n];n++)-1===this.mathml.indexOf(t)&&this.mathml.push(t)}appendChild(e){this.childNodes.push(e),this.addMathmlNodes(e.mathml),e.parent=this}replaceChild(e,t){const n=this.childNodes.indexOf(e);if(-1===n)return;e.parent=null,t.parent=this,this.childNodes[n]=t;const r=e.mathml.filter((function(e){return-1===t.mathml.indexOf(e)})),i=t.mathml.filter((function(t){return-1===e.mathml.indexOf(t)}));this.removeMathmlNodes(r),this.addMathmlNodes(i)}appendContentNode(e){e&&(this.contentNodes.push(e),this.addMathmlNodes(e.mathml),e.parent=this)}removeContentNode(e){if(e){const t=this.contentNodes.indexOf(e);-1!==t&&this.contentNodes.slice(t,1)}}equals(e){if(!e)return!1;if(this.type!==e.type||this.role!==e.role||this.textContent!==e.textContent||this.childNodes.length!==e.childNodes.length||this.contentNodes.length!==e.contentNodes.length)return!1;for(let t,n,r=0;t=this.childNodes[r],n=e.childNodes[r];r++)if(!t.equals(n))return!1;for(let t,n,r=0;t=this.contentNodes[r],n=e.contentNodes[r];r++)if(!t.equals(n))return!1;return!0}displayTree(){console.info(this.displayTree_(0))}addAnnotation(e,t){t&&this.addAnnotation_(e,t)}getAnnotation(e){const t=this.annotation[e];return t||[]}hasAnnotation(e,t){const n=this.annotation[e];return!!n&&-1!==n.indexOf(t)}parseAnnotation(e){const t=e.split(";");for(let e=0,n=t.length;e<n;e++){const n=t[e].split(":");this.addAnnotation(n[0],n[1])}}meaning(){return{type:this.type,role:this.role,font:this.font}}xmlAttributes(e){const t=this.allAttributes();for(let n,r=0;n=t[r];r++)e.setAttribute(n[0],n[1]);this.addExternalAttributes(e)}addExternalAttributes(e){for(const t in this.attributes)e.setAttribute(t,this.attributes[t])}removeMathmlNodes(e){const t=this.mathml;for(let n,r=0;n=e[r];r++){const e=t.indexOf(n);-1!==e&&t.splice(e,1)}this.mathml=t}displayTree_(e){e++;const t=Array(e).join("  ");let n="";n+="\n"+t+this.toString(),n+="\n"+t+"MathmlTree:",n+="\n"+t+this.mathmlTreeString(),n+="\n"+t+"MathML:";for(let e,r=0;e=this.mathml[r];r++)n+="\n"+t+e.toString();return n+="\n"+t+"Begin Content",this.contentNodes.forEach((function(t){n+=t.displayTree_(e)})),n+="\n"+t+"End Content",n+="\n"+t+"Begin Children",this.childNodes.forEach((function(t){n+=t.displayTree_(e)})),n+="\n"+t+"End Children",n}mathmlTreeString(){return this.mathmlTree?this.mathmlTree.toString():"EMPTY"}addAnnotation_(e,t){const n=this.annotation[e];n?n.push(t):this.annotation[e]=[t]}}t.SemanticNode=s},4790:function(e,t,n){Object.defineProperty(t,"__esModule",{value:!0}),t.SemanticNodeFactory=void 0;const r=n(7405),i=n(7405),o=n(9444);t.SemanticNodeFactory=class{constructor(){this.leafMap=new i.SemanticNodeCollator,this.defaultMap=new r.SemanticDefault,this.idCounter_=-1}makeNode(e){return this.createNode_(e)}makeUnprocessed(e){const t=this.createNode_();return t.mathml=[e],t.mathmlTree=e,t}makeEmptyNode(){const e=this.createNode_();return e.type="empty",e}makeContentNode(e){const t=this.createNode_();return t.updateContent(e),t}makeMultipleContentNodes(e,t){const n=[];for(let r=0;r<e;r++)n.push(this.makeContentNode(t));return n}makeLeafNode(e,t){if(!e)return this.makeEmptyNode();const n=this.makeContentNode(e);n.font=t||n.font;const r=this.defaultMap.retrieveNode(n);return r&&(n.type=r.type,n.role=r.role,n.font=r.font),this.leafMap.addNode(n),n}makeBranchNode(e,t,n,r){const i=this.createNode_();return r&&i.updateContent(r),i.type=e,i.childNodes=t,i.contentNodes=n,t.concat(n).forEach((function(e){e.parent=i,i.addMathmlNodes(e.mathml)})),i}createNode_(e){return void 0!==e?this.idCounter_=Math.max(this.idCounter_,e):e=++this.idCounter_,new o.SemanticNode(e)}}},178:function(e,t){Object.defineProperty(t,"__esModule",{value:!0}),t.SemanticComparator=t.reduce=t.sort=t.apply=t.add=void 0;const n=[];function r(e){n.push(e)}function i(e,t){for(let r,i=0;r=n[i];i++){const n=r.compare(e,t);if(0!==n)return n}return 0}function o(e){e.sort(i)}t.add=r,t.apply=i,t.sort=o,t.reduce=function(e){if(e.length<=1)return e;const t=e.slice();o(t);const n=[];let r;do{r=t.pop(),n.push(r)}while(r&&t.length&&0===i(t[t.length-1],r));return n};class s{constructor(e,t=null){this.comparator=e,this.type=t,r(this)}compare(e,t){return this.type&&this.type===e.type&&this.type===t.type?this.comparator(e,t):0}}t.SemanticComparator=s,new s((function(e,t){return"simple function"===e.role?1:"simple function"===t.role?-1:0}),"identifier")},6098:function(e,t,n){Object.defineProperty(t,"__esModule",{value:!0}),t.SemanticAbstractParser=void 0;const r=n(4790);t.SemanticAbstractParser=class{constructor(e){this.type=e,this.factory_=new r.SemanticNodeFactory}getFactory(){return this.factory_}setFactory(e){this.factory_=e}getType(){return this.type}parseList(e){const t=[];for(let n,r=0;n=e[r];r++)t.push(this.parse(n));return t}}},6161:function(e,t,n){Object.defineProperty(t,"__esModule",{value:!0}),t.isMembership=t.elligibleRightNeutral=t.elligibleLeftNeutral=t.compareNeutralFences=t.isNeutralFence=t.isImplicitOp=t.isImplicit=t.isPureUnit=t.isUnitCounter=t.isNumber=t.isSingletonSetContent=t.scriptedElement_=t.illegalSingleton_=t.isSetNode=t.isRightBrace=t.isLeftBrace=t.isSimpleFunction=t.singlePunctAtPosition=t.isSimpleFunctionHead=t.isLimitBase=t.isBinomial=t.lineIsLabelled=t.tableIsMultiline=t.tableIsCases=t.isFencedElement=t.tableIsMatrixOrVector=t.isTableOrMultiline=t.isElligibleEmbellishedFence=t.isFence=t.isPunctuation=t.isRelation=t.isOperator=t.isEmbellished=t.isGeneralFunctionBoundary=t.isIntegralDxBoundarySingle=t.isIntegralDxBoundary=t.isBigOpBoundary=t.isPrefixFunctionBoundary=t.isSimpleFunctionScope=t.isAccent=t.isRole=t.embellishedType=t.isType=void 0;const r=n(4020),i=n(8901);function o(e,t){return e.type===t}function s(e,t){return e.embellished===t}function a(e,t){return e.role===t}function c(e){return u(e)||d(e)}function l(e){return o(e,"operator")||s(e,"operator")}function u(e){return o(e,"relation")||s(e,"relation")}function d(e){return o(e,"punctuation")||s(e,"punctuation")}function h(e){return o(e,"fence")||s(e,"fence")}function p(e){return!e.embellished||!function(e){return o(e,"tensor")&&(!o(e.childNodes[1],"empty")||!o(e.childNodes[2],"empty"))&&(!o(e.childNodes[3],"empty")||!o(e.childNodes[4],"empty"))}(e)&&((!a(e,"close")||!o(e,"tensor"))&&((!a(e,"open")||!o(e,"subscript")&&!o(e,"superscript"))&&p(e.childNodes[0])))}function f(e){return!!e&&(o(e,"table")||o(e,"multiline"))}function m(e){return!!e&&o(e,"fenced")&&(a(e,"leftright")||N(e))&&1===e.childNodes.length}function g(e){return!!e&&-1!==["{","\ufe5b","\uff5b"].indexOf(e.textContent)}function S(e){return!!e&&-1!==["}","\ufe5c","\uff5d"].indexOf(e.textContent)}function b(e){return"number"===e.type&&("integer"===e.role||"float"===e.role)}function N(e){return"neutral"===e.role||"metric"===e.role}t.isType=o,t.embellishedType=s,t.isRole=a,t.isAccent=function(e){const t=new RegExp("\u221e|\u1ab2");return o(e,"fence")||o(e,"punctuation")||o(e,"operator")&&!e.textContent.match(t)||o(e,"relation")||o(e,"identifier")&&a(e,"unknown")&&!e.textContent.match(r.allLettersRegExp)&&!e.textContent.match(t)},t.isSimpleFunctionScope=function(e){const t=e.childNodes;if(0===t.length)return!0;if(t.length>1)return!1;const n=t[0];if("infixop"===n.type){if("implicit"!==n.role)return!1;if(n.childNodes.some((e=>o(e,"infixop"))))return!1}return!0},t.isPrefixFunctionBoundary=function(e){return l(e)&&!a(e,"division")||o(e,"appl")||c(e)},t.isBigOpBoundary=function(e){return l(e)||c(e)},t.isIntegralDxBoundary=function(e,t){return!!t&&o(t,"identifier")&&r.lookupSecondary("d",e.textContent)},t.isIntegralDxBoundarySingle=function(e){if(o(e,"identifier")){const t=e.textContent[0];return t&&e.textContent[1]&&r.lookupSecondary("d",t)}return!1},t.isGeneralFunctionBoundary=c,t.isEmbellished=function(e){return e.embellished?e.embellished:r.isEmbellishedType(e.type)?e.type:null},t.isOperator=l,t.isRelation=u,t.isPunctuation=d,t.isFence=h,t.isElligibleEmbellishedFence=function(e){return!(!e||!h(e))&&(!e.embellished||p(e))},t.isTableOrMultiline=f,t.tableIsMatrixOrVector=function(e){return!!e&&m(e)&&f(e.childNodes[0])},t.isFencedElement=m,t.tableIsCases=function(e,t){return t.length>0&&a(t[t.length-1],"openfence")},t.tableIsMultiline=function(e){return e.childNodes.every((function(e){return e.childNodes.length<=1}))},t.lineIsLabelled=function(e){return o(e,"line")&&e.contentNodes.length&&a(e.contentNodes[0],"label")},t.isBinomial=function(e){return 2===e.childNodes.length},t.isLimitBase=function e(t){return o(t,"largeop")||o(t,"limboth")||o(t,"limlower")||o(t,"limupper")||o(t,"function")&&a(t,"limit function")||(o(t,"overscore")||o(t,"underscore"))&&e(t.childNodes[0])},t.isSimpleFunctionHead=function(e){return"identifier"===e.type||"latinletter"===e.role||"greekletter"===e.role||"otherletter"===e.role},t.singlePunctAtPosition=function(e,t,n){return 1===t.length&&("punctuation"===e[n].type||"punctuation"===e[n].embellished)&&e[n]===t[0]},t.isSimpleFunction=function(e){return o(e,"identifier")&&a(e,"simple function")},t.isLeftBrace=g,t.isRightBrace=S,t.isSetNode=function(e){return g(e.contentNodes[0])&&S(e.contentNodes[1])},t.illegalSingleton_=["punctuation","punctuated","relseq","multirel","table","multiline","cases","inference"],t.scriptedElement_=["limupper","limlower","limboth","subscript","superscript","underscore","overscore","tensor"],t.isSingletonSetContent=function e(n){const r=n.type;return-1===t.illegalSingleton_.indexOf(r)&&("infixop"!==r||"implicit"===n.role)&&("fenced"===r?"leftright"!==n.role||e(n.childNodes[0]):-1===t.scriptedElement_.indexOf(r)||e(n.childNodes[0]))},t.isNumber=b,t.isUnitCounter=function(e){return b(e)||"vulgar"===e.role||"mixed"===e.role},t.isPureUnit=function(e){const t=e.childNodes;return"unit"===e.role&&(!t.length||"unit"===t[0].role)},t.isImplicit=function(e){return"implicit"===e.role||"unit"===e.role&&!!e.contentNodes.length&&e.contentNodes[0].textContent===r.invisibleTimes()},t.isImplicitOp=function(e){return"infixop"===e.type&&"implicit"===e.role},t.isNeutralFence=N,t.compareNeutralFences=function(e,t){return N(e)&&N(t)&&(0,i.getEmbellishedInner)(e).textContent===(0,i.getEmbellishedInner)(t).textContent},t.elligibleLeftNeutral=function(e){return!!N(e)&&(!e.embellished||"superscript"!==e.type&&"subscript"!==e.type&&("tensor"!==e.type||"empty"===e.childNodes[3].type&&"empty"===e.childNodes[4].type))},t.elligibleRightNeutral=function(e){return!!N(e)&&(!e.embellished||("tensor"!==e.type||"empty"===e.childNodes[1].type&&"empty"===e.childNodes[2].type))},t.isMembership=function(e){return["element","nonelement","reelement","renonelement"].includes(e.role)}},7793:function(e,t,n){Object.defineProperty(t,"__esModule",{value:!0});const r=n(6671),i=n(4020),o=n(2721),s=n(4790),a=n(6161),c=n(8901);class l{constructor(){this.funcAppls={},this.factory_=new s.SemanticNodeFactory,o.updateFactory(this.factory_)}static getInstance(){return l.instance=l.instance||new l,l.instance}static tableToMultiline(e){if(a.tableIsMultiline(e)){e.type="multiline";for(let t,n=0;t=e.childNodes[n];n++)l.rowToLine_(t,"multiline");1===e.childNodes.length&&!a.lineIsLabelled(e.childNodes[0])&&a.isFencedElement(e.childNodes[0].childNodes[0])&&l.tableToMatrixOrVector_(l.rewriteFencedLine_(e)),l.binomialForm_(e),l.classifyMultiline(e)}else l.classifyTable(e)}static number(e){"unknown"!==e.type&&"identifier"!==e.type||(e.type="number"),l.numberRole_(e),l.exprFont_(e)}static classifyMultiline(e){let t=0;const n=e.childNodes.length;let r;for(;t<n&&(!(r=e.childNodes[t])||!r.childNodes.length);)t++;if(t>=n)return;const i=r.childNodes[0].role;"unknown"!==i&&e.childNodes.every((function(e){const t=e.childNodes[0];return!t||t.role===i&&(a.isType(t,"relation")||a.isType(t,"relseq"))}))&&(e.role=i)}static classifyTable(e){const t=l.computeColumns_(e);l.classifyByColumns_(e,t,"equality")||l.classifyByColumns_(e,t,"inequality",["equality"])||l.classifyByColumns_(e,t,"arrow")||l.detectCaleyTable(e)}static detectCaleyTable(e){if(!e.mathmlTree)return!1;const t=e.mathmlTree,n=t.getAttribute("columnlines"),r=t.getAttribute("rowlines");return!(!n||!r)&&(!(!l.cayleySpacing(n)||!l.cayleySpacing(r))&&(e.role="cayley",!0))}static cayleySpacing(e){const t=e.split(" ");return("solid"===t[0]||"dashed"===t[0])&&t.slice(1).every((e=>"none"===e))}static proof(e,t,n){const r=l.separateSemantics(t);return l.getInstance().proof(e,r,n)}static findSemantics(e,t,n){const r=null==n?null:n,i=l.getSemantics(e);return!!i&&(!!i[t]&&(null==r||i[t]===r))}static getSemantics(e){const t=e.getAttribute("semantics");return t?l.separateSemantics(t):null}static removePrefix(e){const[,...t]=e.split("_");return t.join("_")}static separateSemantics(e){const t={};return e.split(";").forEach((function(e){const[n,r]=e.split(":");t[l.removePrefix(n)]=r})),t}static matchSpaces_(e,t){for(let n,r=0;n=t[r];r++){const t=e[r].mathmlTree,i=e[r+1].mathmlTree;if(!t||!i)continue;const o=t.nextSibling;if(!o||o===i)continue;const s=l.getSpacer_(o);s&&(n.mathml.push(s),n.mathmlTree=s,n.role="space")}}static getSpacer_(e){if("MSPACE"===r.tagName(e))return e;for(;c.hasEmptyTag(e)&&1===e.childNodes.length;)if(e=e.childNodes[0],"MSPACE"===r.tagName(e))return e;return null}static fenceToPunct_(e){const t=l.FENCE_TO_PUNCT_[e.role];if(t){for(;e.embellished;)e.embellished="punctuation",a.isRole(e,"subsup")||a.isRole(e,"underover")||(e.role=t),e=e.childNodes[0];e.type="punctuation",e.role=t}}static classifyFunction_(e,t){if("appl"===e.type||"bigop"===e.type||"integral"===e.type)return"";if(t[0]&&t[0].textContent===i.functionApplication()){l.getInstance().funcAppls[e.id]=t.shift();let n="simple function";return o.run("simple2prefix",e),"prefix function"!==e.role&&"limit function"!==e.role||(n=e.role),l.propagateFunctionRole_(e,n),"prefix"}const n=l.CLASSIFY_FUNCTION_[e.role];return n||(a.isSimpleFunctionHead(e)?"simple":"")}static propagateFunctionRole_(e,t){if(e){if("infixop"===e.type)return;a.isRole(e,"subsup")||a.isRole(e,"underover")||(e.role=t),l.propagateFunctionRole_(e.childNodes[0],t)}}static getFunctionOp_(e,t){if(t(e))return e;for(let n,r=0;n=e.childNodes[r];r++){const e=l.getFunctionOp_(n,t);if(e)return e}return null}static tableToMatrixOrVector_(e){const t=e.childNodes[0];a.isType(t,"multiline")?l.tableToVector_(e):l.tableToMatrix_(e),e.contentNodes.forEach(t.appendContentNode.bind(t));for(let e,n=0;e=t.childNodes[n];n++)l.assignRoleToRow_(e,l.getComponentRoles_(t));return t.parent=null,t}static tableToVector_(e){const t=e.childNodes[0];t.type="vector",1!==t.childNodes.length?l.binomialForm_(t):l.tableToSquare_(e)}static binomialForm_(e){a.isBinomial(e)&&(e.role="binomial",e.childNodes[0].role="binomial",e.childNodes[1].role="binomial")}static tableToMatrix_(e){const t=e.childNodes[0];t.type="matrix",t.childNodes&&t.childNodes.length>0&&t.childNodes[0].childNodes&&t.childNodes.length===t.childNodes[0].childNodes.length?l.tableToSquare_(e):t.childNodes&&1===t.childNodes.length&&(t.role="rowvector")}static tableToSquare_(e){const t=e.childNodes[0];a.isNeutralFence(e)?t.role="determinant":t.role="squarematrix"}static getComponentRoles_(e){const t=e.role;return t&&"unknown"!==t?t:e.type.toLowerCase()||"unknown"}static tableToCases_(e,t){for(let t,n=0;t=e.childNodes[n];n++)l.assignRoleToRow_(t,"cases");return e.type="cases",e.appendContentNode(t),a.tableIsMultiline(e)&&l.binomialForm_(e),e}static rewriteFencedLine_(e){const t=e.childNodes[0],n=e.childNodes[0].childNodes[0],r=e.childNodes[0].childNodes[0].childNodes[0];return n.parent=e.parent,e.parent=n,r.parent=t,n.childNodes=[e],t.childNodes=[r],n}static rowToLine_(e,t){const n=t||"unknown";a.isType(e,"row")&&(e.type="line",e.role=n,1===e.childNodes.length&&a.isType(e.childNodes[0],"cell")&&(e.childNodes=e.childNodes[0].childNodes,e.childNodes.forEach((function(t){t.parent=e}))))}static assignRoleToRow_(e,t){a.isType(e,"line")?e.role=t:a.isType(e,"row")&&(e.role=t,e.childNodes.forEach((function(e){a.isType(e,"cell")&&(e.role=t)})))}static nextSeparatorFunction_(e){let t;if(e){if(e.match(/^\s+$/))return null;t=e.replace(/\s/g,"").split("").filter((function(e){return e}))}else t=[","];return function(){return t.length>1?t.shift():t[0]}}static numberRole_(e){if("unknown"!==e.role)return;const t=[...e.textContent].filter((e=>e.match(/[^\s]/))),n=t.map(i.lookupMeaning);if(n.every((function(e){return"number"===e.type&&"integer"===e.role||"punctuation"===e.type&&"comma"===e.role})))return e.role="integer",void("0"===t[0]&&e.addAnnotation("general","basenumber"));n.every((function(e){return"number"===e.type&&"integer"===e.role||"punctuation"===e.type}))?e.role="float":e.role="othernumber"}static exprFont_(e){if("unknown"!==e.font)return;const t=[...e.textContent].map(i.lookupMeaning).reduce((function(e,t){return e&&t.font&&"unknown"!==t.font&&t.font!==e?"unknown"===e?t.font:null:e}),"unknown");t&&(e.font=t)}static purgeFences_(e){const t=e.rel,n=e.comp,r=[],i=[];for(;t.length>0;){const e=t.shift();let o=n.shift();a.isElligibleEmbellishedFence(e)?(r.push(e),i.push(o)):(l.fenceToPunct_(e),o.push(e),o=o.concat(n.shift()),n.unshift(o))}return i.push(n.shift()),{rel:r,comp:i}}static rewriteFencedNode_(e){const t=e.contentNodes[0],n=e.contentNodes[1];let r=l.rewriteFence_(e,t);return e.contentNodes[0]=r.fence,r=l.rewriteFence_(r.node,n),e.contentNodes[1]=r.fence,e.contentNodes[0].parent=e,e.contentNodes[1].parent=e,r.node.parent=null,r.node}static rewriteFence_(e,t){if(!t.embellished)return{node:e,fence:t};const n=t.childNodes[0],r=l.rewriteFence_(e,n);return a.isType(t,"superscript")||a.isType(t,"subscript")||a.isType(t,"tensor")?(a.isRole(t,"subsup")||(t.role=e.role),n!==r.node&&(t.replaceChild(n,r.node),n.parent=e),l.propagateFencePointer_(t,n),{node:t,fence:r.fence}):(t.replaceChild(n,r.fence),t.mathmlTree&&-1===t.mathml.indexOf(t.mathmlTree)&&t.mathml.push(t.mathmlTree),{node:r.node,fence:t})}static propagateFencePointer_(e,t){e.fencePointer=t.fencePointer||t.id.toString(),e.embellished=null}static classifyByColumns_(e,t,n,r){return!!(3===t.length&&l.testColumns_(t,1,(e=>l.isPureRelation_(e,n)))||2===t.length&&(l.testColumns_(t,1,(e=>l.isEndRelation_(e,n)||l.isPureRelation_(e,n)))||l.testColumns_(t,0,(e=>l.isEndRelation_(e,n,!0)||l.isPureRelation_(e,n)))))&&(e.role=n,!0)}static isEndRelation_(e,t,n){const r=n?e.childNodes.length-1:0;return a.isType(e,"relseq")&&a.isRole(e,t)&&a.isType(e.childNodes[r],"empty")}static isPureRelation_(e,t){return a.isType(e,"relation")&&a.isRole(e,t)}static computeColumns_(e){const t=[];for(let n,r=0;n=e.childNodes[r];r++)for(let e,r=0;e=n.childNodes[r];r++){t[r]?t[r].push(e):t[r]=[e]}return t}static testColumns_(e,t,n){const r=e[t];return!!r&&(r.some((function(e){return e.childNodes.length&&n(e.childNodes[0])}))&&r.every((function(e){return!e.childNodes.length||n(e.childNodes[0])})))}setNodeFactory(e){l.getInstance().factory_=e,o.updateFactory(l.getInstance().factory_)}getNodeFactory(){return l.getInstance().factory_}identifierNode(e,t,n){if("MathML-Unit"===n)e.type="identifier",e.role="unit";else if(!t&&1===e.textContent.length&&("integer"===e.role||"latinletter"===e.role||"greekletter"===e.role)&&"normal"===e.font)return e.font="italic",o.run("simpleNamedFunction",e);return"unknown"===e.type&&(e.type="identifier"),l.exprFont_(e),o.run("simpleNamedFunction",e)}implicitNode(e){if(e=l.getInstance().getMixedNumbers_(e),1===(e=l.getInstance().combineUnits_(e)).length)return e[0];const t=l.getInstance().implicitNode_(e);return o.run("combine_juxtaposition",t)}text(e,t){return l.exprFont_(e),e.type="text","MS"===t?(e.role="string",e):"MSPACE"===t||e.textContent.match(/^\s*$/)?(e.role="space",e):e}row(e){return 0===(e=e.filter((function(e){return!a.isType(e,"empty")}))).length?l.getInstance().factory_.makeEmptyNode():(e=l.getInstance().getFencesInRow_(e),e=l.getInstance().tablesInRow(e),e=l.getInstance().getPunctuationInRow_(e),e=l.getInstance().getTextInRow_(e),e=l.getInstance().getFunctionsInRow_(e),l.getInstance().relationsInRow_(e))}limitNode(e,t){if(!t.length)return l.getInstance().factory_.makeEmptyNode();let n,r=t[0],i="unknown";if(!t[1])return r;if(a.isLimitBase(r)){n=l.MML_TO_LIMIT_[e];const o=n.length;if(i=n.type,t=t.slice(0,n.length+1),1===o&&a.isAccent(t[1])||2===o&&a.isAccent(t[1])&&a.isAccent(t[2]))return n=l.MML_TO_BOUNDS_[e],l.getInstance().accentNode_(r,t,n.type,n.length,n.accent);if(2===o){if(a.isAccent(t[1]))return r=l.getInstance().accentNode_(r,[r,t[1]],{MSUBSUP:"subscript",MUNDEROVER:"underscore"}[e],1,!0),t[2]?l.getInstance().makeLimitNode_(r,[r,t[2]],null,"limupper"):r;if(t[2]&&a.isAccent(t[2]))return r=l.getInstance().accentNode_(r,[r,t[2]],{MSUBSUP:"superscript",MUNDEROVER:"overscore"}[e],1,!0),l.getInstance().makeLimitNode_(r,[r,t[1]],null,"limlower");t[o]||(i="limlower")}return l.getInstance().makeLimitNode_(r,t,null,i)}return n=l.MML_TO_BOUNDS_[e],l.getInstance().accentNode_(r,t,n.type,n.length,n.accent)}tablesInRow(e){let t=c.partitionNodes(e,a.tableIsMatrixOrVector),n=[];for(let e,r=0;e=t.rel[r];r++)n=n.concat(t.comp.shift()),n.push(l.tableToMatrixOrVector_(e));n=n.concat(t.comp.shift()),t=c.partitionNodes(n,a.isTableOrMultiline),n=[];for(let e,r=0;e=t.rel[r];r++){const r=t.comp.shift();a.tableIsCases(e,r)&&l.tableToCases_(e,r.pop()),n=n.concat(r),n.push(e)}return n.concat(t.comp.shift())}mfenced(e,t,n,r){if(n&&r.length>0){const e=l.nextSeparatorFunction_(n),t=[r.shift()];r.forEach((n=>{t.push(l.getInstance().factory_.makeContentNode(e())),t.push(n)})),r=t}return e&&t?l.getInstance().horizontalFencedNode_(l.getInstance().factory_.makeContentNode(e),l.getInstance().factory_.makeContentNode(t),r):(e&&r.unshift(l.getInstance().factory_.makeContentNode(e)),t&&r.push(l.getInstance().factory_.makeContentNode(t)),l.getInstance().row(r))}fractionLikeNode(e,t,n,r){let i;if(!r&&c.isZeroLength(n)){const n=l.getInstance().factory_.makeBranchNode("line",[e],[]),r=l.getInstance().factory_.makeBranchNode("line",[t],[]);return i=l.getInstance().factory_.makeBranchNode("multiline",[n,r],[]),l.binomialForm_(i),l.classifyMultiline(i),i}return i=l.getInstance().fractionNode_(e,t),r&&i.addAnnotation("general","bevelled"),i}tensor(e,t,n,r,i){const o=l.getInstance().factory_.makeBranchNode("tensor",[e,l.getInstance().scriptNode_(t,"leftsub"),l.getInstance().scriptNode_(n,"leftsuper"),l.getInstance().scriptNode_(r,"rightsub"),l.getInstance().scriptNode_(i,"rightsuper")],[]);return o.role=e.role,o.embellished=a.isEmbellished(e),o}pseudoTensor(e,t,n){const r=e=>!a.isType(e,"empty"),i=t.filter(r).length,o=n.filter(r).length;if(!i&&!o)return e;const s=i?o?"MSUBSUP":"MSUB":"MSUP",c=[e];return i&&c.push(l.getInstance().scriptNode_(t,"rightsub",!0)),o&&c.push(l.getInstance().scriptNode_(n,"rightsuper",!0)),l.getInstance().limitNode(s,c)}font(e){const t=l.MATHJAX_FONTS[e];return t||e}proof(e,t,n){if(t.inference||t.axiom||console.log("Noise"),t.axiom){const t=l.getInstance().cleanInference(e.childNodes),r=t.length?l.getInstance().factory_.makeBranchNode("inference",n(t),[]):l.getInstance().factory_.makeEmptyNode();return r.role="axiom",r.mathmlTree=e,r}const r=l.getInstance().inference(e,t,n);return t.proof&&(r.role="proof",r.childNodes[0].role="final"),r}inference(e,t,n){if(t.inferenceRule){const t=l.getInstance().getFormulas(e,[],n);return l.getInstance().factory_.makeBranchNode("inference",[t.conclusion,t.premises],[])}const i=t.labelledRule,o=r.toArray(e.childNodes),s=[];"left"!==i&&"both"!==i||s.push(l.getInstance().getLabel(e,o,n,"left")),"right"!==i&&"both"!==i||s.push(l.getInstance().getLabel(e,o,n,"right"));const a=l.getInstance().getFormulas(e,o,n),c=l.getInstance().factory_.makeBranchNode("inference",[a.conclusion,a.premises],s);return c.mathmlTree=e,c}getLabel(e,t,n,i){const o=l.getInstance().findNestedRow(t,"prooflabel",i),s=l.getInstance().factory_.makeBranchNode("rulelabel",n(r.toArray(o.childNodes)),[]);return s.role=i,s.mathmlTree=o,s}getFormulas(e,t,n){const i=t.length?l.getInstance().findNestedRow(t,"inferenceRule"):e,o="up"===l.getSemantics(i).inferenceRule,s=o?i.childNodes[1]:i.childNodes[0],a=o?i.childNodes[0]:i.childNodes[1],c=s.childNodes[0].childNodes[0],u=r.toArray(c.childNodes[0].childNodes),d=[];let h=1;for(const e of u)h%2&&d.push(e.childNodes[0]),h++;const p=n(d),f=n(r.toArray(a.childNodes[0].childNodes))[0],m=l.getInstance().factory_.makeBranchNode("premises",p,[]);m.mathmlTree=c;const g=l.getInstance().factory_.makeBranchNode("conclusion",[f],[]);return g.mathmlTree=a.childNodes[0].childNodes[0],{conclusion:g,premises:m}}findNestedRow(e,t,n){return l.getInstance().findNestedRow_(e,t,0,n)}cleanInference(e){return r.toArray(e).filter((function(e){return"MSPACE"!==r.tagName(e)}))}operatorNode(e){return"unknown"===e.type&&(e.type="operator"),o.run("multioperator",e)}implicitNode_(e){const t=l.getInstance().factory_.makeMultipleContentNodes(e.length-1,i.invisibleTimes());l.matchSpaces_(e,t);const n=l.getInstance().infixNode_(e,t[0]);return n.role="implicit",t.forEach((function(e){e.parent=n})),n.contentNodes=t,n}infixNode_(e,t){const n=l.getInstance().factory_.makeBranchNode("infixop",e,[t],c.getEmbellishedInner(t).textContent);return n.role=t.role,o.run("propagateSimpleFunction",n)}explicitMixed_(e){const t=c.partitionNodes(e,(function(e){return e.textContent===i.invisiblePlus()}));if(!t.rel.length)return e;let n=[];for(let e,r=0;e=t.rel[r];r++){const i=t.comp[r],o=t.comp[r+1],s=i.length-1;if(i[s]&&o[0]&&a.isType(i[s],"number")&&!a.isRole(i[s],"mixed")&&a.isType(o[0],"fraction")){const e=l.getInstance().factory_.makeBranchNode("number",[i[s],o[0]],[]);e.role="mixed",n=n.concat(i.slice(0,s)),n.push(e),o.shift()}else n=n.concat(i),n.push(e)}return n.concat(t.comp[t.comp.length-1])}concatNode_(e,t,n){if(0===t.length)return e;const r=t.map((function(e){return c.getEmbellishedInner(e).textContent})).join(" "),i=l.getInstance().factory_.makeBranchNode(n,[e],t,r);return t.length>1&&(i.role="multiop"),i}prefixNode_(e,t){const n=c.partitionNodes(t,(e=>a.isRole(e,"subtraction")));let r=l.getInstance().concatNode_(e,n.comp.pop(),"prefixop");for(1===r.contentNodes.length&&"addition"===r.contentNodes[0].role&&"+"===r.contentNodes[0].textContent&&(r.role="positive");n.rel.length>0;)r=l.getInstance().concatNode_(r,[n.rel.pop()],"prefixop"),r.role="negative",r=l.getInstance().concatNode_(r,n.comp.pop(),"prefixop");return r}postfixNode_(e,t){return t.length?l.getInstance().concatNode_(e,t,"postfixop"):e}combineUnits_(e){const t=c.partitionNodes(e,(function(e){return!a.isRole(e,"unit")}));if(e.length===t.rel.length)return t.rel;const n=[];let r,i;do{const e=t.comp.shift();r=t.rel.shift();let o=null;i=n.pop(),i&&(e.length&&a.isUnitCounter(i)?e.unshift(i):n.push(i)),1===e.length&&(o=e.pop()),e.length>1&&(o=l.getInstance().implicitNode_(e),o.role="unit"),o&&n.push(o),r&&n.push(r)}while(r);return n}getMixedNumbers_(e){const t=c.partitionNodes(e,(function(e){return a.isType(e,"fraction")&&a.isRole(e,"vulgar")}));if(!t.rel.length)return e;let n=[];for(let e,r=0;e=t.rel[r];r++){const i=t.comp[r],o=i.length-1;if(i[o]&&a.isType(i[o],"number")&&(a.isRole(i[o],"integer")||a.isRole(i[o],"float"))){const t=l.getInstance().factory_.makeBranchNode("number",[i[o],e],[]);t.role="mixed",n=n.concat(i.slice(0,o)),n.push(t)}else n=n.concat(i),n.push(e)}return n.concat(t.comp[t.comp.length-1])}getTextInRow_(e){if(e.length<=1)return e;const t=c.partitionNodes(e,(e=>a.isType(e,"text")));if(0===t.rel.length)return e;const n=[];let r=t.comp[0];r.length>0&&n.push(l.getInstance().row(r));for(let e,i=0;e=t.rel[i];i++)n.push(e),r=t.comp[i+1],r.length>0&&n.push(l.getInstance().row(r));return[l.getInstance().dummyNode_(n)]}relationsInRow_(e){const t=c.partitionNodes(e,a.isRelation),n=t.rel[0];if(!n)return l.getInstance().operationsInRow_(e);if(1===e.length)return e[0];const r=t.comp.map(l.getInstance().operationsInRow_);let i;return t.rel.some((function(e){return!e.equals(n)}))?(i=l.getInstance().factory_.makeBranchNode("multirel",r,t.rel),t.rel.every((function(e){return e.role===n.role}))&&(i.role=n.role),i):(i=l.getInstance().factory_.makeBranchNode("relseq",r,t.rel,c.getEmbellishedInner(n).textContent),i.role=n.role,i)}operationsInRow_(e){if(0===e.length)return l.getInstance().factory_.makeEmptyNode();if(1===(e=l.getInstance().explicitMixed_(e)).length)return e[0];const t=[];for(;e.length>0&&a.isOperator(e[0]);)t.push(e.shift());if(0===e.length)return l.getInstance().prefixNode_(t.pop(),t);if(1===e.length)return l.getInstance().prefixNode_(e[0],t);e=o.run("convert_juxtaposition",e);const n=c.sliceNodes(e,a.isOperator),r=l.getInstance().prefixNode_(l.getInstance().implicitNode(n.head),t);return n.div?l.getInstance().operationsTree_(n.tail,r,n.div):r}operationsTree_(e,t,n,r){const i=r||[];if(0===e.length){if(i.unshift(n),"infixop"===t.type){const e=l.getInstance().postfixNode_(t.childNodes.pop(),i);return t.appendChild(e),t}return l.getInstance().postfixNode_(t,i)}const o=c.sliceNodes(e,a.isOperator);if(0===o.head.length)return i.push(o.div),l.getInstance().operationsTree_(o.tail,t,n,i);const s=l.getInstance().prefixNode_(l.getInstance().implicitNode(o.head),i),u=l.getInstance().appendOperand_(t,n,s);return o.div?l.getInstance().operationsTree_(o.tail,u,o.div,[]):u}appendOperand_(e,t,n){if("infixop"!==e.type)return l.getInstance().infixNode_([e,n],t);const r=l.getInstance().appendDivisionOp_(e,t,n);return r||(l.getInstance().appendExistingOperator_(e,t,n)?e:"multiplication"===t.role?l.getInstance().appendMultiplicativeOp_(e,t,n):l.getInstance().appendAdditiveOp_(e,t,n))}appendDivisionOp_(e,t,n){return"division"===t.role?a.isImplicit(e)?l.getInstance().infixNode_([e,n],t):l.getInstance().appendLastOperand_(e,t,n):"division"===e.role?l.getInstance().infixNode_([e,n],t):null}appendLastOperand_(e,t,n){let r=e,i=e.childNodes[e.childNodes.length-1];for(;i&&"infixop"===i.type&&!a.isImplicit(i);)r=i,i=r.childNodes[e.childNodes.length-1];const o=l.getInstance().infixNode_([r.childNodes.pop(),n],t);return r.appendChild(o),e}appendMultiplicativeOp_(e,t,n){if(a.isImplicit(e))return l.getInstance().infixNode_([e,n],t);let r=e,i=e.childNodes[e.childNodes.length-1];for(;i&&"infixop"===i.type&&!a.isImplicit(i);)r=i,i=r.childNodes[e.childNodes.length-1];const o=l.getInstance().infixNode_([r.childNodes.pop(),n],t);return r.appendChild(o),e}appendAdditiveOp_(e,t,n){return l.getInstance().infixNode_([e,n],t)}appendExistingOperator_(e,t,n){return!(!e||"infixop"!==e.type||a.isImplicit(e))&&(e.contentNodes[0].equals(t)?(e.appendContentNode(t),e.appendChild(n),!0):l.getInstance().appendExistingOperator_(e.childNodes[e.childNodes.length-1],t,n))}getFencesInRow_(e){let t=c.partitionNodes(e,a.isFence);t=l.purgeFences_(t);const n=t.comp.shift();return l.getInstance().fences_(t.rel,t.comp,[],[n])}fences_(e,t,n,r){if(0===e.length&&0===n.length)return r[0];const i=e=>a.isRole(e,"open");if(0===e.length){const e=r.shift();for(;n.length>0;){if(i(n[0])){const t=n.shift();l.fenceToPunct_(t),e.push(t)}else{const t=c.sliceNodes(n,i),o=t.head.length-1,s=l.getInstance().neutralFences_(t.head,r.slice(0,o));r=r.slice(o),e.push(...s),t.div&&t.tail.unshift(t.div),n=t.tail}e.push(...r.shift())}return e}const o=n[n.length-1],s=e[0].role;if("open"===s||a.isNeutralFence(e[0])&&(!o||!a.compareNeutralFences(e[0],o))){n.push(e.shift());const i=t.shift();return i&&r.push(i),l.getInstance().fences_(e,t,n,r)}if(o&&"close"===s&&"open"===o.role){const i=l.getInstance().horizontalFencedNode_(n.pop(),e.shift(),r.pop());return r.push(r.pop().concat([i],t.shift())),l.getInstance().fences_(e,t,n,r)}if(o&&a.compareNeutralFences(e[0],o)){if(!a.elligibleLeftNeutral(o)||!a.elligibleRightNeutral(e[0])){n.push(e.shift());const i=t.shift();return i&&r.push(i),l.getInstance().fences_(e,t,n,r)}const i=l.getInstance().horizontalFencedNode_(n.pop(),e.shift(),r.pop());return r.push(r.pop().concat([i],t.shift())),l.getInstance().fences_(e,t,n,r)}if(o&&"close"===s&&a.isNeutralFence(o)&&n.some(i)){const o=c.sliceNodes(n,i,!0),s=r.pop(),a=r.length-o.tail.length+1,u=l.getInstance().neutralFences_(o.tail,r.slice(a));r=r.slice(0,a);const d=l.getInstance().horizontalFencedNode_(o.div,e.shift(),r.pop().concat(u,s));return r.push(r.pop().concat([d],t.shift())),l.getInstance().fences_(e,t,o.head,r)}const u=e.shift();return l.fenceToPunct_(u),r.push(r.pop().concat([u],t.shift())),l.getInstance().fences_(e,t,n,r)}neutralFences_(e,t){if(0===e.length)return e;if(1===e.length)return l.fenceToPunct_(e[0]),e;const n=e.shift();if(!a.elligibleLeftNeutral(n)){l.fenceToPunct_(n);const r=t.shift();return r.unshift(n),r.concat(l.getInstance().neutralFences_(e,t))}const r=c.sliceNodes(e,(function(e){return a.compareNeutralFences(e,n)}));if(!r.div){l.fenceToPunct_(n);const r=t.shift();return r.unshift(n),r.concat(l.getInstance().neutralFences_(e,t))}if(!a.elligibleRightNeutral(r.div))return l.fenceToPunct_(r.div),e.unshift(n),l.getInstance().neutralFences_(e,t);const i=l.getInstance().combineFencedContent_(n,r.div,r.head,t);if(r.tail.length>0){const e=i.shift(),t=l.getInstance().neutralFences_(r.tail,i);return e.concat(t)}return i[0]}combineFencedContent_(e,t,n,r){if(0===n.length){const n=l.getInstance().horizontalFencedNode_(e,t,r.shift());return r.length>0?r[0].unshift(n):r=[[n]],r}const i=r.shift(),o=n.length-1,s=r.slice(0,o),a=(r=r.slice(o)).shift(),c=l.getInstance().neutralFences_(n,s);i.push(...c),i.push(...a);const u=l.getInstance().horizontalFencedNode_(e,t,i);return r.length>0?r[0].unshift(u):r=[[u]],r}horizontalFencedNode_(e,t,n){const r=l.getInstance().row(n);let i=l.getInstance().factory_.makeBranchNode("fenced",[r],[e,t]);return"open"===e.role?(l.getInstance().classifyHorizontalFence_(i),i=o.run("propagateComposedFunction",i)):i.role=e.role,i=o.run("detect_cycle",i),l.rewriteFencedNode_(i)}classifyHorizontalFence_(e){e.role="leftright";const t=e.childNodes;if(!a.isSetNode(e)||t.length>1)return;if(0===t.length||"empty"===t[0].type)return void(e.role="set empty");const n=t[0].type;if(1===t.length&&a.isSingletonSetContent(t[0]))return void(e.role="set singleton");const r=t[0].role;if("punctuated"===n&&"sequence"===r){if("comma"!==t[0].contentNodes[0].role)return 1!==t[0].contentNodes.length||"vbar"!==t[0].contentNodes[0].role&&"colon"!==t[0].contentNodes[0].role?void 0:(e.role="set extended",void l.getInstance().setExtension_(e));e.role="set collection"}}setExtension_(e){const t=e.childNodes[0].childNodes[0];t&&"infixop"===t.type&&1===t.contentNodes.length&&a.isMembership(t.contentNodes[0])&&(t.addAnnotation("set","intensional"),t.contentNodes[0].addAnnotation("set","intensional"))}getPunctuationInRow_(e){if(e.length<=1)return e;const t=e=>{const t=e.type;return"punctuation"===t||"text"===t||"operator"===t||"relation"===t},n=c.partitionNodes(e,(function(n){if(!a.isPunctuation(n))return!1;if(a.isPunctuation(n)&&!a.isRole(n,"ellipsis"))return!0;const r=e.indexOf(n);if(0===r)return!e[1]||!t(e[1]);const i=e[r-1];if(r===e.length-1)return!t(i);const o=e[r+1];return!t(i)||!t(o)}));if(0===n.rel.length)return e;const r=[];let i=n.comp.shift();i.length>0&&r.push(l.getInstance().row(i));let o=0;for(;n.comp.length>0;)r.push(n.rel[o++]),i=n.comp.shift(),i.length>0&&r.push(l.getInstance().row(i));return[l.getInstance().punctuatedNode_(r,n.rel)]}punctuatedNode_(e,t){const n=l.getInstance().factory_.makeBranchNode("punctuated",e,t);if(t.length===e.length){const e=t[0].role;if("unknown"!==e&&t.every((function(t){return t.role===e})))return n.role=e,n}return a.singlePunctAtPosition(e,t,0)?n.role="startpunct":a.singlePunctAtPosition(e,t,e.length-1)?n.role="endpunct":t.every((e=>a.isRole(e,"dummy")))?n.role="text":t.every((e=>a.isRole(e,"space")))?n.role="space":n.role="sequence",n}dummyNode_(e){const t=l.getInstance().factory_.makeMultipleContentNodes(e.length-1,i.invisibleComma());return t.forEach((function(e){e.role="dummy"})),l.getInstance().punctuatedNode_(e,t)}accentRole_(e,t){if(!a.isAccent(e))return!1;const n=e.textContent,r=i.lookupSecondary("bar",n)||i.lookupSecondary("tilde",n)||e.role;return e.role="underscore"===t?"underaccent":"overaccent",e.addAnnotation("accent",r),!0}accentNode_(e,t,n,r,i){const o=(t=t.slice(0,r+1))[1],s=t[2];let a;if(!i&&s&&(a=l.getInstance().factory_.makeBranchNode("subscript",[e,o],[]),a.role="subsup",t=[a,s],n="superscript"),i){const r=l.getInstance().accentRole_(o,n);if(s){l.getInstance().accentRole_(s,"overscore")&&!r?(a=l.getInstance().factory_.makeBranchNode("overscore",[e,s],[]),t=[a,o],n="underscore"):(a=l.getInstance().factory_.makeBranchNode("underscore",[e,o],[]),t=[a,s],n="overscore"),a.role="underover"}}return l.getInstance().makeLimitNode_(e,t,a,n)}makeLimitNode_(e,t,n,r){if("limupper"===r&&"limlower"===e.type)return e.childNodes.push(t[1]),t[1].parent=e,e.type="limboth",e;if("limlower"===r&&"limupper"===e.type)return e.childNodes.splice(1,-1,t[1]),t[1].parent=e,e.type="limboth",e;const i=l.getInstance().factory_.makeBranchNode(r,t,[]),o=a.isEmbellished(e);return n&&(n.embellished=o),i.embellished=o,i.role=e.role,i}getFunctionsInRow_(e,t){const n=t||[];if(0===e.length)return n;const r=e.shift(),i=l.classifyFunction_(r,e);if(!i)return n.push(r),l.getInstance().getFunctionsInRow_(e,n);const o=l.getInstance().getFunctionsInRow_(e,[]),s=l.getInstance().getFunctionArgs_(r,o,i);return n.concat(s)}getFunctionArgs_(e,t,n){let r,i,o;switch(n){case"integral":{const n=l.getInstance().getIntegralArgs_(t);if(!n.intvar&&!n.integrand.length)return n.rest.unshift(e),n.rest;const r=l.getInstance().row(n.integrand);return o=l.getInstance().integralNode_(e,r,n.intvar),n.rest.unshift(o),n.rest}case"prefix":if(t[0]&&"fenced"===t[0].type){const n=t.shift();return a.isNeutralFence(n)||(n.role="leftright"),o=l.getInstance().functionNode_(e,n),t.unshift(o),t}if(r=c.sliceNodes(t,a.isPrefixFunctionBoundary),r.head.length)i=l.getInstance().row(r.head),r.div&&r.tail.unshift(r.div);else{if(!r.div||!a.isType(r.div,"appl"))return t.unshift(e),t;i=r.div}return o=l.getInstance().functionNode_(e,i),r.tail.unshift(o),r.tail;case"bigop":return r=c.sliceNodes(t,a.isBigOpBoundary),r.head.length?(i=l.getInstance().row(r.head),o=l.getInstance().bigOpNode_(e,i),r.div&&r.tail.unshift(r.div),r.tail.unshift(o),r.tail):(t.unshift(e),t);default:{if(0===t.length)return[e];const n=t[0];return"fenced"===n.type&&!a.isNeutralFence(n)&&a.isSimpleFunctionScope(n)?(n.role="leftright",l.propagateFunctionRole_(e,"simple function"),o=l.getInstance().functionNode_(e,t.shift()),t.unshift(o),t):(t.unshift(e),t)}}}getIntegralArgs_(e,t=[]){if(0===e.length)return{integrand:t,intvar:null,rest:e};const n=e[0];if(a.isGeneralFunctionBoundary(n))return{integrand:t,intvar:null,rest:e};if(a.isIntegralDxBoundarySingle(n))return n.role="integral",{integrand:t,intvar:n,rest:e.slice(1)};if(e[1]&&a.isIntegralDxBoundary(n,e[1])){const r=l.getInstance().prefixNode_(e[1],[n]);return r.role="integral",{integrand:t,intvar:r,rest:e.slice(2)}}return t.push(e.shift()),l.getInstance().getIntegralArgs_(e,t)}functionNode_(e,t){const n=l.getInstance().factory_.makeContentNode(i.functionApplication()),r=l.getInstance().funcAppls[e.id];r&&(n.mathmlTree=r.mathmlTree,n.mathml=r.mathml,n.annotation=r.annotation,n.attributes=r.attributes,delete l.getInstance().funcAppls[e.id]),n.type="punctuation",n.role="application";const o=l.getFunctionOp_(e,(function(e){return a.isType(e,"function")||a.isType(e,"identifier")&&a.isRole(e,"simple function")}));return l.getInstance().functionalNode_("appl",[e,t],o,[n])}bigOpNode_(e,t){const n=l.getFunctionOp_(e,(e=>a.isType(e,"largeop")));return l.getInstance().functionalNode_("bigop",[e,t],n,[])}integralNode_(e,t,n){t=t||l.getInstance().factory_.makeEmptyNode(),n=n||l.getInstance().factory_.makeEmptyNode();const r=l.getFunctionOp_(e,(e=>a.isType(e,"largeop")));return l.getInstance().functionalNode_("integral",[e,t,n],r,[])}functionalNode_(e,t,n,r){const i=t[0];let o;n&&(o=n.parent,r.push(n));const s=l.getInstance().factory_.makeBranchNode(e,t,r);return s.role=i.role,o&&(n.parent=o),s}fractionNode_(e,t){const n=l.getInstance().factory_.makeBranchNode("fraction",[e,t],[]);return n.role=n.childNodes.every((function(e){return a.isType(e,"number")&&a.isRole(e,"integer")}))?"vulgar":n.childNodes.every(a.isPureUnit)?"unit":"division",o.run("propagateSimpleFunction",n)}scriptNode_(e,t,n){let r;switch(e.length){case 0:r=l.getInstance().factory_.makeEmptyNode();break;case 1:if(r=e[0],n)return r;break;default:r=l.getInstance().dummyNode_(e)}return r.role=t,r}findNestedRow_(e,t,n,i){if(n>3)return null;for(let o,s=0;o=e[s];s++){const e=r.tagName(o);if("MSPACE"!==e){if("MROW"===e)return l.getInstance().findNestedRow_(r.toArray(o.childNodes),t,n+1,i);if(l.findSemantics(o,t,i))return o}}return null}}t.default=l,l.FENCE_TO_PUNCT_={metric:"metric",neutral:"vbar",open:"openfence",close:"closefence"},l.MML_TO_LIMIT_={MSUB:{type:"limlower",length:1},MUNDER:{type:"limlower",length:1},MSUP:{type:"limupper",length:1},MOVER:{type:"limupper",length:1},MSUBSUP:{type:"limboth",length:2},MUNDEROVER:{type:"limboth",length:2}},l.MML_TO_BOUNDS_={MSUB:{type:"subscript",length:1,accent:!1},MSUP:{type:"superscript",length:1,accent:!1},MSUBSUP:{type:"subscript",length:2,accent:!1},MUNDER:{type:"underscore",length:1,accent:!0},MOVER:{type:"overscore",length:1,accent:!0},MUNDEROVER:{type:"underscore",length:2,accent:!0}},l.CLASSIFY_FUNCTION_={integral:"integral",sum:"bigop","prefix function":"prefix","limit function":"prefix","simple function":"prefix","composed function":"prefix"},l.MATHJAX_FONTS={"-tex-caligraphic":"caligraphic","-tex-caligraphic-bold":"caligraphic-bold","-tex-calligraphic":"caligraphic","-tex-calligraphic-bold":"caligraphic-bold","-tex-oldstyle":"oldstyle","-tex-oldstyle-bold":"oldstyle-bold","-tex-mathit":"italic"}},7984:function(e,t,n){Object.defineProperty(t,"__esModule",{value:!0}),t.SemanticSkeleton=void 0;const r=n(1426),i=n(5024),o=n(8171);class s{constructor(e){this.parents=null,this.levelsMap=null,e=0===e?e:e||[],this.array=e}static fromTree(e){return s.fromNode(e.root)}static fromNode(e){return new s(s.fromNode_(e))}static fromString(e){return new s(s.fromString_(e))}static simpleCollapseStructure(e){return"number"==typeof e}static contentCollapseStructure(e){return!!e&&!s.simpleCollapseStructure(e)&&"c"===e[0]}static interleaveIds(e,t){return r.interleaveLists(s.collapsedLeafs(e),s.collapsedLeafs(t))}static collapsedLeafs(...e){return e.reduce(((e,t)=>{return e.concat((n=t,s.simpleCollapseStructure(n)?[n]:(n=n,s.contentCollapseStructure(n[1])?n.slice(2):n.slice(1))));var n}),[])}static fromStructure(e,t){return new s(s.tree_(e,t.root))}static combineContentChildren(e,t,n){switch(e.type){case"relseq":case"infixop":case"multirel":return r.interleaveLists(n,t);case"prefixop":return t.concat(n);case"postfixop":return n.concat(t);case"fenced":return n.unshift(t[0]),n.push(t[1]),n;case"appl":return[n[0],t[0],n[1]];case"root":return[n[1],n[0]];case"row":case"line":return t.length&&n.unshift(t[0]),n;default:return n}}static makeSexp_(e){return s.simpleCollapseStructure(e)?e.toString():s.contentCollapseStructure(e)?"(c "+e.slice(1).map(s.makeSexp_).join(" ")+")":"("+e.map(s.makeSexp_).join(" ")+")"}static fromString_(e){let t=e.replace(/\(/g,"[");return t=t.replace(/\)/g,"]"),t=t.replace(/ /g,","),t=t.replace(/c/g,'"c"'),JSON.parse(t)}static fromNode_(e){if(!e)return[];const t=e.contentNodes;let n;t.length&&(n=t.map(s.fromNode_),n.unshift("c"));const r=e.childNodes;if(!r.length)return t.length?[e.id,n]:e.id;const i=r.map(s.fromNode_);return t.length&&i.unshift(n),i.unshift(e.id),i}static tree_(e,t){if(!t)return[];if(!t.childNodes.length)return t.id;const n=t.id,r=[n],a=i.evalXPath(`.//self::*[@${o.Attribute.ID}=${n}]`,e)[0],c=s.combineContentChildren(t,t.contentNodes.map((function(e){return e})),t.childNodes.map((function(e){return e})));a&&s.addOwns_(a,c);for(let t,n=0;t=c[n];n++)r.push(s.tree_(e,t));return r}static addOwns_(e,t){const n=e.getAttribute(o.Attribute.COLLAPSED),r=n?s.realLeafs_(s.fromString(n).array):t.map((e=>e.id));e.setAttribute(o.Attribute.OWNS,r.join(" "))}static realLeafs_(e){if(s.simpleCollapseStructure(e))return[e];if(s.contentCollapseStructure(e))return[];e=e;let t=[];for(let n=1;n<e.length;n++)t=t.concat(s.realLeafs_(e[n]));return t}populate(){this.parents&&this.levelsMap||(this.parents={},this.levelsMap={},this.populate_(this.array,this.array,[]))}toString(){return s.makeSexp_(this.array)}populate_(e,t,n){if(s.simpleCollapseStructure(e))return e=e,this.levelsMap[e]=t,void(this.parents[e]=e===n[0]?n.slice(1):n);const r=s.contentCollapseStructure(e)?e.slice(1):e,i=[r[0]].concat(n);for(let t=0,n=r.length;t<n;t++){const n=r[t];this.populate_(n,e,i)}}isRoot(e){return e===this.levelsMap[e][0]}directChildren(e){if(!this.isRoot(e))return[];return this.levelsMap[e].slice(1).map((e=>s.simpleCollapseStructure(e)?e:s.contentCollapseStructure(e)?e[1]:e[0]))}subtreeNodes(e){if(!this.isRoot(e))return[];const t=(e,n)=>{s.simpleCollapseStructure(e)?n.push(e):(e=e,s.contentCollapseStructure(e)&&(e=e.slice(1)),e.forEach((e=>t(e,n))))},n=this.levelsMap[e],r=[];return t(n.slice(1),r),r}}t.SemanticSkeleton=s},1784:function(e,t,n){Object.defineProperty(t,"__esModule",{value:!0}),t.SemanticTree=void 0;const r=n(6671),i=n(4036),o=n(241),s=n(8122),a=n(9444),c=n(6161);n(7103);class l{constructor(e){this.mathml=e,this.parser=new s.SemanticMathml,this.root=this.parser.parse(e),this.collator=this.parser.getFactory().leafMap.collateMeaning();const t=this.collator.newDefault();t&&(this.parser=new s.SemanticMathml,this.parser.getFactory().defaultMap=t,this.root=this.parser.parse(e)),u.visit(this.root,{}),(0,i.annotate)(this.root)}static empty(){const e=r.parseInput("<math/>"),t=new l(e);return t.mathml=e,t}static fromNode(e,t){const n=l.empty();return n.root=e,t&&(n.mathml=t),n}static fromRoot(e,t){let n=e;for(;n.parent;)n=n.parent;const r=l.fromNode(n);return t&&(r.mathml=t),r}static fromXml(e){const t=l.empty();return e.childNodes[0]&&(t.root=a.SemanticNode.fromXml(e.childNodes[0])),t}xml(e){const t=r.parseInput("<stree></stree>"),n=this.root.xml(t.ownerDocument,e);return t.appendChild(n),t}toString(e){return r.serializeXml(this.xml(e))}formatXml(e){const t=this.toString(e);return r.formatXml(t)}displayTree(){this.root.displayTree()}replaceNode(e,t){const n=e.parent;n?n.replaceChild(e,t):this.root=t}toJson(){const e={};return e.stree=this.root.toJson(),e}}t.SemanticTree=l;const u=new o.SemanticVisitor("general","unit",((e,t)=>{if("infixop"===e.type&&("multiplication"===e.role||"implicit"===e.role)){const t=e.childNodes;t.length&&(c.isPureUnit(t[0])||c.isUnitCounter(t[0]))&&e.childNodes.slice(1).every(c.isPureUnit)&&(e.role="unit")}return!1}))},8901:function(e,t,n){Object.defineProperty(t,"__esModule",{value:!0}),t.partitionNodes=t.sliceNodes=t.getEmbellishedInner=t.addAttributes=t.isZeroLength=t.purgeNodes=t.isOrphanedGlyph=t.hasDisplayTag=t.hasEmptyTag=t.hasIgnoreTag=t.hasLeafTag=t.hasMathTag=t.directSpeechKeys=t.DISPLAYTAGS=t.EMPTYTAGS=t.IGNORETAGS=t.LEAFTAGS=void 0;const r=n(6671);function i(e){return!!e&&-1!==t.LEAFTAGS.indexOf(r.tagName(e))}function o(e,t,n){n&&e.reverse();const r=[];for(let i,o=0;i=e[o];o++){if(t(i))return n?{head:e.slice(o+1).reverse(),div:i,tail:r.reverse()}:{head:r,div:i,tail:e.slice(o+1)};r.push(i)}return n?{head:[],div:null,tail:r.reverse()}:{head:r,div:null,tail:[]}}t.LEAFTAGS=["MO","MI","MN","MTEXT","MS","MSPACE"],t.IGNORETAGS=["MERROR","MPHANTOM","MALIGNGROUP","MALIGNMARK","MPRESCRIPTS","ANNOTATION","ANNOTATION-XML"],t.EMPTYTAGS=["MATH","MROW","MPADDED","MACTION","NONE","MSTYLE","SEMANTICS"],t.DISPLAYTAGS=["MROOT","MSQRT"],t.directSpeechKeys=["aria-label","exact-speech","alt"],t.hasMathTag=function(e){return!!e&&"MATH"===r.tagName(e)},t.hasLeafTag=i,t.hasIgnoreTag=function(e){return!!e&&-1!==t.IGNORETAGS.indexOf(r.tagName(e))},t.hasEmptyTag=function(e){return!!e&&-1!==t.EMPTYTAGS.indexOf(r.tagName(e))},t.hasDisplayTag=function(e){return!!e&&-1!==t.DISPLAYTAGS.indexOf(r.tagName(e))},t.isOrphanedGlyph=function(e){return!!e&&"MGLYPH"===r.tagName(e)&&!i(e.parentNode)},t.purgeNodes=function(e){const n=[];for(let i,o=0;i=e[o];o++){if(i.nodeType!==r.NodeType.ELEMENT_NODE)continue;const e=r.tagName(i);-1===t.IGNORETAGS.indexOf(e)&&(-1!==t.EMPTYTAGS.indexOf(e)&&0===i.childNodes.length||n.push(i))}return n},t.isZeroLength=function(e){if(!e)return!1;if(-1!==["negativeveryverythinmathspace","negativeverythinmathspace","negativethinmathspace","negativemediummathspace","negativethickmathspace","negativeverythickmathspace","negativeveryverythickmathspace"].indexOf(e))return!0;const t=e.match(/[0-9.]+/);return!!t&&0===parseFloat(t[0])},t.addAttributes=function(e,n){if(n.hasAttributes()){const r=n.attributes;for(let n=r.length-1;n>=0;n--){const i=r[n].name;i.match(/^ext/)&&(e.attributes[i]=r[n].value,e.nobreaking=!0),-1!==t.directSpeechKeys.indexOf(i)&&(e.attributes["ext-speech"]=r[n].value,e.nobreaking=!0),i.match(/texclass$/)&&(e.attributes.texclass=r[n].value),"href"===i&&(e.attributes.href=r[n].value,e.nobreaking=!0)}}},t.getEmbellishedInner=function e(t){return t&&t.embellished&&t.childNodes.length>0?e(t.childNodes[0]):t},t.sliceNodes=o,t.partitionNodes=function(e,t){let n=e;const r=[],i=[];let s=null;do{s=o(n,t),i.push(s.head),r.push(s.div),n=s.tail}while(s.div);return r.pop(),{rel:r,comp:i}}},9135:function(e,t,n){Object.defineProperty(t,"__esModule",{value:!0}),t.AbstractSpeechGenerator=void 0;const r=n(985),i=n(8171),o=n(1848),s=n(144);t.AbstractSpeechGenerator=class{constructor(){this.modality=i.addPrefix("speech"),this.rebuilt_=null,this.options_={}}getRebuilt(){return this.rebuilt_}setRebuilt(e){this.rebuilt_=e}setOptions(e){this.options_=e||{},this.modality=i.addPrefix(this.options_.modality||"speech")}getOptions(){return this.options_}start(){}end(){}generateSpeech(e,t){return this.rebuilt_||(this.rebuilt_=new o.RebuildStree(t)),(0,r.setup)(this.options_),s.computeMarkup(this.getRebuilt().xml)}}},3153:function(e,t,n){Object.defineProperty(t,"__esModule",{value:!0}),t.AdhocSpeechGenerator=void 0;const r=n(9135);class i extends r.AbstractSpeechGenerator{getSpeech(e,t){const n=this.generateSpeech(e,t);return e.setAttribute(this.modality,n),n}}t.AdhocSpeechGenerator=i},6281:function(e,t,n){Object.defineProperty(t,"__esModule",{value:!0}),t.ColorGenerator=void 0;const r=n(8171),i=n(1123),o=n(1848),s=n(8835),a=n(9135);class c extends a.AbstractSpeechGenerator{constructor(){super(...arguments),this.modality=(0,r.addPrefix)("foreground"),this.contrast=new i.ContrastPicker}static visitStree_(e,t,n){if(e.childNodes.length){if(e.contentNodes.length&&("punctuated"===e.type&&e.contentNodes.forEach((e=>n[e.id]=!0)),"implicit"!==e.role&&t.push(e.contentNodes.map((e=>e.id)))),e.childNodes.length){if("implicit"===e.role){const r=[];let i=[];for(const t of e.childNodes){const e=[];c.visitStree_(t,e,n),e.length<=2&&r.push(e.shift()),i=i.concat(e)}return t.push(r),void i.forEach((e=>t.push(e)))}e.childNodes.forEach((e=>c.visitStree_(e,t,n)))}}else n[e.id]||t.push(e.id)}getSpeech(e,t){return s.getAttribute(e,this.modality)}generateSpeech(e,t){return this.getRebuilt()||this.setRebuilt(new o.RebuildStree(e)),this.colorLeaves_(e),s.getAttribute(e,this.modality)}colorLeaves_(e){const t=[];c.visitStree_(this.getRebuilt().streeRoot,t,{});for(const n of t){const t=this.contrast.generate();let r=!1;r=Array.isArray(n)?n.map((n=>this.colorLeave_(e,n,t))).reduce(((e,t)=>e||t),!1):this.colorLeave_(e,n.toString(),t),r&&this.contrast.increment()}}colorLeave_(e,t,n){const r=s.getBySemanticId(e,t);return!!r&&(r.setAttribute(this.modality,n),!0)}}t.ColorGenerator=c},1565:function(e,t,n){Object.defineProperty(t,"__esModule",{value:!0}),t.DirectSpeechGenerator=void 0;const r=n(8835),i=n(9135);class o extends i.AbstractSpeechGenerator{getSpeech(e,t){return r.getAttribute(e,this.modality)}}t.DirectSpeechGenerator=o},7721:function(e,t,n){Object.defineProperty(t,"__esModule",{value:!0}),t.DummySpeechGenerator=void 0;const r=n(9135);class i extends r.AbstractSpeechGenerator{getSpeech(e,t){return""}}t.DummySpeechGenerator=i},1558:function(e,t,n){Object.defineProperty(t,"__esModule",{value:!0}),t.NodeSpeechGenerator=void 0;const r=n(8835),i=n(7486);class o extends i.TreeSpeechGenerator{getSpeech(e,t){return super.getSpeech(e,t),r.getAttribute(e,this.modality)}}t.NodeSpeechGenerator=o},7317:function(e,t,n){Object.defineProperty(t,"__esModule",{value:!0}),t.generatorMapping_=t.generator=void 0;const r=n(3153),i=n(6281),o=n(1565),s=n(7721),a=n(1558),c=n(5778),l=n(7486);t.generator=function(e){return(t.generatorMapping_[e]||t.generatorMapping_.Direct)()},t.generatorMapping_={Adhoc:()=>new r.AdhocSpeechGenerator,Color:()=>new i.ColorGenerator,Direct:()=>new o.DirectSpeechGenerator,Dummy:()=>new s.DummySpeechGenerator,Node:()=>new a.NodeSpeechGenerator,Summary:()=>new c.SummarySpeechGenerator,Tree:()=>new l.TreeSpeechGenerator}},144:function(e,t,n){Object.defineProperty(t,"__esModule",{value:!0}),t.computeSummary_=t.retrieveSummary=t.connectAllMactions=t.connectMactions=t.nodeAtPosition_=t.computePrefix_=t.retrievePrefix=t.addPrefix=t.addModality=t.addSpeech=t.recomputeMarkup=t.computeMarkup=t.recomputeSpeech=t.computeSpeech=void 0;const r=n(4253),i=n(6671),o=n(5024),s=n(8171),a=n(6060),c=n(1784),l=n(8835);function u(e){return a.SpeechRuleEngine.getInstance().evaluateNode(e)}function d(e){return u(c.SemanticTree.fromNode(e).xml())}function h(e){const t=d(e);return r.markup(t)}function p(e){const t=f(e);return r.markup(t)}function f(e){const t=c.SemanticTree.fromRoot(e),n=o.evalXPath('.//*[@id="'+e.id+'"]',t.xml());let r=n[0];return n.length>1&&(r=m(e,n)||r),r?a.SpeechRuleEngine.getInstance().runInSetting({modality:"prefix",domain:"default",style:"default",strict:!0,speech:!0},(function(){return a.SpeechRuleEngine.getInstance().evaluateNode(r)})):[]}function m(e,t){const n=t[0];if(!e.parent)return n;const r=[];for(;e;)r.push(e.id),e=e.parent;const i=function(e,t){for(;t.length&&t.shift().toString()===e.getAttribute("id")&&e.parentNode&&e.parentNode.parentNode;)e=e.parentNode.parentNode;return!t.length};for(let e,n=0;e=t[n];n++)if(i(e,r.slice()))return e;return n}function g(e){return e?a.SpeechRuleEngine.getInstance().runInSetting({modality:"summary",strict:!1,speech:!0},(function(){return a.SpeechRuleEngine.getInstance().evaluateNode(e)})):[]}t.computeSpeech=u,t.recomputeSpeech=d,t.computeMarkup=function(e){const t=u(e);return r.markup(t)},t.recomputeMarkup=h,t.addSpeech=function(e,t,n){const o=i.querySelectorAllByAttrValue(n,"id",t.id.toString())[0],a=o?r.markup(u(o)):h(t);e.setAttribute(s.Attribute.SPEECH,a)},t.addModality=function(e,t,n){const r=h(t);e.setAttribute(n,r)},t.addPrefix=function(e,t){const n=p(t);n&&e.setAttribute(s.Attribute.PREFIX,n)},t.retrievePrefix=p,t.computePrefix_=f,t.nodeAtPosition_=m,t.connectMactions=function(e,t,n){const r=i.querySelectorAll(t,"maction");for(let t,o=0;t=r[o];o++){const r=t.getAttribute("id"),o=i.querySelectorAllByAttrValue(e,"id",r)[0];if(!o)continue;const a=t.childNodes[1],c=a.getAttribute(s.Attribute.ID);let u=l.getBySemanticId(e,c);if(u&&"dummy"!==u.getAttribute(s.Attribute.TYPE))continue;if(u=o.childNodes[0],u.getAttribute("sre-highlighter-added"))continue;const d=a.getAttribute(s.Attribute.PARENT);d&&u.setAttribute(s.Attribute.PARENT,d),u.setAttribute(s.Attribute.TYPE,"dummy"),u.setAttribute(s.Attribute.ID,c);i.querySelectorAllByAttrValue(n,"id",c)[0].setAttribute("alternative",c)}},t.connectAllMactions=function(e,t){const n=i.querySelectorAll(e,"maction");for(let e,r=0;e=n[r];r++){const n=e.childNodes[1].getAttribute(s.Attribute.ID);i.querySelectorAllByAttrValue(t,"id",n)[0].setAttribute("alternative",n)}},t.retrieveSummary=function(e){const t=g(e);return r.markup(t)},t.computeSummary_=g},5778:function(e,t,n){Object.defineProperty(t,"__esModule",{value:!0}),t.SummarySpeechGenerator=void 0;const r=n(9135),i=n(144);class o extends r.AbstractSpeechGenerator{getSpeech(e,t){return i.connectAllMactions(t,this.getRebuilt().xml),this.generateSpeech(e,t)}}t.SummarySpeechGenerator=o},7486:function(e,t,n){Object.defineProperty(t,"__esModule",{value:!0}),t.TreeSpeechGenerator=void 0;const r=n(8171),i=n(8835),o=n(9135),s=n(144);class a extends o.AbstractSpeechGenerator{getSpeech(e,t){const n=this.generateSpeech(e,t),o=this.getRebuilt().nodeDict;for(const n in o){const a=o[n],c=i.getBySemanticId(t,n),l=i.getBySemanticId(e,n);c&&l&&(this.modality&&this.modality!==r.Attribute.SPEECH?s.addModality(l,a,this.modality):s.addSpeech(l,a,this.getRebuilt().xml),this.modality===r.Attribute.SPEECH&&s.addPrefix(l,a))}return n}}t.TreeSpeechGenerator=a},650:function(e,t,n){Object.defineProperty(t,"__esModule",{value:!0}),t.INTERVALS=t.makeLetter=t.numberRules=t.alphabetRules=t.getFont=t.makeInterval=t.generate=t.makeDomains_=t.Domains_=t.Base=t.Embellish=t.Font=void 0;const r=n(4886),i=n(2371),o=n(4524),s=n(3319),a=n(4161);var c,l,u;function d(){const e=o.LOCALE.ALPHABETS,n=(e,t)=>{const n={};return Object.keys(e).forEach((e=>n[e]=!0)),Object.keys(t).forEach((e=>n[e]=!0)),Object.keys(n)};t.Domains_.small=n(e.smallPrefix,e.letterTrans),t.Domains_.capital=n(e.capPrefix,e.letterTrans),t.Domains_.digit=n(e.digitPrefix,e.digitTrans)}function h(e){const t=e.toString(16).toUpperCase();return t.length>3?t:("000"+t).slice(-4)}function p([e,t],n){const r=parseInt(e,16),i=parseInt(t,16),o=[];for(let e=r;e<=i;e++){let t=h(e);!1!==n[t]&&(t=n[t]||t,o.push(t))}return o}function f(e){const t="normal"===e||"fullwidth"===e?"":o.LOCALE.MESSAGES.font[e]||o.LOCALE.MESSAGES.embellish[e]||"";return(0,s.localeFontCombiner)(t)}function m(e,n,r,i,s,a){const c=f(i);for(let i,l,u,d=0;i=e[d],l=n[d],u=r[d];d++){const e=a?o.LOCALE.ALPHABETS.capPrefix:o.LOCALE.ALPHABETS.smallPrefix,n=a?t.Domains_.capital:t.Domains_.small;S(c.combiner,i,l,u,c.font,e,s,o.LOCALE.ALPHABETS.letterTrans,n)}}function g(e,n,r,i,s){const a=f(r);for(let r,c,l=0;r=e[l],c=n[l];l++){const e=o.LOCALE.ALPHABETS.digitPrefix,n=l+s;S(a.combiner,r,c,n,a.font,e,i,o.LOCALE.ALPHABETS.digitTrans,t.Domains_.digit)}}function S(e,t,n,r,i,o,s,c,l){for(let u,d=0;u=l[d];d++){const l=u in c?c[u]:c.default,d=u in 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n=r.default.getInstance().locale;r.default.getInstance().locale=e,i.setLocale(),a.addSymbolRules({locale:e}),d();const s=t.INTERVALS;for(let e,t=0;e=s[t];t++){const t=p(e.interval,e.subst),n=t.map((function(e){return String.fromCodePoint(parseInt(e,16))}));if("offset"in 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0;const r=n(4886),i=n(4998),o=n(8310),s=n(8310),a=n(4161),c=n(6060);class l extends o.DynamicCstr{constructor(e,t){super(e),this.preference=t}static comparator(){return new d(r.default.getInstance().dynamicCstr,s.DynamicProperties.createProp([o.DynamicCstr.DEFAULT_VALUES[s.Axis.LOCALE]],[o.DynamicCstr.DEFAULT_VALUES[s.Axis.MODALITY]],[o.DynamicCstr.DEFAULT_VALUES[s.Axis.DOMAIN]],[o.DynamicCstr.DEFAULT_VALUES[s.Axis.STYLE]]))}static fromPreference(e){const t=e.split(":"),n={},r=u.getProperties(),i=Object.keys(r);for(let e,o=0;e=t[o];o++){const t=e.split("_");if(-1===i.indexOf(t[0]))continue;const o=t[1];o&&o!==l.AUTO&&-1!==r[t[0]].indexOf(o)&&(n[t[0]]=t[1])}return n}static toPreference(e){const t=Object.keys(e),n=[];for(let r=0;r<t.length;r++)n.push(t[r]+"_"+e[t[r]]);return n.length?n.join(":"):o.DynamicCstr.DEFAULT_VALUE}static getLocalePreferences(e){const t=e||a.enumerate(c.SpeechRuleEngine.getInstance().enumerate());return l.getLocalePreferences_(t)}static smartPreferences(e,t){const n=l.getLocalePreferences()[t];if(!n)return[];const r=e.explorers.speech,o=l.relevantPreferences(r.walker.getFocus().getSemanticPrimary()),s=i.DOMAIN_TO_STYLES.clearspeak;return[{type:"radio",content:"No Preferences",id:"clearspeak-default",variable:"speechRules"},{type:"radio",content:"Current Preferences",id:"clearspeak-"+s,variable:"speechRules"},{type:"rule"},{type:"label",content:"Preferences for "+o},{type:"rule"}].concat(n[o].map((function(e){const t=e.split("_");return{type:"radio",content:t[1],id:"clearspeak-"+l.addPreference(s,t[0],t[1]),variable:"speechRules"}})))}static relevantPreferences(e){const t=f[e.type];return t&&(t[e.role]||t[""])||"ImpliedTimes"}static findPreference(e,t){if("default"===e)return l.AUTO;return l.fromPreference(e)[t]||l.AUTO}static addPreference(e,t,n){if("default"===e)return t+"_"+n;const r=l.fromPreference(e);return r[t]=n,l.toPreference(r)}static getLocalePreferences_(e){const t={};for(const n in e){if(!e[n].speech||!e[n].speech.clearspeak)continue;const r=Object.keys(e[n].speech.clearspeak),i=t[n]={};for(const e in u.getProperties()){const t=u.getProperties()[e],n=[e+"_Auto"];if(t)for(const i of t)-1!==r.indexOf(e+"_"+i)&&n.push(e+"_"+i);i[e]=n}}return t}equal(e){if(!super.equal(e))return!1;const t=Object.keys(this.preference),n=e.preference;if(t.length!==Object.keys(n).length)return!1;for(let e,r=0;e=t[r];r++)if(this.preference[e]!==n[e])return!1;return!0}}t.ClearspeakPreferences=l,l.AUTO="Auto";const u=new s.DynamicProperties({AbsoluteValue:["Auto","AbsEnd","Cardinality","Determinant"],Bar:["Auto","Conjugate"],Caps:["Auto","SayCaps"],CombinationPermutation:["Auto","ChoosePermute"],Currency:["Auto","Position","Prefix"],Ellipses:["Auto","AndSoOn"],Enclosed:["Auto","EndEnclose"],Exponent:["Auto","AfterPower","Ordinal","OrdinalPower","Exponent"],Fraction:["Auto","EndFrac","FracOver","General","GeneralEndFrac","Ordinal","Over","OverEndFrac","Per"],Functions:["Auto","None","Reciprocal"],ImpliedTimes:["Auto","MoreImpliedTimes","None"],Log:["Auto","LnAsNaturalLog"],Matrix:["Auto","Combinatoric","EndMatrix","EndVector","SilentColNum","SpeakColNum","Vector"],MultiLineLabel:["Auto","Case","Constraint","Equation","Line","None","Row","Step"],MultiLineOverview:["Auto","None"],MultiLinePausesBetweenColumns:["Auto","Long","Short"],MultsymbolDot:["Auto","Dot"],MultsymbolX:["Auto","By","Cross"],Paren:["Auto","CoordPoint","Interval","Silent","Speak","SpeakNestingLevel"],Prime:["Auto","Angle","Length"],Roots:["Auto","PosNegSqRoot","PosNegSqRootEnd","RootEnd"],SetMemberSymbol:["Auto","Belongs","Element","Member","In"],Sets:["Auto","SilentBracket","woAll"],TriangleSymbol:["Auto","Delta"],Trig:["Auto","ArcTrig","TrigInverse","Reciprocal"],VerticalLine:["Auto","Divides","Given","SuchThat"]});class d extends s.DefaultComparator{constructor(e,t){super(e,t),this.preference=e instanceof l?e.preference:{}}match(e){if(!(e instanceof l))return super.match(e);if("default"===e.getComponents()[s.Axis.STYLE])return!0;const t=Object.keys(e.preference);for(let n,r=0;n=t[r];r++)if(this.preference[n]!==e.preference[n])return!1;return!0}compare(e,t){const n=super.compare(e,t);if(0!==n)return n;const r=e instanceof l,i=t instanceof l;if(!r&&i)return 1;if(r&&!i)return-1;if(!r&&!i)return 0;const o=Object.keys(e.preference).length,s=Object.keys(t.preference).length;return o>s?-1:o<s?1:0}}t.Comparator=d;class h extends s.DynamicCstrParser{constructor(){super([s.Axis.LOCALE,s.Axis.MODALITY,s.Axis.DOMAIN,s.Axis.STYLE])}parse(e){const t=super.parse(e);let n=t.getValue(s.Axis.STYLE);const r=t.getValue(s.Axis.LOCALE),i=t.getValue(s.Axis.MODALITY);let a={};return n!==o.DynamicCstr.DEFAULT_VALUE&&(a=this.fromPreference(n),n=this.toPreference(a)),new l({locale:r,modality:i,domain:"clearspeak",style:n},a)}fromPreference(e){return l.fromPreference(e)}toPreference(e){return l.toPreference(e)}}t.Parser=h;const p=[["AbsoluteValue","fenced","neutral","metric"],["Bar","overscore","overaccent"],["Caps","identifier","latinletter"],["CombinationPermutation","appl","unknown"],["Ellipses","punctuation","ellipsis"],["Exponent","superscript",""],["Fraction","fraction",""],["Functions","appl","simple function"],["ImpliedTimes","operator","implicit"],["Log","appl","prefix 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e}();r.default.getInstance().comparators.clearspeak=l.comparator,r.default.getInstance().parsers.clearspeak=new h},127:function(e,t,n){Object.defineProperty(t,"__esModule",{value:!0}),t.ClearspeakRules=void 0;const r=n(8310),i=n(931),o=n(8662),s=n(3269),a=n(2110),c=n(7278);t.ClearspeakRules=function(){c.addStore(r.DynamicCstr.BASE_LOCALE+".speech.clearspeak","",{CTFpauseSeparator:i.pauseSeparator,CTFnodeCounter:o.nodeCounter,CTFcontentIterator:i.contentIterator,CSFvulgarFraction:a.vulgarFraction,CQFvulgarFractionSmall:o.isSmallVulgarFraction,CQFcellsSimple:o.allCellsSimple,CSFordinalExponent:o.ordinalExponent,CSFwordOrdinal:o.wordOrdinal,CQFmatchingFences:o.matchingFences,CSFnestingDepth:o.nestingDepth,CQFfencedArguments:o.fencedArguments,CQFsimpleArguments:o.simpleArguments,CQFspaceoutNumber:s.spaceoutNumber})}},8662:function(e,t,n){Object.defineProperty(t,"__esModule",{value:!0}),t.wordOrdinal=t.layoutFactor_=t.fencedFactor_=t.simpleFactor_=t.simpleArguments=t.fencedArguments=t.insertNesting=t.matchingFences=t.nestingDepth=t.NESTING_DEPTH=t.ordinalExponent=t.allTextLastContent_=t.isUnitExpression=t.isSmallVulgarFraction=t.allCellsSimple=t.allIndices_=t.isInteger_=t.simpleCell_=t.simpleNode=t.hasPreference=t.isSimpleFraction_=t.isSimpleNumber_=t.isNumber_=t.isLetter_=t.isSimple_=t.isSimpleLetters_=t.isSimpleDegree_=t.isSimpleNegative_=t.isSimpleFunction_=t.isSimpleExpression=t.nodeCounter=void 0;const r=n(6671),i=n(4886),o=n(5024),s=n(4524),a=n(9385),c=n(1058),l=n(931),u=n(4036),d=n(241),h=n(4020);function p(e){return y(e)||S(e)||g(e)||m(e)||f(e)}function f(e){return"appl"===e.type&&("prefix function"===e.childNodes[0].role||"simple function"===e.childNodes[0].role)&&(b(e.childNodes[1])||"fenced"===e.childNodes[1].type&&b(e.childNodes[1].childNodes[0]))}function m(e){return"prefixop"===e.type&&"negative"===e.role&&b(e.childNodes[0])&&"prefixop"!==e.childNodes[0].type&&"appl"!==e.childNodes[0].type&&"punctuated"!==e.childNodes[0].type}function g(e){return"punctuated"===e.type&&"endpunct"===e.role&&2===e.childNodes.length&&"degree"===e.childNodes[1].role&&(N(e.childNodes[0])||E(e.childNodes[0])||"prefixop"===e.childNodes[0].type&&"negative"===e.childNodes[0].role&&(N(e.childNodes[0].childNodes[0])||E(e.childNodes[0].childNodes[0])))}function S(e){return 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c.concat(l&&u&&"space"===a.getAttribute("role")?[r.AuditoryDescription.create({text:"\u2800"},{})]:[])}}},2110:function(e,t,n){Object.defineProperty(t,"__esModule",{value:!0}),t.ordinalPosition=t.vulgarFraction=t.wordCounter=t.ordinalCounter=void 0;const r=n(1930),i=n(6671),o=n(4524),s=n(9385);t.ordinalCounter=function(e,t){let n=0;return function(){return o.LOCALE.NUMBERS.numericOrdinal(++n)+" "+t}},t.wordCounter=function(e,t){let n=0;return function(){return o.LOCALE.NUMBERS.numberToOrdinal(++n,!1)+" "+t}},t.vulgarFraction=function(e){const t=(0,s.convertVulgarFraction)(e,o.LOCALE.MESSAGES.MS.FRAC_OVER);return t.convertible&&t.enumerator&&t.denominator?[new r.Span(o.LOCALE.NUMBERS.numberToWords(t.enumerator),{extid:e.childNodes[0].childNodes[0].getAttribute("extid"),separator:""}),new r.Span(o.LOCALE.NUMBERS.vulgarSep,{separator:""}),new r.Span(o.LOCALE.NUMBERS.numberToOrdinal(t.denominator,1!==t.enumerator),{extid:e.childNodes[0].childNodes[1].getAttribute("extid")})]:[new 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r=n(4148),i=n(4253),o=n(6671),s=n(4998),a=n(985),c=n(6988),l=n(8171),u=n(4524),d=n(1058),h=n(7984),p=n(7317),f=n(144),m=n(3955),g=n(5658),S=n(1848),b=n(8119),N=n(8835),E=n(5024);class y{constructor(e,t,n,r){this.node=e,this.generator=t,this.highlighter=n,this.modifier=!1,this.keyMapping=new Map([[c.KeyCode.UP,this.up.bind(this)],[c.KeyCode.DOWN,this.down.bind(this)],[c.KeyCode.RIGHT,this.right.bind(this)],[c.KeyCode.LEFT,this.left.bind(this)],[c.KeyCode.TAB,this.repeat.bind(this)],[c.KeyCode.DASH,this.expand.bind(this)],[c.KeyCode.SPACE,this.depth.bind(this)],[c.KeyCode.HOME,this.home.bind(this)],[c.KeyCode.X,this.summary.bind(this)],[c.KeyCode.Z,this.detail.bind(this)],[c.KeyCode.V,this.virtualize.bind(this)],[c.KeyCode.P,this.previous.bind(this)],[c.KeyCode.U,this.undo.bind(this)],[c.KeyCode.LESS,this.previousRules.bind(this)],[c.KeyCode.GREATER,this.nextRules.bind(this)]]),this.cursors=[],this.xml_=null,this.rebuilt_=null,this.focus_=null,this.active_=!1,this.node.id?this.id=this.node.id:this.node.hasAttribute(y.SRE_ID_ATTR)?this.id=this.node.getAttribute(y.SRE_ID_ATTR):(this.node.setAttribute(y.SRE_ID_ATTR,y.ID_COUNTER.toString()),this.id=y.ID_COUNTER++),this.rootNode=N.getSemanticRoot(e),this.rootId=this.rootNode.getAttribute(l.Attribute.ID),this.xmlString_=r,this.moved=b.WalkerMoves.ENTER}getXml(){return this.xml_||(this.xml_=o.parseInput(this.xmlString_)),this.xml_}getRebuilt(){return this.rebuilt_||this.rebuildStree(),this.rebuilt_}isActive(){return this.active_}activate(){this.isActive()||(this.generator.start(),this.toggleActive_())}deactivate(){this.isActive()&&(b.WalkerState.setState(this.id,this.primaryId()),this.generator.end(),this.toggleActive_())}getFocus(e=!1){return this.focus_||(this.focus_=this.singletonFocus(this.rootId)),e&&this.updateFocus(),this.focus_}setFocus(e){this.focus_=e}getDepth(){return this.levels.depth()-1}isSpeech(){return this.generator.modality===l.Attribute.SPEECH}focusDomNodes(){return this.getFocus().getDomNodes()}focusSemanticNodes(){return this.getFocus().getSemanticNodes()}speech(){const e=this.focusDomNodes();if(!e.length)return"";const t=this.specialMove();if(null!==t)return t;switch(this.moved){case b.WalkerMoves.DEPTH:return this.depth_();case b.WalkerMoves.SUMMARY:return this.summary_();case b.WalkerMoves.DETAIL:return this.detail_();default:{const t=[],n=this.focusSemanticNodes();for(let r=0,i=e.length;r<i;r++){const i=e[r],o=n[r];t.push(i?this.generator.getSpeech(i,this.getXml()):f.recomputeMarkup(o))}return this.mergePrefix_(t)}}}move(e){const t=this.keyMapping.get(e);if(!t)return null;const n=t();return!(!n||n===this.getFocus())&&(this.setFocus(n),this.moved===b.WalkerMoves.HOME&&(this.levels=this.initLevels()),!0)}up(){return this.moved=b.WalkerMoves.UP,this.getFocus()}down(){return this.moved=b.WalkerMoves.DOWN,this.getFocus()}left(){return this.moved=b.WalkerMoves.LEFT,this.getFocus()}right(){return this.moved=b.WalkerMoves.RIGHT,this.getFocus()}repeat(){return this.moved=b.WalkerMoves.REPEAT,this.getFocus().clone()}depth(){return this.moved=this.isSpeech()?b.WalkerMoves.DEPTH:b.WalkerMoves.REPEAT,this.getFocus().clone()}home(){this.moved=b.WalkerMoves.HOME;return this.singletonFocus(this.rootId)}getBySemanticId(e){return N.getBySemanticId(this.node,e)}primaryId(){return this.getFocus().getSemanticPrimary().id.toString()}expand(){const e=this.getFocus().getDomPrimary(),t=this.actionable_(e);return t?(this.moved=b.WalkerMoves.EXPAND,t.dispatchEvent(new Event("click")),this.getFocus().clone()):this.getFocus()}expandable(e){return!!this.actionable_(e)&&0===e.childNodes.length}collapsible(e){return!!this.actionable_(e)&&e.childNodes.length>0}restoreState(){if(!this.highlighter)return;const e=b.WalkerState.getState(this.id);if(!e)return;let t=this.getRebuilt().nodeDict[e];const n=[];for(;t;)n.push(t.id),t=t.parent;for(n.pop();n.length>0;){this.down();const e=n.pop(),t=this.findFocusOnLevel(e);if(!t)break;this.setFocus(t)}this.moved=b.WalkerMoves.ENTER}updateFocus(){this.setFocus(g.Focus.factory(this.getFocus().getSemanticPrimary().id.toString(),this.getFocus().getSemanticNodes().map((e=>e.id.toString())),this.getRebuilt(),this.node))}rebuildStree(){this.rebuilt_=new S.RebuildStree(this.getXml()),this.rootId=this.rebuilt_.stree.root.id.toString(),this.generator.setRebuilt(this.rebuilt_),this.skeleton=h.SemanticSkeleton.fromTree(this.rebuilt_.stree),this.skeleton.populate(),this.focus_=this.singletonFocus(this.rootId),this.levels=this.initLevels(),f.connectMactions(this.node,this.getXml(),this.rebuilt_.xml)}previousLevel(){const e=this.getFocus().getDomPrimary();return e?N.getAttribute(e,l.Attribute.PARENT):this.getFocus().getSemanticPrimary().parent.id.toString()}nextLevel(){const e=this.getFocus().getDomPrimary();let t,n;if(e){t=N.splitAttribute(N.getAttribute(e,l.Attribute.CHILDREN)),n=N.splitAttribute(N.getAttribute(e,l.Attribute.CONTENT));const r=N.getAttribute(e,l.Attribute.TYPE),i=N.getAttribute(e,l.Attribute.ROLE);return this.combineContentChildren(r,i,n,t)}const r=e=>e.id.toString(),i=this.getRebuilt().nodeDict[this.primaryId()];return t=i.childNodes.map(r),n=i.contentNodes.map(r),0===t.length?[]:this.combineContentChildren(i.type,i.role,n,t)}singletonFocus(e){this.getRebuilt();const t=this.retrieveVisuals(e);return this.focusFromId(e,t)}retrieveVisuals(e){if(!this.skeleton)return[e];const t=parseInt(e,10),n=this.skeleton.subtreeNodes(t);if(!n.length)return[e];n.unshift(t);const r={},i=[];E.updateEvaluator(this.getXml());for(const e of n)r[e]||(i.push(e.toString()),r[e]=!0,this.subtreeIds(e,r));return i}subtreeIds(e,t){const n=E.evalXPath(`//*[@data-semantic-id="${e}"]`,this.getXml());E.evalXPath("*//@data-semantic-id",n[0]).forEach((e=>t[parseInt(e.textContent,10)]=!0))}focusFromId(e,t){return g.Focus.factory(e,t,this.getRebuilt(),this.node)}summary(){return this.moved=this.isSpeech()?b.WalkerMoves.SUMMARY:b.WalkerMoves.REPEAT,this.getFocus().clone()}detail(){return this.moved=this.isSpeech()?b.WalkerMoves.DETAIL:b.WalkerMoves.REPEAT,this.getFocus().clone()}specialMove(){return null}virtualize(e){return this.cursors.push({focus:this.getFocus(),levels:this.levels,undo:e||!this.cursors.length}),this.levels=this.levels.clone(),this.getFocus().clone()}previous(){const e=this.cursors.pop();return e?(this.levels=e.levels,e.focus):this.getFocus()}undo(){let e;do{e=this.cursors.pop()}while(e&&!e.undo);return e?(this.levels=e.levels,e.focus):this.getFocus()}update(e){this.generator.setOptions(e),(0,a.setup)(e).then((()=>p.generator("Tree").getSpeech(this.node,this.getXml())))}nextRules(){const e=this.generator.getOptions();return"speech"!==e.modality?this.getFocus():(s.DOMAIN_TO_STYLES[e.domain]=e.style,e.domain="mathspeak"===e.domain?"clearspeak":"mathspeak",e.style=s.DOMAIN_TO_STYLES[e.domain],this.update(e),this.moved=b.WalkerMoves.REPEAT,this.getFocus().clone())}nextStyle(e,t){if("mathspeak"===e){const e=["default","brief","sbrief"],n=e.indexOf(t);return-1===n?t:n>=e.length-1?e[0]:e[n+1]}if("clearspeak"===e){const e=m.ClearspeakPreferences.getLocalePreferences().en;if(!e)return"default";const n=m.ClearspeakPreferences.relevantPreferences(this.getFocus().getSemanticPrimary()),r=m.ClearspeakPreferences.findPreference(t,n),i=e[n].map((function(e){return e.split("_")[1]})),o=i.indexOf(r);if(-1===o)return t;const s=o>=i.length-1?i[0]:i[o+1];return m.ClearspeakPreferences.addPreference(t,n,s)}return t}previousRules(){const e=this.generator.getOptions();return"speech"!==e.modality?this.getFocus():(e.style=this.nextStyle(e.domain,e.style),this.update(e),this.moved=b.WalkerMoves.REPEAT,this.getFocus().clone())}refocus(){let e,t=this.getFocus();for(;!t.getNodes().length;){e=this.levels.peek();const n=this.up();if(!n)break;this.setFocus(n),t=this.getFocus(!0)}this.levels.push(e),this.setFocus(t)}toggleActive_(){this.active_=!this.active_}mergePrefix_(e,t=[]){const n=this.isSpeech()?this.prefix_():"";n&&e.unshift(n);const r=this.isSpeech()?this.postfix_():"";return r&&e.push(r),i.finalize(i.merge(t.concat(e)))}prefix_(){const e=this.getFocus().getDomNodes(),t=this.getFocus().getSemanticNodes();return e[0]?N.getAttribute(e[0],l.Attribute.PREFIX):f.retrievePrefix(t[0])}postfix_(){const e=this.getFocus().getDomNodes();return e[0]?N.getAttribute(e[0],l.Attribute.POSTFIX):""}depth_(){const e=d.Grammar.getInstance().getParameter("depth");d.Grammar.getInstance().setParameter("depth",!0);const t=this.getFocus().getDomPrimary(),n=this.expandable(t)?u.LOCALE.MESSAGES.navigate.EXPANDABLE:this.collapsible(t)?u.LOCALE.MESSAGES.navigate.COLLAPSIBLE:"",o=u.LOCALE.MESSAGES.navigate.LEVEL+" "+this.getDepth(),s=this.getFocus().getSemanticNodes(),a=f.retrievePrefix(s[0]),c=[new r.AuditoryDescription({text:o,personality:{}}),new r.AuditoryDescription({text:a,personality:{}}),new r.AuditoryDescription({text:n,personality:{}})];return d.Grammar.getInstance().setParameter("depth",e),i.finalize(i.markup(c))}actionable_(e){const t=null==e?void 0:e.parentNode;return t&&this.highlighter.isMactionNode(t)?t:null}summary_(){const e=this.getFocus().getSemanticPrimary().id.toString(),t=this.getRebuilt().xml.getAttribute("id")===e?this.getRebuilt().xml:o.querySelectorAllByAttrValue(this.getRebuilt().xml,"id",e)[0],n=f.retrieveSummary(t);return this.mergePrefix_([n])}detail_(){const e=this.getFocus().getSemanticPrimary().id.toString(),t=this.getRebuilt().xml.getAttribute("id")===e?this.getRebuilt().xml:o.querySelectorAllByAttrValue(this.getRebuilt().xml,"id",e)[0],n=t.getAttribute("alternative");t.removeAttribute("alternative");const r=f.computeMarkup(t),i=this.mergePrefix_([r]);return t.setAttribute("alternative",n),i}}t.AbstractWalker=y,y.ID_COUNTER=0,y.SRE_ID_ATTR="sre-explorer-id"},4296:function(e,t,n){Object.defineProperty(t,"__esModule",{value:!0}),t.DummyWalker=void 0;const r=n(4660);class i extends r.AbstractWalker{up(){return null}down(){return null}left(){return null}right(){return null}repeat(){return null}depth(){return null}home(){return null}getDepth(){return 0}initLevels(){return null}combineContentChildren(e,t,n,r){return[]}findFocusOnLevel(e){return null}}t.DummyWalker=i},5658:function(e,t,n){Object.defineProperty(t,"__esModule",{value:!0}),t.Focus=void 0;const r=n(8835);class i{constructor(e,t){this.nodes=e,this.primary=t,this.domNodes=[],this.domPrimary_=null,this.allNodes=[]}static factory(e,t,n,o){const s=e=>r.getBySemanticId(o,e),a=n.nodeDict,c=s(e),l=t.map(s),u=t.map((function(e){return a[e]})),d=new i(u,a[e]);return d.domNodes=l,d.domPrimary_=c,d.allNodes=i.generateAllVisibleNodes_(t,l,a,o),d}static generateAllVisibleNodes_(e,t,n,o){const s=e=>r.getBySemanticId(o,e);let a=[];for(let r=0,c=e.length;r<c;r++){if(t[r]){a.push(t[r]);continue}const c=n[e[r]];if(!c)continue;const l=c.childNodes.map((function(e){return e.id.toString()})),u=l.map(s);a=a.concat(i.generateAllVisibleNodes_(l,u,n,o))}return a}getSemanticPrimary(){return this.primary}getSemanticNodes(){return this.nodes}getNodes(){return this.allNodes}getDomNodes(){return this.domNodes}getDomPrimary(){return this.domPrimary_}toString(){return"Primary:"+this.domPrimary_+" Nodes:"+this.domNodes}clone(){const e=new i(this.nodes,this.primary);return e.domNodes=this.domNodes,e.domPrimary_=this.domPrimary_,e.allNodes=this.allNodes,e}}t.Focus=i},1497:function(e,t){Object.defineProperty(t,"__esModule",{value:!0}),t.Levels=void 0;class n{constructor(){this.level_=[]}push(e){this.level_.push(e)}pop(){return this.level_.pop()}peek(){return this.level_[this.level_.length-1]||null}indexOf(e){const t=this.peek();return t?t.indexOf(e):null}find(e){const t=this.peek();if(!t)return null;for(let n=0,r=t.length;n<r;n++)if(e(t[n]))return t[n];return null}get(e){const t=this.peek();return!t||e<0||e>=t.length?null:t[e]}depth(){return this.level_.length}clone(){const e=new n;return e.level_=this.level_.slice(0),e}toString(){let e="";for(let t,n=0;t=this.level_[n];n++)e+="\n"+t.map((function(e){return e.toString()}));return e}}t.Levels=n},1848:function(e,t,n){Object.defineProperty(t,"__esModule",{value:!0}),t.RebuildStree=void 0;const r=n(6671),i=n(8171),o=n(4020),s=n(4790),a=n(7793),c=n(7984),l=n(1784),u=n(8901),d=n(8835);class h{constructor(e){this.mathml=e,this.factory=new s.SemanticNodeFactory,this.nodeDict={},this.mmlRoot=d.getSemanticRoot(e),this.streeRoot=this.assembleTree(this.mmlRoot),this.stree=l.SemanticTree.fromNode(this.streeRoot,this.mathml),this.xml=this.stree.xml(),a.default.getInstance().setNodeFactory(this.factory)}static addAttributes(e,t,n){n&&1===t.childNodes.length&&t.childNodes[0].nodeType!==r.NodeType.TEXT_NODE&&u.addAttributes(e,t.childNodes[0]),u.addAttributes(e,t)}static textContent(e,t,n){if(!n&&t.textContent)return void(e.textContent=t.textContent);const r=d.splitAttribute(d.getAttribute(t,i.Attribute.OPERATOR));r.length>1&&(e.textContent=r[1])}static isPunctuated(e){return!c.SemanticSkeleton.simpleCollapseStructure(e)&&e[1]&&c.SemanticSkeleton.contentCollapseStructure(e[1])}getTree(){return this.stree}assembleTree(e){const t=this.makeNode(e),n=d.splitAttribute(d.getAttribute(e,i.Attribute.CHILDREN)),r=d.splitAttribute(d.getAttribute(e,i.Attribute.CONTENT));if(h.addAttributes(t,e,!(n.length||r.length)),0===r.length&&0===n.length)return h.textContent(t,e),t;if(r.length>0){const e=d.getBySemanticId(this.mathml,r[0]);e&&h.textContent(t,e,!0)}t.contentNodes=r.map((e=>this.setParent(e,t))),t.childNodes=n.map((e=>this.setParent(e,t)));const o=d.getAttribute(e,i.Attribute.COLLAPSED);return o?this.postProcess(t,o):t}makeNode(e){const t=d.getAttribute(e,i.Attribute.TYPE),n=d.getAttribute(e,i.Attribute.ROLE),r=d.getAttribute(e,i.Attribute.FONT),o=d.getAttribute(e,i.Attribute.ANNOTATION)||"",s=d.getAttribute(e,i.Attribute.ID),a=d.getAttribute(e,i.Attribute.EMBELLISHED),c=d.getAttribute(e,i.Attribute.FENCEPOINTER),l=this.createNode(parseInt(s,10));return l.type=t,l.role=n,l.font=r||"unknown",l.parseAnnotation(o),c&&(l.fencePointer=c),a&&(l.embellished=a),l}makePunctuation(e){const t=this.createNode(e);return t.updateContent((0,o.invisibleComma)()),t.role="dummy",t}makePunctuated(e,t,n){const r=this.createNode(t[0]);r.type="punctuated",r.embellished=e.embellished,r.fencePointer=e.fencePointer,r.role=n;const i=t.splice(1,1)[0].slice(1);r.contentNodes=i.map(this.makePunctuation.bind(this)),this.collapsedChildren_(t)}makeEmpty(e,t,n){const r=this.createNode(t);r.type="empty",r.embellished=e.embellished,r.fencePointer=e.fencePointer,r.role=n}makeIndex(e,t,n){if(h.isPunctuated(t))return this.makePunctuated(e,t,n),void(t=t[0]);c.SemanticSkeleton.simpleCollapseStructure(t)&&!this.nodeDict[t.toString()]&&this.makeEmpty(e,t,n)}postProcess(e,t){const n=c.SemanticSkeleton.fromString(t).array;if("subsup"===e.type){const t=this.createNode(n[1][0]);return t.type="subscript",t.role="subsup",e.type="superscript",t.embellished=e.embellished,t.fencePointer=e.fencePointer,this.makeIndex(e,n[1][2],"rightsub"),this.makeIndex(e,n[2],"rightsuper"),this.collapsedChildren_(n),e}if("subscript"===e.type)return this.makeIndex(e,n[2],"rightsub"),this.collapsedChildren_(n),e;if("superscript"===e.type)return this.makeIndex(e,n[2],"rightsuper"),this.collapsedChildren_(n),e;if("tensor"===e.type)return this.makeIndex(e,n[2],"leftsub"),this.makeIndex(e,n[3],"leftsuper"),this.makeIndex(e,n[4],"rightsub"),this.makeIndex(e,n[5],"rightsuper"),this.collapsedChildren_(n),e;if("punctuated"===e.type){if(h.isPunctuated(n)){const t=n.splice(1,1)[0].slice(1);e.contentNodes=t.map(this.makePunctuation.bind(this))}return e}if("underover"===e.type){const t=this.createNode(n[1][0]);return"overaccent"===e.childNodes[1].role?(t.type="overscore",e.type="underscore"):(t.type="underscore",e.type="overscore"),t.role="underover",t.embellished=e.embellished,t.fencePointer=e.fencePointer,this.collapsedChildren_(n),e}return e}createNode(e){const t=this.factory.makeNode(e);return this.nodeDict[e.toString()]=t,t}collapsedChildren_(e){const t=e=>{const n=this.nodeDict[e[0]];n.childNodes=[];for(let r=1,i=e.length;r<i;r++){const i=e[r];n.childNodes.push(c.SemanticSkeleton.simpleCollapseStructure(i)?this.nodeDict[i]:t(i))}return n};t(e)}setParent(e,t){const n=d.getBySemanticId(this.mathml,e),r=this.assembleTree(n);return r.parent=t,r}}t.RebuildStree=h},1937:function(e,t,n){Object.defineProperty(t,"__esModule",{value:!0}),t.SemanticWalker=void 0;const r=n(4660),i=n(1497);class o extends r.AbstractWalker{constructor(e,t,n,r){super(e,t,n,r),this.node=e,this.generator=t,this.highlighter=n,this.levels=null,this.restoreState()}initLevels(){const e=new i.Levels;return e.push([this.getFocus()]),e}up(){super.up();const e=this.previousLevel();if(!e)return null;this.levels.pop();return this.levels.find((function(t){return t.getSemanticNodes().some((function(t){return t.id.toString()===e}))}))}down(){super.down();const e=this.nextLevel();return 0===e.length?null:(this.levels.push(e),e[0])}combineContentChildren(e,t,n,r){switch(e){case"relseq":case"infixop":case"multirel":return this.makePairList(r,n);case"prefixop":return[this.focusFromId(r[0],n.concat(r))];case"postfixop":return[this.focusFromId(r[0],r.concat(n))];case"matrix":case"vector":case"fenced":return[this.focusFromId(r[0],[n[0],r[0],n[1]])];case"cases":return[this.focusFromId(r[0],[n[0],r[0]])];case"punctuated":return"text"===t?r.map(this.singletonFocus.bind(this)):r.length===n.length?n.map(this.singletonFocus.bind(this)):this.combinePunctuations(r,n,[],[]);case"appl":return[this.focusFromId(r[0],[r[0],n[0]]),this.singletonFocus(r[1])];case"root":return[this.singletonFocus(r[1]),this.singletonFocus(r[0])];default:return r.map(this.singletonFocus.bind(this))}}combinePunctuations(e,t,n,r){if(0===e.length)return r;const i=e.shift(),o=t.shift();return i===o?(n.push(o),this.combinePunctuations(e,t,n,r)):(t.unshift(o),n.push(i),e.length===t.length?(r.push(this.focusFromId(i,n.concat(t))),r):(r.push(this.focusFromId(i,n)),this.combinePunctuations(e,t,[],r)))}makePairList(e,t){if(0===e.length)return[];if(1===e.length)return[this.singletonFocus(e[0])];const n=[this.singletonFocus(e.shift())];for(let r=0,i=e.length;r<i;r++)n.push(this.focusFromId(e[r],[t[r],e[r]]));return n}left(){super.left();const e=this.levels.indexOf(this.getFocus());if(null===e)return null;const t=this.levels.get(e-1);return t||null}right(){super.right();const e=this.levels.indexOf(this.getFocus());if(null===e)return null;const t=this.levels.get(e+1);return t||null}findFocusOnLevel(e){return this.levels.find((t=>t.getSemanticPrimary().id===e))}}t.SemanticWalker=o},3531:function(e,t,n){Object.defineProperty(t,"__esModule",{value:!0}),t.SyntaxWalker=void 0;const r=n(1426),i=n(4660),o=n(1497);class s extends i.AbstractWalker{constructor(e,t,n,r){super(e,t,n,r),this.node=e,this.generator=t,this.highlighter=n,this.levels=null,this.restoreState()}initLevels(){const e=new o.Levels;return e.push([this.primaryId()]),e}up(){super.up();const e=this.previousLevel();return e?(this.levels.pop(),this.singletonFocus(e)):null}down(){super.down();const e=this.nextLevel();if(0===e.length)return null;const t=this.singletonFocus(e[0]);return t&&this.levels.push(e),t}combineContentChildren(e,t,n,i){switch(e){case"relseq":case"infixop":case"multirel":return(0,r.interleaveLists)(i,n);case"prefixop":return n.concat(i);case"postfixop":return i.concat(n);case"matrix":case"vector":case"fenced":return i.unshift(n[0]),i.push(n[1]),i;case"cases":return i.unshift(n[0]),i;case"punctuated":return"text"===t?(0,r.interleaveLists)(i,n):i;case"appl":return[i[0],n[0],i[1]];case"root":return[i[1],i[0]];default:return i}}left(){super.left();const e=this.levels.indexOf(this.primaryId());if(null===e)return null;const t=this.levels.get(e-1);return t?this.singletonFocus(t):null}right(){super.right();const e=this.levels.indexOf(this.primaryId());if(null===e)return null;const t=this.levels.get(e+1);return t?this.singletonFocus(t):null}findFocusOnLevel(e){return this.singletonFocus(e.toString())}focusDomNodes(){return[this.getFocus().getDomPrimary()]}focusSemanticNodes(){return[this.getFocus().getSemanticPrimary()]}}t.SyntaxWalker=s},4051:function(e,t,n){Object.defineProperty(t,"__esModule",{value:!0}),t.TableWalker=void 0;const r=n(6671),i=n(6988),o=n(3531),s=n(8119);class a extends o.SyntaxWalker{constructor(e,t,n,r){super(e,t,n,r),this.node=e,this.generator=t,this.highlighter=n,this.firstJump=null,this.key_=null,this.row_=0,this.currentTable_=null,this.keyMapping.set(i.KeyCode.ZERO,this.jumpCell.bind(this)),this.keyMapping.set(i.KeyCode.ONE,this.jumpCell.bind(this)),this.keyMapping.set(i.KeyCode.TWO,this.jumpCell.bind(this)),this.keyMapping.set(i.KeyCode.THREE,this.jumpCell.bind(this)),this.keyMapping.set(i.KeyCode.FOUR,this.jumpCell.bind(this)),this.keyMapping.set(i.KeyCode.FIVE,this.jumpCell.bind(this)),this.keyMapping.set(i.KeyCode.SIX,this.jumpCell.bind(this)),this.keyMapping.set(i.KeyCode.SEVEN,this.jumpCell.bind(this)),this.keyMapping.set(i.KeyCode.EIGHT,this.jumpCell.bind(this)),this.keyMapping.set(i.KeyCode.NINE,this.jumpCell.bind(this))}move(e){this.key_=e;const t=super.move(e);return this.modifier=!1,t}up(){return this.moved=s.WalkerMoves.UP,this.eligibleCell_()?this.verticalMove_(!1):super.up()}down(){return this.moved=s.WalkerMoves.DOWN,this.eligibleCell_()?this.verticalMove_(!0):super.down()}jumpCell(){if(!this.isInTable_()||null===this.key_)return this.getFocus();if(this.moved===s.WalkerMoves.ROW){this.moved=s.WalkerMoves.CELL;const e=this.key_-i.KeyCode.ZERO;return this.isLegalJump_(this.row_,e)?this.jumpCell_(this.row_,e):this.getFocus()}const e=this.key_-i.KeyCode.ZERO;return e>this.currentTable_.childNodes.length?this.getFocus():(this.row_=e,this.moved=s.WalkerMoves.ROW,this.getFocus().clone())}undo(){const e=super.undo();return e===this.firstJump&&(this.firstJump=null),e}eligibleCell_(){const e=this.getFocus().getSemanticPrimary();return this.modifier&&"cell"===e.type&&-1!==a.ELIGIBLE_CELL_ROLES.indexOf(e.role)}verticalMove_(e){const t=this.previousLevel();if(!t)return null;const n=this.getFocus(),r=this.levels.indexOf(this.primaryId()),i=this.levels.pop(),o=this.levels.indexOf(t),s=this.levels.get(e?o+1:o-1);if(!s)return this.levels.push(i),null;this.setFocus(this.singletonFocus(s));const a=this.nextLevel();return a[r]?(this.levels.push(a),this.singletonFocus(a[r])):(this.setFocus(n),this.levels.push(i),null)}jumpCell_(e,t){this.firstJump?this.virtualize(!1):(this.firstJump=this.getFocus(),this.virtualize(!0));const n=this.currentTable_.id.toString();let r;do{r=this.levels.pop()}while(-1===r.indexOf(n));this.levels.push(r),this.setFocus(this.singletonFocus(n)),this.levels.push(this.nextLevel());const i=this.currentTable_.childNodes[e-1];return this.setFocus(this.singletonFocus(i.id.toString())),this.levels.push(this.nextLevel()),this.singletonFocus(i.childNodes[t-1].id.toString())}isLegalJump_(e,t){const n=r.querySelectorAllByAttrValue(this.getRebuilt().xml,"id",this.currentTable_.id.toString())[0];if(!n||n.hasAttribute("alternative"))return!1;const i=this.currentTable_.childNodes[e-1];if(!i)return!1;const o=r.querySelectorAllByAttrValue(n,"id",i.id.toString())[0];return!(!o||o.hasAttribute("alternative"))&&!(!i||!i.childNodes[t-1])}isInTable_(){let e=this.getFocus().getSemanticPrimary();for(;e;){if(-1!==a.ELIGIBLE_TABLE_TYPES.indexOf(e.type))return this.currentTable_=e,!0;e=e.parent}return!1}}t.TableWalker=a,a.ELIGIBLE_CELL_ROLES=["determinant","rowvector","binomial","squarematrix","multiline","matrix","vector","cases","table"],a.ELIGIBLE_TABLE_TYPES=["multiline","matrix","vector","cases","table"]},8119:function(e,t){Object.defineProperty(t,"__esModule",{value:!0}),t.WalkerState=t.WalkerMoves=void 0,function(e){e.UP="up",e.DOWN="down",e.LEFT="left",e.RIGHT="right",e.REPEAT="repeat",e.DEPTH="depth",e.ENTER="enter",e.EXPAND="expand",e.HOME="home",e.SUMMARY="summary",e.DETAIL="detail",e.ROW="row",e.CELL="cell"}(t.WalkerMoves||(t.WalkerMoves={}));class n{static resetState(e){delete n.STATE[e]}static setState(e,t){n.STATE[e]=t}static getState(e){return n.STATE[e]}}t.WalkerState=n,n.STATE={}},907:function(e,t,n){Object.defineProperty(t,"__esModule",{value:!0}),t.walkerMapping_=t.walker=void 0;const r=n(4296),i=n(1937),o=n(3531),s=n(4051);t.walker=function(e,n,r,i,o){return(t.walkerMapping_[e.toLowerCase()]||t.walkerMapping_.dummy)(n,r,i,o)},t.walkerMapping_={dummy:(e,t,n,i)=>new r.DummyWalker(e,t,n,i),semantic:(e,t,n,r)=>new i.SemanticWalker(e,t,n,r),syntax:(e,t,n,r)=>new o.SyntaxWalker(e,t,n,r),table:(e,t,n,r)=>new s.TableWalker(e,t,n,r)}},8835:function(e,t,n){Object.defineProperty(t,"__esModule",{value:!0}),t.getBySemanticId=t.getSemanticRoot=t.getAttribute=t.splitAttribute=void 0;const r=n(6671),i=n(8171);t.splitAttribute=function(e){return e?e.split(/,/):[]},t.getAttribute=function(e,t){return e.getAttribute(t)},t.getSemanticRoot=function(e){if(e.hasAttribute(i.Attribute.TYPE)&&!e.hasAttribute(i.Attribute.PARENT))return e;const t=r.querySelectorAllByAttr(e,i.Attribute.TYPE);for(let e,n=0;e=t[n];n++)if(!e.hasAttribute(i.Attribute.PARENT))return e;return e},t.getBySemanticId=function(e,t){return e.getAttribute(i.Attribute.ID)===t?e:r.querySelectorAllByAttrValue(e,i.Attribute.ID,t)[0]}}},__webpack_module_cache__={};function __webpack_require__(e){var t=__webpack_module_cache__[e];if(void 0!==t)return t.exports;var n=__webpack_module_cache__[e]={exports:{}};return __webpack_modules__[e].call(n.exports,n,n.exports,__webpack_require__),n.exports}__webpack_require__.n=function(e){var t=e&&e.__esModule?function(){return e.default}:function(){return e};return __webpack_require__.d(t,{a:t}),t},__webpack_require__.d=function(e,t){for(var n in t)__webpack_require__.o(t,n)&&!__webpack_require__.o(e,n)&&Object.defineProperty(e,n,{enumerable:!0,get:t[n]})},__webpack_require__.g=function(){if("object"==typeof globalThis)return globalThis;try{return this||new Function("return this")()}catch(e){if("object"==typeof window)return window}}(),__webpack_require__.o=function(e,t){return Object.prototype.hasOwnProperty.call(e,t)};var __webpack_exports__={};!function(){var e=__webpack_require__(8723),t=__webpack_require__(7306),n=__webpack_require__(8905),r=__webpack_require__.n(n);MathJax.loader&&MathJax.loader.checkVersion("a11y/sre",t.q,"a11y"),(0,e.r8)({_:{a11y:{sre:n}}});var i=__webpack_require__(1993);if(MathJax.startup){var o=i.GL.resolvePath("[sre]",!1);if("undefined"!=typeof window)window.SREfeature={json:o};else{try{o=MathJax.config.loader.require.resolve(o+"/base.json").replace(/\/base\.json$/,"")}catch(e){}__webpack_require__.g.SREfeature={json:o}}}MathJax.startup&&(("undefined"!=typeof window?window:__webpack_require__.g).SREfeature.custom=function(e){return r().preloadLocales(e)})}()})();
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diff --git a/devel/_static/mathjax/input/tex/extensions/action.js b/devel/_static/mathjax/input/tex/extensions/action.js
new file mode 100644
index 0000000000..26f1e7a85f
--- /dev/null
+++ b/devel/_static/mathjax/input/tex/extensions/action.js
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/devel/_static/mathjax/input/tex/extensions/all-packages.js b/devel/_static/mathjax/input/tex/extensions/all-packages.js
new file mode 100644
index 0000000000..a8283a6cf8
--- /dev/null
+++ b/devel/_static/mathjax/input/tex/extensions/all-packages.js
@@ -0,0 +1,34 @@
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l.CharacterMap("AMSsymbols-mathchar0mo",u.default.mathchar0mo,{dotplus:"\u2214",ltimes:"\u22c9",smallsetminus:["\u2216",{variantForm:!0}],rtimes:"\u22ca",Cap:"\u22d2",doublecap:"\u22d2",leftthreetimes:"\u22cb",Cup:"\u22d3",doublecup:"\u22d3",rightthreetimes:"\u22cc",barwedge:"\u22bc",curlywedge:"\u22cf",veebar:"\u22bb",curlyvee:"\u22ce",doublebarwedge:"\u2a5e",boxminus:"\u229f",circleddash:"\u229d",boxtimes:"\u22a0",circledast:"\u229b",boxdot:"\u22a1",circledcirc:"\u229a",boxplus:"\u229e",centerdot:["\u22c5",{variantForm:!0}],divideontimes:"\u22c7",intercal:"\u22ba",leqq:"\u2266",geqq:"\u2267",leqslant:"\u2a7d",geqslant:"\u2a7e",eqslantless:"\u2a95",eqslantgtr:"\u2a96",lesssim:"\u2272",gtrsim:"\u2273",lessapprox:"\u2a85",gtrapprox:"\u2a86",approxeq:"\u224a",lessdot:"\u22d6",gtrdot:"\u22d7",lll:"\u22d8",llless:"\u22d8",ggg:"\u22d9",gggtr:"\u22d9",lessgtr:"\u2276",gtrless:"\u2277",lesseqgtr:"\u22da",gtreqless:"\u22db",lesseqqgtr:"\u2a8b",gtreqqless:"\u2a8c",doteqdot:"\u2251",Doteq:"\u2251",eqcirc:"\u2256",risingdotseq:"\u2253",circeq:"\u2257",fallingdotseq:"\u2252",triangleq:"\u225c",backsim:"\u223d",thicksim:["\u223c",{variantForm:!0}],backsimeq:"\u22cd",thickapprox:["\u2248",{variantForm:!0}],subseteqq:"\u2ac5",supseteqq:"\u2ac6",Subset:"\u22d0",Supset:"\u22d1",sqsubset:"\u228f",sqsupset:"\u2290",preccurlyeq:"\u227c",succcurlyeq:"\u227d",curlyeqprec:"\u22de",curlyeqsucc:"\u22df",precsim:"\u227e",succsim:"\u227f",precapprox:"\u2ab7",succapprox:"\u2ab8",vartriangleleft:"\u22b2",lhd:"\u22b2",vartriangleright:"\u22b3",rhd:"\u22b3",trianglelefteq:"\u22b4",unlhd:"\u22b4",trianglerighteq:"\u22b5",unrhd:"\u22b5",vDash:["\u22a8",{variantForm:!0}],Vdash:"\u22a9",Vvdash:"\u22aa",smallsmile:["\u2323",{variantForm:!0}],shortmid:["\u2223",{variantForm:!0}],smallfrown:["\u2322",{variantForm:!0}],shortparallel:["\u2225",{variantForm:!0}],bumpeq:"\u224f",between:"\u226c",Bumpeq:"\u224e",pitchfork:"\u22d4",varpropto:["\u221d",{variantForm:!0}],backepsilon:"\u220d",blacktriangleleft:"\u25c2",blacktriangleright:"\u25b8",therefore:"\u2234",because:"\u2235",eqsim:"\u2242",vartriangle:["\u25b3",{variantForm:!0}],Join:"\u22c8",nless:"\u226e",ngtr:"\u226f",nleq:"\u2270",ngeq:"\u2271",nleqslant:["\u2a87",{variantForm:!0}],ngeqslant:["\u2a88",{variantForm:!0}],nleqq:["\u2270",{variantForm:!0}],ngeqq:["\u2271",{variantForm:!0}],lneq:"\u2a87",gneq:"\u2a88",lneqq:"\u2268",gneqq:"\u2269",lvertneqq:["\u2268",{variantForm:!0}],gvertneqq:["\u2269",{variantForm:!0}],lnsim:"\u22e6",gnsim:"\u22e7",lnapprox:"\u2a89",gnapprox:"\u2a8a",nprec:"\u2280",nsucc:"\u2281",npreceq:["\u22e0",{variantForm:!0}],nsucceq:["\u22e1",{variantForm:!0}],precneqq:"\u2ab5",succneqq:"\u2ab6",precnsim:"\u22e8",succnsim:"\u22e9",precnapprox:"\u2ab9",succnapprox:"\u2aba",nsim:"\u2241",ncong:"\u2247",nshortmid:["\u2224",{variantForm:!0}],nshortparallel:["\u2226",{variantForm:!0}],nmid:"\u2224",nparallel:"\u2226",nvdash:"\u22ac",nvDash:"\u22ad",nVdash:"\u22ae",nVDash:"\u22af",ntriangleleft:"\u22ea",ntriangleright:"\u22eb",ntrianglelefteq:"\u22ec",ntrianglerighteq:"\u22ed",nsubseteq:"\u2288",nsupseteq:"\u2289",nsubseteqq:["\u2288",{variantForm:!0}],nsupseteqq:["\u2289",{variantForm:!0}],subsetneq:"\u228a",supsetneq:"\u228b",varsubsetneq:["\u228a",{variantForm:!0}],varsupsetneq:["\u228b",{variantForm:!0}],subsetneqq:"\u2acb",supsetneqq:"\u2acc",varsubsetneqq:["\u2acb",{variantForm:!0}],varsupsetneqq:["\u2acc",{variantForm:!0}],leftleftarrows:"\u21c7",rightrightarrows:"\u21c9",leftrightarrows:"\u21c6",rightleftarrows:"\u21c4",Lleftarrow:"\u21da",Rrightarrow:"\u21db",twoheadleftarrow:"\u219e",twoheadrightarrow:"\u21a0",leftarrowtail:"\u21a2",rightarrowtail:"\u21a3",looparrowleft:"\u21ab",looparrowright:"\u21ac",leftrightharpoons:"\u21cb",rightleftharpoons:["\u21cc",{variantForm:!0}],curvearrowleft:"\u21b6",curvearrowright:"\u21b7",circlearrowleft:"\u21ba",circlearrowright:"\u21bb",Lsh:"\u21b0",Rsh:"\u21b1",upuparrows:"\u21c8",downdownarrows:"\u21ca",upharpoonleft:"\u21bf",upharpoonright:"\u21be",downharpoonleft:"\u21c3",restriction:"\u21be",multimap:"\u22b8",downharpoonright:"\u21c2",leftrightsquigarrow:"\u21ad",rightsquigarrow:"\u21dd",leadsto:"\u21dd",dashrightarrow:"\u21e2",dashleftarrow:"\u21e0",nleftarrow:"\u219a",nrightarrow:"\u219b",nLeftarrow:"\u21cd",nRightarrow:"\u21cf",nleftrightarrow:"\u21ae",nLeftrightarrow:"\u21ce"}),new l.DelimiterMap("AMSsymbols-delimiter",u.default.delimiter,{"\\ulcorner":"\u231c","\\urcorner":"\u231d","\\llcorner":"\u231e","\\lrcorner":"\u231f"}),new l.CommandMap("AMSsymbols-macros",{implies:["Macro","\\;\\Longrightarrow\\;"],impliedby:["Macro","\\;\\Longleftarrow\\;"]},s.AmsMethods)},8016:function(t,e,r){var n=this&&this.__assign||function(){return n=Object.assign||function(t){for(var e,r=1,n=arguments.length;r<n;r++)for(var a in e=arguments[r])Object.prototype.hasOwnProperty.call(e,a)&&(t[a]=e[a]);return t},n.apply(this,arguments)},a=this&&this.__read||function(t,e){var r="function"==typeof Symbol&&t[Symbol.iterator];if(!r)return t;var n,a,o=r.call(t),i=[];try{for(;(void 0===e||e-- >0)&&!(n=o.next()).done;)i.push(n.value)}catch(t){a={error:t}}finally{try{n&&!n.done&&(r=o.return)&&r.call(o)}finally{if(a)throw a.error}}return i},o=this&&this.__importDefault||function(t){return t&&t.__esModule?t:{default:t}};Object.defineProperty(e,"__esModule",{value:!0}),e.NEW_OPS=e.AmsMethods=void 0;var i=o(r(7398)),s=o(r(4945)),l=o(r(4748)),c=r(6108),u=o(r(2193)),d=o(r(3402)),p=r(4924),f=o(r(7360)),m=r(2955);function h(t){if(!t||t.isInferred&&0===t.childNodes.length)return[null,null];if(t.isKind("msubsup")&&g(t))return[t,null];var e=l.default.getChildAt(t,0);return t.isInferred&&e&&g(e)?(t.childNodes.splice(0,1),[e,t]):[null,t]}function g(t){var e=t.childNodes[0];return e&&e.isKind("mi")&&""===e.getText()}e.AmsMethods={},e.AmsMethods.AmsEqnArray=function(t,e,r,n,a,o,s){var l=t.GetBrackets("\\begin{"+e.getName()+"}"),c=f.default.EqnArray(t,e,r,n,a,o,s);return i.default.setArrayAlign(c,l)},e.AmsMethods.AlignAt=function(t,r,n,a){var o,s,l=r.getName(),c="",u=[];if(a||(s=t.GetBrackets("\\begin{"+l+"}")),(o=t.GetArgument("\\begin{"+l+"}")).match(/[^0-9]/))throw new d.default("PositiveIntegerArg","Argument to %1 must me a positive integer","\\begin{"+l+"}");for(var p=parseInt(o,10);p>0;)c+="rl",u.push("0em 0em"),p--;var f=u.join(" ");if(a)return e.AmsMethods.EqnArray(t,r,n,a,c,f);var m=e.AmsMethods.EqnArray(t,r,n,a,c,f);return i.default.setArrayAlign(m,s)},e.AmsMethods.Multline=function(t,e,r){t.Push(e),i.default.checkEqnEnv(t);var n=t.itemFactory.create("multline",r,t.stack);return n.arraydef={displaystyle:!0,rowspacing:".5em",columnspacing:"100%",width:t.options.ams.multlineWidth,side:t.options.tagSide,minlabelspacing:t.options.tagIndent,framespacing:t.options.ams.multlineIndent+" 0",frame:"","data-width-includes-label":!0},n},e.AmsMethods.XalignAt=function(t,r,n,a){var o=t.GetArgument("\\begin{"+r.getName()+"}");if(o.match(/[^0-9]/))throw new d.default("PositiveIntegerArg","Argument to %1 must me a positive integer","\\begin{"+r.getName()+"}");var i=a?"crl":"rlc",s=a?"fit auto auto":"auto auto fit",l=e.AmsMethods.FlalignArray(t,r,n,a,!1,i,s,!0);return l.setProperty("xalignat",2*parseInt(o)),l},e.AmsMethods.FlalignArray=function(t,e,r,n,a,o,s,l){void 0===l&&(l=!1),t.Push(e),i.default.checkEqnEnv(t),o=o.split("").join(" ").replace(/r/g,"right").replace(/l/g,"left").replace(/c/g,"center");var c=t.itemFactory.create("flalign",e.getName(),r,n,a,t.stack);return c.arraydef={width:"100%",displaystyle:!0,columnalign:o,columnspacing:"0em",columnwidth:s,rowspacing:"3pt",side:t.options.tagSide,minlabelspacing:l?"0":t.options.tagIndent,"data-width-includes-label":!0},c.setProperty("zeroWidthLabel",l),c},e.NEW_OPS="ams-declare-ops",e.AmsMethods.HandleDeclareOp=function(t,r){var n=t.GetStar()?"*":"",a=i.default.trimSpaces(t.GetArgument(r));"\\"===a.charAt(0)&&(a=a.substr(1));var o=t.GetArgument(r);t.configuration.handlers.retrieve(e.NEW_OPS).add(a,new p.Macro(a,e.AmsMethods.Macro,["\\operatorname".concat(n,"{").concat(o,"}")]))},e.AmsMethods.HandleOperatorName=function(t,e){var r=t.GetStar(),a=i.default.trimSpaces(t.GetArgument(e)),o=new u.default(a,n(n({},t.stack.env),{font:c.TexConstant.Variant.NORMAL,multiLetterIdentifiers:/^[-*a-z]+/i,operatorLetters:!0}),t.configuration).mml();if(o.isKind("mi")||(o=t.create("node","TeXAtom",[o])),l.default.setProperties(o,{movesupsub:r,movablelimits:!0,texClass:m.TEXCLASS.OP}),!r){var s=t.GetNext(),d=t.i;"\\"===s&&++t.i&&"limits"!==t.GetCS()&&(t.i=d)}t.Push(o)},e.AmsMethods.SideSet=function(t,e){var r=a(h(t.ParseArg(e)),2),n=r[0],o=r[1],s=a(h(t.ParseArg(e)),2),c=s[0],u=s[1],d=t.ParseArg(e),p=d;n&&(o?n.replaceChild(t.create("node","mphantom",[t.create("node","mpadded",[i.default.copyNode(d,t)],{width:0})]),l.default.getChildAt(n,0)):(p=t.create("node","mmultiscripts",[d]),c&&l.default.appendChildren(p,[l.default.getChildAt(c,1)||t.create("node","none"),l.default.getChildAt(c,2)||t.create("node","none")]),l.default.setProperty(p,"scriptalign","left"),l.default.appendChildren(p,[t.create("node","mprescripts"),l.default.getChildAt(n,1)||t.create("node","none"),l.default.getChildAt(n,2)||t.create("node","none")]))),c&&p===d&&(c.replaceChild(d,l.default.getChildAt(c,0)),p=c);var f=t.create("node","TeXAtom",[],{texClass:m.TEXCLASS.OP,movesupsub:!0,movablelimits:!0});o&&(n&&f.appendChild(n),f.appendChild(o)),f.appendChild(p),u&&f.appendChild(u),t.Push(f)},e.AmsMethods.operatorLetter=function(t,e){return!!t.stack.env.operatorLetters&&s.default.variable(t,e)},e.AmsMethods.MultiIntegral=function(t,e,r){var n=t.GetNext();if("\\"===n){var a=t.i;n=t.GetArgument(e),t.i=a,"\\limits"===n&&(r="\\idotsint"===e?"\\!\\!\\mathop{\\,\\,"+r+"}":"\\!\\!\\!\\mathop{\\,\\,\\,"+r+"}")}t.string=r+" "+t.string.slice(t.i),t.i=0},e.AmsMethods.xArrow=function(t,e,r,n,a){var o={width:"+"+i.default.Em((n+a)/18),lspace:i.default.Em(n/18)},s=t.GetBrackets(e),c=t.ParseArg(e),d=t.create("node","mspace",[],{depth:".25em"}),p=t.create("token","mo",{stretchy:!0,texClass:m.TEXCLASS.REL},String.fromCodePoint(r));p=t.create("node","mstyle",[p],{scriptlevel:0});var f=t.create("node","munderover",[p]),h=t.create("node","mpadded",[c,d],o);if(l.default.setAttribute(h,"voffset","-.2em"),l.default.setAttribute(h,"height","-.2em"),l.default.setChild(f,f.over,h),s){var g=new u.default(s,t.stack.env,t.configuration).mml(),v=t.create("node","mspace",[],{height:".75em"});h=t.create("node","mpadded",[g,v],o),l.default.setAttribute(h,"voffset",".15em"),l.default.setAttribute(h,"depth","-.15em"),l.default.setChild(f,f.under,h)}l.default.setProperty(f,"subsupOK",!0),t.Push(f)},e.AmsMethods.HandleShove=function(t,e,r){var n=t.stack.Top();if("multline"!==n.kind)throw new d.default("CommandOnlyAllowedInEnv","%1 only allowed in %2 environment",t.currentCS,"multline");if(n.Size())throw new d.default("CommandAtTheBeginingOfLine","%1 must come at the beginning of the line",t.currentCS);n.setProperty("shove",r)},e.AmsMethods.CFrac=function(t,e){var r=i.default.trimSpaces(t.GetBrackets(e,"")),n=t.GetArgument(e),a=t.GetArgument(e),o={l:c.TexConstant.Align.LEFT,r:c.TexConstant.Align.RIGHT,"":""},s=new u.default("\\strut\\textstyle{"+n+"}",t.stack.env,t.configuration).mml(),p=new u.default("\\strut\\textstyle{"+a+"}",t.stack.env,t.configuration).mml(),f=t.create("node","mfrac",[s,p]);if(null==(r=o[r]))throw new d.default("IllegalAlign","Illegal alignment specified in %1",t.currentCS);r&&l.default.setProperties(f,{numalign:r,denomalign:r}),t.Push(f)},e.AmsMethods.Genfrac=function(t,e,r,n,a,o){null==r&&(r=t.GetDelimiterArg(e)),null==n&&(n=t.GetDelimiterArg(e)),null==a&&(a=t.GetArgument(e)),null==o&&(o=i.default.trimSpaces(t.GetArgument(e)));var s=t.ParseArg(e),c=t.ParseArg(e),u=t.create("node","mfrac",[s,c]);if(""!==a&&l.default.setAttribute(u,"linethickness",a),(r||n)&&(l.default.setProperty(u,"withDelims",!0),u=i.default.fixedFence(t.configuration,r,u,n)),""!==o){var p=parseInt(o,10),f=["D","T","S","SS"][p];if(null==f)throw new d.default("BadMathStyleFor","Bad math style for %1",t.currentCS);u=t.create("node","mstyle",[u]),"D"===f?l.default.setProperties(u,{displaystyle:!0,scriptlevel:0}):l.default.setProperties(u,{displaystyle:!1,scriptlevel:p-1})}t.Push(u)},e.AmsMethods.HandleTag=function(t,e){if(!t.tags.currentTag.taggable&&t.tags.env)throw new d.default("CommandNotAllowedInEnv","%1 not allowed in %2 environment",t.currentCS,t.tags.env);if(t.tags.currentTag.tag)throw new d.default("MultipleCommand","Multiple %1",t.currentCS);var r=t.GetStar(),n=i.default.trimSpaces(t.GetArgument(e));t.tags.tag(n,r)},e.AmsMethods.HandleNoTag=f.default.HandleNoTag,e.AmsMethods.HandleRef=f.default.HandleRef,e.AmsMethods.Macro=f.default.Macro,e.AmsMethods.Accent=f.default.Accent,e.AmsMethods.Tilde=f.default.Tilde,e.AmsMethods.Array=f.default.Array,e.AmsMethods.Spacer=f.default.Spacer,e.AmsMethods.NamedOp=f.default.NamedOp,e.AmsMethods.EqnArray=f.default.EqnArray,e.AmsMethods.Equation=f.default.Equation},6769:function(t,e,r){Object.defineProperty(e,"__esModule",{value:!0}),e.AmsCdConfiguration=void 0;var n=r(251);r(8704),e.AmsCdConfiguration=n.Configuration.create("amscd",{handler:{character:["amscd_special"],macro:["amscd_macros"],environment:["amscd_environment"]},options:{amscd:{colspace:"5pt",rowspace:"5pt",harrowsize:"2.75em",varrowsize:"1.75em",hideHorizontalLabels:!1}}})},8704:function(t,e,r){var n=this&&this.__createBinding||(Object.create?function(t,e,r,n){void 0===n&&(n=r);var a=Object.getOwnPropertyDescriptor(e,r);a&&!("get"in a?!e.__esModule:a.writable||a.configurable)||(a={enumerable:!0,get:function(){return e[r]}}),Object.defineProperty(t,n,a)}:function(t,e,r,n){void 0===n&&(n=r),t[n]=e[r]}),a=this&&this.__setModuleDefault||(Object.create?function(t,e){Object.defineProperty(t,"default",{enumerable:!0,value:e})}:function(t,e){t.default=e}),o=this&&this.__importStar||function(t){if(t&&t.__esModule)return t;var e={};if(null!=t)for(var r in t)"default"!==r&&Object.prototype.hasOwnProperty.call(t,r)&&n(e,t,r);return a(e,t),e},i=this&&this.__importDefault||function(t){return t&&t.__esModule?t:{default:t}};Object.defineProperty(e,"__esModule",{value:!0});var s=o(r(5871)),l=i(r(4945)),c=i(r(8834));new s.EnvironmentMap("amscd_environment",l.default.environment,{CD:"CD"},c.default),new s.CommandMap("amscd_macros",{minCDarrowwidth:"minCDarrowwidth",minCDarrowheight:"minCDarrowheight"},c.default),new 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+/*!
+ *************************************************************************
+ *
+ *  mhchemParser.ts
+ *  4.1.1
+ *
+ *  Parser for the \ce command and \pu command for MathJax and Co.
+ *
+ *  mhchem's \ce is a tool for writing beautiful chemical equations easily.
+ *  mhchem's \pu is a tool for writing physical units easily.
+ *
+ *  ----------------------------------------------------------------------
+ *
+ *  Copyright (c) 2015-2021 Martin Hensel
+ *
+ *  Licensed under the Apache License, Version 2.0 (the "License");
+ *  you may not use this file except in compliance with the License.
+ *  You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ *  Unless required by applicable law or agreed to in writing, software
+ *  distributed under the License is distributed on an "AS IS" BASIS,
+ *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ *  See the License for the specific language governing permissions and
+ *  limitations under the License.
+ *
+ *  ----------------------------------------------------------------------
+ *
+ *  https://github.com/mhchem/mhchemParser
+ *
+ */
+Object.defineProperty(e,"__esModule",{value:!0}),e.mhchemParser=void 0;var r=function(){function t(){}return t.toTex=function(t,e){return o.go(a.go(t,e),"tex"!==e)},t}();function n(t){var e,r,n={};for(e in t)for(r in t[e]){var a=r.split("|");t[e][r].stateArray=a;for(var o=0;o<a.length;o++)n[a[o]]=[]}for(e in t)for(r in t[e])for(a=t[e][r].stateArray||[],o=0;o<a.length;o++){var i=t[e][r];i.action_=[].concat(i.action_);for(var s=0;s<i.action_.length;s++)"string"==typeof i.action_[s]&&(i.action_[s]={type_:i.action_[s]});for(var l=e.split("|"),c=0;c<l.length;c++)if("*"===a[o]){var u=void 0;for(u in n)n[u].push({pattern:l[c],task:i})}else n[a[o]].push({pattern:l[c],task:i})}return n}e.mhchemParser=r;var a={go:function(t,e){if(!t)return[];void 0===e&&(e="ce");var r,n="0",o={};o.parenthesisLevel=0,t=(t=(t=t.replace(/\n/g," ")).replace(/[\u2212\u2013\u2014\u2010]/g,"-")).replace(/[\u2026]/g,"...");for(var i=10,s=[];;){r!==t?(i=10,r=t):i--;var l=a.stateMachines[e],c=l.transitions[n]||l.transitions["*"];t:for(var u=0;u<c.length;u++){var d=a.patterns.match_(c[u].pattern,t);if(d){for(var p=c[u].task,f=0;f<p.action_.length;f++){var m=void 0;if(l.actions[p.action_[f].type_])m=l.actions[p.action_[f].type_](o,d.match_,p.action_[f].option);else{if(!a.actions[p.action_[f].type_])throw["MhchemBugA","mhchem bug A. 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diff --git a/devel/_static/mathjax/input/tex/extensions/ams.js b/devel/_static/mathjax/input/tex/extensions/ams.js
new file mode 100644
index 0000000000..abd4619a90
--- /dev/null
+++ b/devel/_static/mathjax/input/tex/extensions/ams.js
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n(e,t),Object.defineProperty(e.prototype,"kind",{get:function(){return"flalign"},enumerable:!1,configurable:!0}),e.prototype.EndEntry=function(){t.prototype.EndEntry.call(this);var e=this.getProperty("xalignat");if(e&&this.row.length>e)throw new m.default("XalignOverflow","Extra %1 in row of %2","&",this.name)},e.prototype.EndRow=function(){for(var e,a=this.row,r=this.getProperty("xalignat");a.length<r;)a.push(this.create("node","mtd"));for(this.row=[],this.padded&&this.row.push(this.create("node","mtd"));e=a.shift();)this.row.push(e),(e=a.shift())&&this.row.push(e),(a.length||this.padded)&&this.row.push(this.create("node","mtd"));this.row.length>this.maxrow&&(this.maxrow=this.row.length),t.prototype.EndRow.call(this);var n=this.table[this.table.length-1];if(this.getProperty("zeroWidthLabel")&&n.isKind("mlabeledtr")){var l=u.default.getChildren(n)[0],o=this.factory.configuration.options.tagSide,s=i({width:0},"right"===o?{lspace:"-1width"}:{}),m=this.create("node","mpadded",u.default.getChildren(l),s);l.setChildren([m])}},e.prototype.EndTable=function(){(t.prototype.EndTable.call(this),this.center)&&(this.maxrow<=2&&(delete this.arraydef.width,delete this.global.indentalign))},e}(o.EqnArrayItem);e.FlalignItem=h},829:function(t,e,a){var r=this&&this.__createBinding||(Object.create?function(t,e,a,r){void 0===r&&(r=a);var n=Object.getOwnPropertyDescriptor(e,a);n&&!("get"in n?!e.__esModule:n.writable||n.configurable)||(n={enumerable:!0,get:function(){return e[a]}}),Object.defineProperty(t,r,n)}:function(t,e,a,r){void 0===r&&(r=a),t[r]=e[a]}),n=this&&this.__setModuleDefault||(Object.create?function(t,e){Object.defineProperty(t,"default",{enumerable:!0,value:e})}:function(t,e){t.default=e}),i=this&&this.__importStar||function(t){if(t&&t.__esModule)return t;var e={};if(null!=t)for(var a in t)"default"!==a&&Object.prototype.hasOwnProperty.call(t,a)&&r(e,t,a);return n(e,t),e},l=this&&this.__importDefault||function(t){return t&&t.__esModule?t:{default:t}};Object.defineProperty(e,"__esModule",{value:!0});var o=a(16),s=i(a(871)),u=a(108),m=l(a(945)),d=l(a(398)),c=a(801),h=a(230);new s.CharacterMap("AMSmath-mathchar0mo",m.default.mathchar0mo,{iiiint:["\u2a0c",{texClass:c.TEXCLASS.OP}]}),new s.RegExpMap("AMSmath-operatorLetter",o.AmsMethods.operatorLetter,/[-*]/i),new s.CommandMap("AMSmath-macros",{mathring:["Accent","02DA"],nobreakspace:"Tilde",negmedspace:["Spacer",h.MATHSPACE.negativemediummathspace],negthickspace:["Spacer",h.MATHSPACE.negativethickmathspace],idotsint:["MultiIntegral","\\int\\cdots\\int"],dddot:["Accent","20DB"],ddddot:["Accent","20DC"],sideset:"SideSet",boxed:["Macro","\\fbox{$\\displaystyle{#1}$}",1],tag:"HandleTag",notag:"HandleNoTag",eqref:["HandleRef",!0],substack:["Macro","\\begin{subarray}{c}#1\\end{subarray}",1],injlim:["NamedOp","inj&thinsp;lim"],projlim:["NamedOp","proj&thinsp;lim"],varliminf:["Macro","\\mathop{\\underline{\\mmlToken{mi}{lim}}}"],varlimsup:["Macro","\\mathop{\\overline{\\mmlToken{mi}{lim}}}"],varinjlim:["Macro","\\mathop{\\underrightarrow{\\mmlToken{mi}{lim}}}"],varprojlim:["Macro","\\mathop{\\underleftarrow{\\mmlToken{mi}{lim}}}"],DeclareMathOperator:"HandleDeclareOp",operatorname:"HandleOperatorName",genfrac:"Genfrac",frac:["Genfrac","","","",""],tfrac:["Genfrac","","","","1"],dfrac:["Genfrac","","","","0"],binom:["Genfrac","(",")","0",""],tbinom:["Genfrac","(",")","0","1"],dbinom:["Genfrac","(",")","0","0"],cfrac:"CFrac",shoveleft:["HandleShove",u.TexConstant.Align.LEFT],shoveright:["HandleShove",u.TexConstant.Align.RIGHT],xrightarrow:["xArrow",8594,5,10],xleftarrow:["xArrow",8592,10,5]},o.AmsMethods),new s.EnvironmentMap("AMSmath-environment",m.default.environment,{"equation*":["Equation",null,!1],"eqnarray*":["EqnArray",null,!1,!0,"rcl",d.default.cols(0,h.MATHSPACE.thickmathspace),".5em"],align:["EqnArray",null,!0,!0,"rl",d.default.cols(0,2)],"align*":["EqnArray",null,!1,!0,"rl",d.default.cols(0,2)],multline:["Multline",null,!0],"multline*":["Multline",null,!1],split:["EqnArray",null,!1,!1,"rl",d.default.cols(0)],gather:["EqnArray",null,!0,!0,"c"],"gather*":["EqnArray",null,!1,!0,"c"],alignat:["AlignAt",null,!0,!0],"alignat*":["AlignAt",null,!1,!0],alignedat:["AlignAt",null,!1,!1],aligned:["AmsEqnArray",null,null,null,"rl",d.default.cols(0,2),".5em","D"],gathered:["AmsEqnArray",null,null,null,"c",null,".5em","D"],xalignat:["XalignAt",null,!0,!0],"xalignat*":["XalignAt",null,!1,!0],xxalignat:["XalignAt",null,!1,!1],flalign:["FlalignArray",null,!0,!1,!0,"rlc","auto auto fit"],"flalign*":["FlalignArray",null,!1,!1,!0,"rlc","auto auto fit"],subarray:["Array",null,null,null,null,d.default.cols(0),"0.1em","S",1],smallmatrix:["Array",null,null,null,"c",d.default.cols(1/3),".2em","S",1],matrix:["Array",null,null,null,"c"],pmatrix:["Array",null,"(",")","c"],bmatrix:["Array",null,"[","]","c"],Bmatrix:["Array",null,"\\{","\\}","c"],vmatrix:["Array",null,"\\vert","\\vert","c"],Vmatrix:["Array",null,"\\Vert","\\Vert","c"],cases:["Array",null,"\\{",".","ll",null,".2em","T"]},o.AmsMethods),new s.DelimiterMap("AMSmath-delimiter",m.default.delimiter,{"\\lvert":["|",{texClass:c.TEXCLASS.OPEN}],"\\rvert":["|",{texClass:c.TEXCLASS.CLOSE}],"\\lVert":["\u2016",{texClass:c.TEXCLASS.OPEN}],"\\rVert":["\u2016",{texClass:c.TEXCLASS.CLOSE}]}),new s.CharacterMap("AMSsymbols-mathchar0mi",m.default.mathchar0mi,{digamma:"\u03dd",varkappa:"\u03f0",varGamma:["\u0393",{mathvariant:u.TexConstant.Variant.ITALIC}],varDelta:["\u0394",{mathvariant:u.TexConstant.Variant.ITALIC}],varTheta:["\u0398",{mathvariant:u.TexConstant.Variant.ITALIC}],varLambda:["\u039b",{mathvariant:u.TexConstant.Variant.ITALIC}],varXi:["\u039e",{mathvariant:u.TexConstant.Variant.ITALIC}],varPi:["\u03a0",{mathvariant:u.TexConstant.Variant.ITALIC}],varSigma:["\u03a3",{mathvariant:u.TexConstant.Variant.ITALIC}],varUpsilon:["\u03a5",{mathvariant:u.TexConstant.Variant.ITALIC}],varPhi:["\u03a6",{mathvariant:u.TexConstant.Variant.ITALIC}],varPsi:["\u03a8",{mathvariant:u.TexConstant.Variant.ITALIC}],varOmega:["\u03a9",{mathvariant:u.TexConstant.Variant.ITALIC}],beth:"\u2136",gimel:"\u2137",daleth:"\u2138",backprime:["\u2035",{variantForm:!0}],hslash:"\u210f",varnothing:["\u2205",{variantForm:!0}],blacktriangle:"\u25b4",triangledown:["\u25bd",{variantForm:!0}],blacktriangledown:"\u25be",square:"\u25fb",Box:"\u25fb",blacksquare:"\u25fc",lozenge:"\u25ca",Diamond:"\u25ca",blacklozenge:"\u29eb",circledS:["\u24c8",{mathvariant:u.TexConstant.Variant.NORMAL}],bigstar:"\u2605",sphericalangle:"\u2222",measuredangle:"\u2221",nexists:"\u2204",complement:"\u2201",mho:"\u2127",eth:["\xf0",{mathvariant:u.TexConstant.Variant.NORMAL}],Finv:"\u2132",diagup:"\u2571",Game:"\u2141",diagdown:"\u2572",Bbbk:["k",{mathvariant:u.TexConstant.Variant.DOUBLESTRUCK}],yen:"\xa5",circledR:"\xae",checkmark:"\u2713",maltese:"\u2720"}),new s.CharacterMap("AMSsymbols-mathchar0mo",m.default.mathchar0mo,{dotplus:"\u2214",ltimes:"\u22c9",smallsetminus:["\u2216",{variantForm:!0}],rtimes:"\u22ca",Cap:"\u22d2",doublecap:"\u22d2",leftthreetimes:"\u22cb",Cup:"\u22d3",doublecup:"\u22d3",rightthreetimes:"\u22cc",barwedge:"\u22bc",curlywedge:"\u22cf",veebar:"\u22bb",curlyvee:"\u22ce",doublebarwedge:"\u2a5e",boxminus:"\u229f",circleddash:"\u229d",boxtimes:"\u22a0",circledast:"\u229b",boxdot:"\u22a1",circledcirc:"\u229a",boxplus:"\u229e",centerdot:["\u22c5",{variantForm:!0}],divideontimes:"\u22c7",intercal:"\u22ba",leqq:"\u2266",geqq:"\u2267",leqslant:"\u2a7d",geqslant:"\u2a7e",eqslantless:"\u2a95",eqslantgtr:"\u2a96",lesssim:"\u2272",gtrsim:"\u2273",lessapprox:"\u2a85",gtrapprox:"\u2a86",approxeq:"\u224a",lessdot:"\u22d6",gtrdot:"\u22d7",lll:"\u22d8",llless:"\u22d8",ggg:"\u22d9",gggtr:"\u22d9",lessgtr:"\u2276",gtrless:"\u2277",lesseqgtr:"\u22da",gtreqless:"\u22db",lesseqqgtr:"\u2a8b",gtreqqless:"\u2a8c",doteqdot:"\u2251",Doteq:"\u2251",eqcirc:"\u2256",risingdotseq:"\u2253",circeq:"\u2257",fallingdotseq:"\u2252",triangleq:"\u225c",backsim:"\u223d",thicksim:["\u223c",{variantForm:!0}],backsimeq:"\u22cd",thickapprox:["\u2248",{variantForm:!0}],subseteqq:"\u2ac5",supseteqq:"\u2ac6",Subset:"\u22d0",Supset:"\u22d1",sqsubset:"\u228f",sqsupset:"\u2290",preccurlyeq:"\u227c",succcurlyeq:"\u227d",curlyeqprec:"\u22de",curlyeqsucc:"\u22df",precsim:"\u227e",succsim:"\u227f",precapprox:"\u2ab7",succapprox:"\u2ab8",vartriangleleft:"\u22b2",lhd:"\u22b2",vartriangleright:"\u22b3",rhd:"\u22b3",trianglelefteq:"\u22b4",unlhd:"\u22b4",trianglerighteq:"\u22b5",unrhd:"\u22b5",vDash:["\u22a8",{variantForm:!0}],Vdash:"\u22a9",Vvdash:"\u22aa",smallsmile:["\u2323",{variantForm:!0}],shortmid:["\u2223",{variantForm:!0}],smallfrown:["\u2322",{variantForm:!0}],shortparallel:["\u2225",{variantForm:!0}],bumpeq:"\u224f",between:"\u226c",Bumpeq:"\u224e",pitchfork:"\u22d4",varpropto:["\u221d",{variantForm:!0}],backepsilon:"\u220d",blacktriangleleft:"\u25c2",blacktriangleright:"\u25b8",therefore:"\u2234",because:"\u2235",eqsim:"\u2242",vartriangle:["\u25b3",{variantForm:!0}],Join:"\u22c8",nless:"\u226e",ngtr:"\u226f",nleq:"\u2270",ngeq:"\u2271",nleqslant:["\u2a87",{variantForm:!0}],ngeqslant:["\u2a88",{variantForm:!0}],nleqq:["\u2270",{variantForm:!0}],ngeqq:["\u2271",{variantForm:!0}],lneq:"\u2a87",gneq:"\u2a88",lneqq:"\u2268",gneqq:"\u2269",lvertneqq:["\u2268",{variantForm:!0}],gvertneqq:["\u2269",{variantForm:!0}],lnsim:"\u22e6",gnsim:"\u22e7",lnapprox:"\u2a89",gnapprox:"\u2a8a",nprec:"\u2280",nsucc:"\u2281",npreceq:["\u22e0",{variantForm:!0}],nsucceq:["\u22e1",{variantForm:!0}],precneqq:"\u2ab5",succneqq:"\u2ab6",precnsim:"\u22e8",succnsim:"\u22e9",precnapprox:"\u2ab9",succnapprox:"\u2aba",nsim:"\u2241",ncong:"\u2247",nshortmid:["\u2224",{variantForm:!0}],nshortparallel:["\u2226",{variantForm:!0}],nmid:"\u2224",nparallel:"\u2226",nvdash:"\u22ac",nvDash:"\u22ad",nVdash:"\u22ae",nVDash:"\u22af",ntriangleleft:"\u22ea",ntriangleright:"\u22eb",ntrianglelefteq:"\u22ec",ntrianglerighteq:"\u22ed",nsubseteq:"\u2288",nsupseteq:"\u2289",nsubseteqq:["\u2288",{variantForm:!0}],nsupseteqq:["\u2289",{variantForm:!0}],subsetneq:"\u228a",supsetneq:"\u228b",varsubsetneq:["\u228a",{variantForm:!0}],varsupsetneq:["\u228b",{variantForm:!0}],subsetneqq:"\u2acb",supsetneqq:"\u2acc",varsubsetneqq:["\u2acb",{variantForm:!0}],varsupsetneqq:["\u2acc",{variantForm:!0}],leftleftarrows:"\u21c7",rightrightarrows:"\u21c9",leftrightarrows:"\u21c6",rightleftarrows:"\u21c4",Lleftarrow:"\u21da",Rrightarrow:"\u21db",twoheadleftarrow:"\u219e",twoheadrightarrow:"\u21a0",leftarrowtail:"\u21a2",rightarrowtail:"\u21a3",looparrowleft:"\u21ab",looparrowright:"\u21ac",leftrightharpoons:"\u21cb",rightleftharpoons:["\u21cc",{variantForm:!0}],curvearrowleft:"\u21b6",curvearrowright:"\u21b7",circlearrowleft:"\u21ba",circlearrowright:"\u21bb",Lsh:"\u21b0",Rsh:"\u21b1",upuparrows:"\u21c8",downdownarrows:"\u21ca",upharpoonleft:"\u21bf",upharpoonright:"\u21be",downharpoonleft:"\u21c3",restriction:"\u21be",multimap:"\u22b8",downharpoonright:"\u21c2",leftrightsquigarrow:"\u21ad",rightsquigarrow:"\u21dd",leadsto:"\u21dd",dashrightarrow:"\u21e2",dashleftarrow:"\u21e0",nleftarrow:"\u219a",nrightarrow:"\u219b",nLeftarrow:"\u21cd",nRightarrow:"\u21cf",nleftrightarrow:"\u21ae",nLeftrightarrow:"\u21ce"}),new s.DelimiterMap("AMSsymbols-delimiter",m.default.delimiter,{"\\ulcorner":"\u231c","\\urcorner":"\u231d","\\llcorner":"\u231e","\\lrcorner":"\u231f"}),new s.CommandMap("AMSsymbols-macros",{implies:["Macro","\\;\\Longrightarrow\\;"],impliedby:["Macro","\\;\\Longleftarrow\\;"]},o.AmsMethods)},16:function(t,e,a){var r=this&&this.__assign||function(){return r=Object.assign||function(t){for(var e,a=1,r=arguments.length;a<r;a++)for(var n in e=arguments[a])Object.prototype.hasOwnProperty.call(e,n)&&(t[n]=e[n]);return t},r.apply(this,arguments)},n=this&&this.__read||function(t,e){var a="function"==typeof Symbol&&t[Symbol.iterator];if(!a)return t;var r,n,i=a.call(t),l=[];try{for(;(void 0===e||e-- >0)&&!(r=i.next()).done;)l.push(r.value)}catch(t){n={error:t}}finally{try{r&&!r.done&&(a=i.return)&&a.call(i)}finally{if(n)throw n.error}}return l},i=this&&this.__importDefault||function(t){return t&&t.__esModule?t:{default:t}};Object.defineProperty(e,"__esModule",{value:!0}),e.NEW_OPS=e.AmsMethods=void 0;var l=i(a(398)),o=i(a(945)),s=i(a(748)),u=a(108),m=i(a(193)),d=i(a(402)),c=a(924),h=i(a(360)),p=a(801);function f(t){if(!t||t.isInferred&&0===t.childNodes.length)return[null,null];if(t.isKind("msubsup")&&g(t))return[t,null];var e=s.default.getChildAt(t,0);return t.isInferred&&e&&g(e)?(t.childNodes.splice(0,1),[e,t]):[null,t]}function g(t){var e=t.childNodes[0];return e&&e.isKind("mi")&&""===e.getText()}e.AmsMethods={},e.AmsMethods.AmsEqnArray=function(t,e,a,r,n,i,o){var s=t.GetBrackets("\\begin{"+e.getName()+"}"),u=h.default.EqnArray(t,e,a,r,n,i,o);return l.default.setArrayAlign(u,s)},e.AmsMethods.AlignAt=function(t,a,r,n){var i,o,s=a.getName(),u="",m=[];if(n||(o=t.GetBrackets("\\begin{"+s+"}")),(i=t.GetArgument("\\begin{"+s+"}")).match(/[^0-9]/))throw new d.default("PositiveIntegerArg","Argument to %1 must me a positive integer","\\begin{"+s+"}");for(var c=parseInt(i,10);c>0;)u+="rl",m.push("0em 0em"),c--;var h=m.join(" ");if(n)return e.AmsMethods.EqnArray(t,a,r,n,u,h);var p=e.AmsMethods.EqnArray(t,a,r,n,u,h);return l.default.setArrayAlign(p,o)},e.AmsMethods.Multline=function(t,e,a){t.Push(e),l.default.checkEqnEnv(t);var r=t.itemFactory.create("multline",a,t.stack);return r.arraydef={displaystyle:!0,rowspacing:".5em",columnspacing:"100%",width:t.options.ams.multlineWidth,side:t.options.tagSide,minlabelspacing:t.options.tagIndent,framespacing:t.options.ams.multlineIndent+" 0",frame:"","data-width-includes-label":!0},r},e.AmsMethods.XalignAt=function(t,a,r,n){var i=t.GetArgument("\\begin{"+a.getName()+"}");if(i.match(/[^0-9]/))throw new d.default("PositiveIntegerArg","Argument to %1 must me a positive integer","\\begin{"+a.getName()+"}");var l=n?"crl":"rlc",o=n?"fit auto auto":"auto auto fit",s=e.AmsMethods.FlalignArray(t,a,r,n,!1,l,o,!0);return s.setProperty("xalignat",2*parseInt(i)),s},e.AmsMethods.FlalignArray=function(t,e,a,r,n,i,o,s){void 0===s&&(s=!1),t.Push(e),l.default.checkEqnEnv(t),i=i.split("").join(" ").replace(/r/g,"right").replace(/l/g,"left").replace(/c/g,"center");var u=t.itemFactory.create("flalign",e.getName(),a,r,n,t.stack);return u.arraydef={width:"100%",displaystyle:!0,columnalign:i,columnspacing:"0em",columnwidth:o,rowspacing:"3pt",side:t.options.tagSide,minlabelspacing:s?"0":t.options.tagIndent,"data-width-includes-label":!0},u.setProperty("zeroWidthLabel",s),u},e.NEW_OPS="ams-declare-ops",e.AmsMethods.HandleDeclareOp=function(t,a){var r=t.GetStar()?"*":"",n=l.default.trimSpaces(t.GetArgument(a));"\\"===n.charAt(0)&&(n=n.substr(1));var i=t.GetArgument(a);t.configuration.handlers.retrieve(e.NEW_OPS).add(n,new c.Macro(n,e.AmsMethods.Macro,["\\operatorname".concat(r,"{").concat(i,"}")]))},e.AmsMethods.HandleOperatorName=function(t,e){var a=t.GetStar(),n=l.default.trimSpaces(t.GetArgument(e)),i=new m.default(n,r(r({},t.stack.env),{font:u.TexConstant.Variant.NORMAL,multiLetterIdentifiers:/^[-*a-z]+/i,operatorLetters:!0}),t.configuration).mml();if(i.isKind("mi")||(i=t.create("node","TeXAtom",[i])),s.default.setProperties(i,{movesupsub:a,movablelimits:!0,texClass:p.TEXCLASS.OP}),!a){var o=t.GetNext(),d=t.i;"\\"===o&&++t.i&&"limits"!==t.GetCS()&&(t.i=d)}t.Push(i)},e.AmsMethods.SideSet=function(t,e){var a=n(f(t.ParseArg(e)),2),r=a[0],i=a[1],o=n(f(t.ParseArg(e)),2),u=o[0],m=o[1],d=t.ParseArg(e),c=d;r&&(i?r.replaceChild(t.create("node","mphantom",[t.create("node","mpadded",[l.default.copyNode(d,t)],{width:0})]),s.default.getChildAt(r,0)):(c=t.create("node","mmultiscripts",[d]),u&&s.default.appendChildren(c,[s.default.getChildAt(u,1)||t.create("node","none"),s.default.getChildAt(u,2)||t.create("node","none")]),s.default.setProperty(c,"scriptalign","left"),s.default.appendChildren(c,[t.create("node","mprescripts"),s.default.getChildAt(r,1)||t.create("node","none"),s.default.getChildAt(r,2)||t.create("node","none")]))),u&&c===d&&(u.replaceChild(d,s.default.getChildAt(u,0)),c=u);var h=t.create("node","TeXAtom",[],{texClass:p.TEXCLASS.OP,movesupsub:!0,movablelimits:!0});i&&(r&&h.appendChild(r),h.appendChild(i)),h.appendChild(c),m&&h.appendChild(m),t.Push(h)},e.AmsMethods.operatorLetter=function(t,e){return!!t.stack.env.operatorLetters&&o.default.variable(t,e)},e.AmsMethods.MultiIntegral=function(t,e,a){var r=t.GetNext();if("\\"===r){var n=t.i;r=t.GetArgument(e),t.i=n,"\\limits"===r&&(a="\\idotsint"===e?"\\!\\!\\mathop{\\,\\,"+a+"}":"\\!\\!\\!\\mathop{\\,\\,\\,"+a+"}")}t.string=a+" "+t.string.slice(t.i),t.i=0},e.AmsMethods.xArrow=function(t,e,a,r,n){var i={width:"+"+l.default.Em((r+n)/18),lspace:l.default.Em(r/18)},o=t.GetBrackets(e),u=t.ParseArg(e),d=t.create("node","mspace",[],{depth:".25em"}),c=t.create("token","mo",{stretchy:!0,texClass:p.TEXCLASS.REL},String.fromCodePoint(a));c=t.create("node","mstyle",[c],{scriptlevel:0});var h=t.create("node","munderover",[c]),f=t.create("node","mpadded",[u,d],i);if(s.default.setAttribute(f,"voffset","-.2em"),s.default.setAttribute(f,"height","-.2em"),s.default.setChild(h,h.over,f),o){var g=new m.default(o,t.stack.env,t.configuration).mml(),M=t.create("node","mspace",[],{height:".75em"});f=t.create("node","mpadded",[g,M],i),s.default.setAttribute(f,"voffset",".15em"),s.default.setAttribute(f,"depth","-.15em"),s.default.setChild(h,h.under,f)}s.default.setProperty(h,"subsupOK",!0),t.Push(h)},e.AmsMethods.HandleShove=function(t,e,a){var r=t.stack.Top();if("multline"!==r.kind)throw new d.default("CommandOnlyAllowedInEnv","%1 only allowed in %2 environment",t.currentCS,"multline");if(r.Size())throw new d.default("CommandAtTheBeginingOfLine","%1 must come at the beginning of the line",t.currentCS);r.setProperty("shove",a)},e.AmsMethods.CFrac=function(t,e){var 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diff --git a/devel/_static/mathjax/input/tex/extensions/amscd.js b/devel/_static/mathjax/input/tex/extensions/amscd.js
new file mode 100644
index 0000000000..79689c6d62
--- /dev/null
+++ b/devel/_static/mathjax/input/tex/extensions/amscd.js
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diff --git a/devel/_static/mathjax/input/tex/extensions/autoload.js b/devel/_static/mathjax/input/tex/extensions/autoload.js
new file mode 100644
index 0000000000..35aff0c908
--- /dev/null
+++ b/devel/_static/mathjax/input/tex/extensions/autoload.js
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/devel/_static/mathjax/input/tex/extensions/bbox.js b/devel/_static/mathjax/input/tex/extensions/bbox.js
new file mode 100644
index 0000000000..effe9de478
--- /dev/null
+++ b/devel/_static/mathjax/input/tex/extensions/bbox.js
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/devel/_static/mathjax/input/tex/extensions/boldsymbol.js b/devel/_static/mathjax/input/tex/extensions/boldsymbol.js
new file mode 100644
index 0000000000..1e61f33c04
--- /dev/null
+++ b/devel/_static/mathjax/input/tex/extensions/boldsymbol.js
@@ -0,0 +1 @@
+!function(){"use strict";var t,a,o,e={667:function(t,a){a.q=void 0,a.q="3.2.2"},986:function(t,a,o){var e=this&&this.__values||function(t){var a="function"==typeof Symbol&&Symbol.iterator,o=a&&t[a],e=0;if(o)return o.call(t);if(t&&"number"==typeof t.length)return{next:function(){return t&&e>=t.length&&(t=void 0),{value:t&&t[e++],done:!t}}};throw new TypeError(a?"Object is not iterable.":"Symbol.iterator is not defined.")},n=this&&this.__importDefault||function(t){return t&&t.__esModule?t:{default:t}};Object.defineProperty(a,"__esModule",{value:!0}),a.BoldsymbolConfiguration=a.rewriteBoldTokens=a.createBoldToken=a.BoldsymbolMethods=void 0;var r=o(251),i=n(o(748)),l=o(108),s=o(871),u=o(348),d={};function x(t,a,o,e){var n=u.NodeFactory.createToken(t,a,o,e);return"mtext"!==a&&t.configuration.parser.stack.env.boldsymbol&&(i.default.setProperty(n,"fixBold",!0),t.configuration.addNode("fixBold",n)),n}function p(t){var a,o;try{for(var n=e(t.data.getList("fixBold")),r=n.next();!r.done;r=n.next()){var s=r.value;if(i.default.getProperty(s,"fixBold")){var u=i.default.getAttribute(s,"mathvariant");null==u?i.default.setAttribute(s,"mathvariant",l.TexConstant.Variant.BOLD):i.default.setAttribute(s,"mathvariant",d[u]||u),i.default.removeProperties(s,"fixBold")}}}catch(t){a={error:t}}finally{try{r&&!r.done&&(o=n.return)&&o.call(n)}finally{if(a)throw a.error}}}d[l.TexConstant.Variant.NORMAL]=l.TexConstant.Variant.BOLD,d[l.TexConstant.Variant.ITALIC]=l.TexConstant.Variant.BOLDITALIC,d[l.TexConstant.Variant.FRAKTUR]=l.TexConstant.Variant.BOLDFRAKTUR,d[l.TexConstant.Variant.SCRIPT]=l.TexConstant.Variant.BOLDSCRIPT,d[l.TexConstant.Variant.SANSSERIF]=l.TexConstant.Variant.BOLDSANSSERIF,d["-tex-calligraphic"]="-tex-bold-calligraphic",d["-tex-oldstyle"]="-tex-bold-oldstyle",d["-tex-mathit"]=l.TexConstant.Variant.BOLDITALIC,a.BoldsymbolMethods={},a.BoldsymbolMethods.Boldsymbol=function(t,a){var o=t.stack.env.boldsymbol;t.stack.env.boldsymbol=!0;var e=t.ParseArg(a);t.stack.env.boldsymbol=o,t.Push(e)},new s.CommandMap("boldsymbol",{boldsymbol:"Boldsymbol"},a.BoldsymbolMethods),a.createBoldToken=x,a.rewriteBoldTokens=p,a.BoldsymbolConfiguration=r.Configuration.create("boldsymbol",{handler:{macro:["boldsymbol"]},nodes:{token:x},postprocessors:[p]})},955:function(t,a){MathJax._.components.global.isObject,MathJax._.components.global.combineConfig,MathJax._.components.global.combineDefaults,a.r8=MathJax._.components.global.combineWithMathJax,MathJax._.components.global.MathJax},251:function(t,a){Object.defineProperty(a,"__esModule",{value:!0}),a.Configuration=MathJax._.input.tex.Configuration.Configuration,a.ConfigurationHandler=MathJax._.input.tex.Configuration.ConfigurationHandler,a.ParserConfiguration=MathJax._.input.tex.Configuration.ParserConfiguration},348:function(t,a){Object.defineProperty(a,"__esModule",{value:!0}),a.NodeFactory=MathJax._.input.tex.NodeFactory.NodeFactory},748:function(t,a){Object.defineProperty(a,"__esModule",{value:!0}),a.default=MathJax._.input.tex.NodeUtil.default},871:function(t,a){Object.defineProperty(a,"__esModule",{value:!0}),a.parseResult=MathJax._.input.tex.SymbolMap.parseResult,a.AbstractSymbolMap=MathJax._.input.tex.SymbolMap.AbstractSymbolMap,a.RegExpMap=MathJax._.input.tex.SymbolMap.RegExpMap,a.AbstractParseMap=MathJax._.input.tex.SymbolMap.AbstractParseMap,a.CharacterMap=MathJax._.input.tex.SymbolMap.CharacterMap,a.DelimiterMap=MathJax._.input.tex.SymbolMap.DelimiterMap,a.MacroMap=MathJax._.input.tex.SymbolMap.MacroMap,a.CommandMap=MathJax._.input.tex.SymbolMap.CommandMap,a.EnvironmentMap=MathJax._.input.tex.SymbolMap.EnvironmentMap},108:function(t,a){Object.defineProperty(a,"__esModule",{value:!0}),a.TexConstant=MathJax._.input.tex.TexConstants.TexConstant}},n={};function r(t){var a=n[t];if(void 0!==a)return a.exports;var o=n[t]={exports:{}};return e[t].call(o.exports,o,o.exports,r),o.exports}t=r(955),a=r(667),o=r(986),MathJax.loader&&MathJax.loader.checkVersion("[tex]/boldsymbol",a.q,"tex-extension"),(0,t.r8)({_:{input:{tex:{boldsymbol:{BoldsymbolConfiguration:o}}}}})}();
\ No newline at end of file
diff --git a/devel/_static/mathjax/input/tex/extensions/braket.js b/devel/_static/mathjax/input/tex/extensions/braket.js
new file mode 100644
index 0000000000..2ee6391ed0
--- /dev/null
+++ b/devel/_static/mathjax/input/tex/extensions/braket.js
@@ -0,0 +1 @@
+!function(){"use strict";var t,e,a,r,o,n={667:function(t,e){e.q=void 0,e.q="3.2.2"},243:function(t,e,a){var r;Object.defineProperty(e,"__esModule",{value:!0}),e.BraketConfiguration=void 0;var o=a(251),n=a(519);a(299),e.BraketConfiguration=o.Configuration.create("braket",{handler:{character:["Braket-characters"],macro:["Braket-macros"]},items:(r={},r[n.BraketItem.prototype.kind]=n.BraketItem,r)})},519:function(t,e,a){var r,o=this&&this.__extends||(r=function(t,e){return r=Object.setPrototypeOf||{__proto__:[]}instanceof Array&&function(t,e){t.__proto__=e}||function(t,e){for(var a in e)Object.prototype.hasOwnProperty.call(e,a)&&(t[a]=e[a])},r(t,e)},function(t,e){if("function"!=typeof e&&null!==e)throw new TypeError("Class extends value "+String(e)+" is not a constructor or null");function a(){this.constructor=t}r(t,e),t.prototype=null===e?Object.create(e):(a.prototype=e.prototype,new a)}),n=this&&this.__importDefault||function(t){return 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\ No newline at end of file
diff --git a/devel/_static/mathjax/input/tex/extensions/bussproofs.js b/devel/_static/mathjax/input/tex/extensions/bussproofs.js
new file mode 100644
index 0000000000..8fa4ad8df5
--- /dev/null
+++ b/devel/_static/mathjax/input/tex/extensions/bussproofs.js
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\ No newline at end of file
diff --git a/devel/_static/mathjax/input/tex/extensions/cancel.js b/devel/_static/mathjax/input/tex/extensions/cancel.js
new file mode 100644
index 0000000000..ad9de67cd2
--- /dev/null
+++ b/devel/_static/mathjax/input/tex/extensions/cancel.js
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/devel/_static/mathjax/input/tex/extensions/cases.js b/devel/_static/mathjax/input/tex/extensions/cases.js
new file mode 100644
index 0000000000..75a2690061
--- /dev/null
+++ b/devel/_static/mathjax/input/tex/extensions/cases.js
@@ -0,0 +1 @@
+!function(){"use strict";var t,e,a,n={667:function(t,e){e.q=void 0,e.q="3.2.2"},530:function(t,e,a){var n,s,r=this&&this.__extends||(n=function(t,e){return n=Object.setPrototypeOf||{__proto__:[]}instanceof Array&&function(t,e){t.__proto__=e}||function(t,e){for(var a in e)Object.prototype.hasOwnProperty.call(e,a)&&(t[a]=e[a])},n(t,e)},function(t,e){if("function"!=typeof e&&null!==e)throw new TypeError("Class extends value "+String(e)+" is not a constructor or null");function a(){this.constructor=t}n(t,e),t.prototype=null===e?Object.create(e):(a.prototype=e.prototype,new a)}),o=this&&this.__importDefault||function(t){return t&&t.__esModule?t:{default:t}};Object.defineProperty(e,"__esModule",{value:!0}),e.CasesConfiguration=e.CasesMethods=e.CasesTags=e.CasesBeginItem=void 0;var i=a(251),u=a(871),m=o(a(398)),p=o(a(360)),l=o(a(402)),c=a(935),h=a(379),x=a(446),f=function(t){function e(){return null!==t&&t.apply(this,arguments)||this}return 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\ No newline at end of file
diff --git a/devel/_static/mathjax/input/tex/extensions/centernot.js b/devel/_static/mathjax/input/tex/extensions/centernot.js
new file mode 100644
index 0000000000..c72a2dc5c7
--- /dev/null
+++ b/devel/_static/mathjax/input/tex/extensions/centernot.js
@@ -0,0 +1 @@
+!function(){"use strict";var e,t,a,n={667:function(e,t){t.q=void 0,t.q="3.2.2"},286:function(e,t,a){var n=this&&this.__values||function(e){var t="function"==typeof Symbol&&Symbol.iterator,a=t&&e[t],n=0;if(a)return a.call(e);if(e&&"number"==typeof e.length)return{next:function(){return e&&n>=e.length&&(e=void 0),{value:e&&e[n++],done:!e}}};throw new TypeError(t?"Object is not iterable.":"Symbol.iterator is not defined.")},r=this&&this.__importDefault||function(e){return e&&e.__esModule?e:{default:e}};Object.defineProperty(t,"__esModule",{value:!0}),t.CenternotConfiguration=t.filterCenterOver=void 0;var o=a(251),i=r(a(193)),u=r(a(748)),l=a(871),p=r(a(360));function c(e){var t,a,r=e.data;try{for(var o=n(r.getList("centerOver")),i=o.next();!i.done;i=o.next()){var l=i.value,p=u.default.getTexClass(l.childNodes[0].childNodes[0]);null!==p&&u.default.setProperties(l.parent.parent.parent.parent.parent.parent,{texClass:p})}}catch(e){t={error:e}}finally{try{i&&!i.done&&(a=o.return)&&a.call(o)}finally{if(t)throw t.error}}}new l.CommandMap("centernot",{centerOver:"CenterOver",centernot:["Macro","\\centerOver{#1}{{\u29f8}}",1]},{CenterOver:function(e,t){var a="{"+e.GetArgument(t)+"}",n=e.ParseArg(t),r=new i.default(a,e.stack.env,e.configuration).mml(),o=e.create("node","TeXAtom",[new i.default(a,e.stack.env,e.configuration).mml(),e.create("node","mpadded",[e.create("node","mpadded",[n],{width:0,lspace:"-.5width"}),e.create("node","mphantom",[r])],{width:0,lspace:"-.5width"})]);e.configuration.addNode("centerOver",r),e.Push(o)},Macro:p.default.Macro}),t.filterCenterOver=c,t.CenternotConfiguration=o.Configuration.create("centernot",{handler:{macro:["centernot"]},postprocessors:[c]})},955:function(e,t){MathJax._.components.global.isObject,MathJax._.components.global.combineConfig,MathJax._.components.global.combineDefaults,t.r8=MathJax._.components.global.combineWithMathJax,MathJax._.components.global.MathJax},251:function(e,t){Object.defineProperty(t,"__esModule",{value:!0}),t.Configuration=MathJax._.input.tex.Configuration.Configuration,t.ConfigurationHandler=MathJax._.input.tex.Configuration.ConfigurationHandler,t.ParserConfiguration=MathJax._.input.tex.Configuration.ParserConfiguration},748:function(e,t){Object.defineProperty(t,"__esModule",{value:!0}),t.default=MathJax._.input.tex.NodeUtil.default},871:function(e,t){Object.defineProperty(t,"__esModule",{value:!0}),t.parseResult=MathJax._.input.tex.SymbolMap.parseResult,t.AbstractSymbolMap=MathJax._.input.tex.SymbolMap.AbstractSymbolMap,t.RegExpMap=MathJax._.input.tex.SymbolMap.RegExpMap,t.AbstractParseMap=MathJax._.input.tex.SymbolMap.AbstractParseMap,t.CharacterMap=MathJax._.input.tex.SymbolMap.CharacterMap,t.DelimiterMap=MathJax._.input.tex.SymbolMap.DelimiterMap,t.MacroMap=MathJax._.input.tex.SymbolMap.MacroMap,t.CommandMap=MathJax._.input.tex.SymbolMap.CommandMap,t.EnvironmentMap=MathJax._.input.tex.SymbolMap.EnvironmentMap},193:function(e,t){Object.defineProperty(t,"__esModule",{value:!0}),t.default=MathJax._.input.tex.TexParser.default},360:function(e,t){Object.defineProperty(t,"__esModule",{value:!0}),t.default=MathJax._.input.tex.base.BaseMethods.default}},r={};function o(e){var t=r[e];if(void 0!==t)return t.exports;var a=r[e]={exports:{}};return n[e].call(a.exports,a,a.exports,o),a.exports}e=o(955),t=o(667),a=o(286),MathJax.loader&&MathJax.loader.checkVersion("[tex]/centernot",t.q,"tex-extension"),(0,e.r8)({_:{input:{tex:{centernot:{CenternotConfiguration:a}}}}})}();
\ No newline at end of file
diff --git a/devel/_static/mathjax/input/tex/extensions/color.js b/devel/_static/mathjax/input/tex/extensions/color.js
new file mode 100644
index 0000000000..1a12f14b88
--- /dev/null
+++ b/devel/_static/mathjax/input/tex/extensions/color.js
@@ -0,0 +1 @@
+!function(){"use strict";var e,o,t,r,a,n,l={667:function(e,o){o.q=void 0,o.q="3.2.2"},224:function(e,o,t){Object.defineProperty(o,"__esModule",{value:!0}),o.ColorConfiguration=void 0;var r=t(871),a=t(251),n=t(162),l=t(358);new r.CommandMap("color",{color:"Color",textcolor:"TextColor",definecolor:"DefineColor",colorbox:"ColorBox",fcolorbox:"FColorBox"},n.ColorMethods);o.ColorConfiguration=a.Configuration.create("color",{handler:{macro:["color"]},options:{color:{padding:"5px",borderWidth:"2px"}},config:function(e,o){o.parseOptions.packageData.set("color",{model:new l.ColorModel})}})},59:function(e,o){Object.defineProperty(o,"__esModule",{value:!0}),o.COLORS=void 0,o.COLORS=new Map([["Apricot","#FBB982"],["Aquamarine","#00B5BE"],["Bittersweet","#C04F17"],["Black","#221E1F"],["Blue","#2D2F92"],["BlueGreen","#00B3B8"],["BlueViolet","#473992"],["BrickRed","#B6321C"],["Brown","#792500"],["BurntOrange","#F7921D"],["CadetBlue","#74729A"],["CarnationPink","#F282B4"],["Cerulean","#00A2E3"],["CornflowerBlue","#41B0E4"],["Cyan","#00AEEF"],["Dandelion","#FDBC42"],["DarkOrchid","#A4538A"],["Emerald","#00A99D"],["ForestGreen","#009B55"],["Fuchsia","#8C368C"],["Goldenrod","#FFDF42"],["Gray","#949698"],["Green","#00A64F"],["GreenYellow","#DFE674"],["JungleGreen","#00A99A"],["Lavender","#F49EC4"],["LimeGreen","#8DC73E"],["Magenta","#EC008C"],["Mahogany","#A9341F"],["Maroon","#AF3235"],["Melon","#F89E7B"],["MidnightBlue","#006795"],["Mulberry","#A93C93"],["NavyBlue","#006EB8"],["OliveGreen","#3C8031"],["Orange","#F58137"],["OrangeRed","#ED135A"],["Orchid","#AF72B0"],["Peach","#F7965A"],["Periwinkle","#7977B8"],["PineGreen","#008B72"],["Plum","#92268F"],["ProcessBlue","#00B0F0"],["Purple","#99479B"],["RawSienna","#974006"],["Red","#ED1B23"],["RedOrange","#F26035"],["RedViolet","#A1246B"],["Rhodamine","#EF559F"],["RoyalBlue","#0071BC"],["RoyalPurple","#613F99"],["RubineRed","#ED017D"],["Salmon","#F69289"],["SeaGreen","#3FBC9D"],["Sepia","#671800"],["SkyBlue","#46C5DD"],["SpringGreen","#C6DC67"],["Tan","#DA9D76"],["TealBlue","#00AEB3"],["Thistle","#D883B7"],["Turquoise","#00B4CE"],["Violet","#58429B"],["VioletRed","#EF58A0"],["White","#FFFFFF"],["WildStrawberry","#EE2967"],["Yellow","#FFF200"],["YellowGreen","#98CC70"],["YellowOrange","#FAA21A"]])},162:function(e,o,t){var r=this&&this.__importDefault||function(e){return e&&e.__esModule?e:{default:e}};Object.defineProperty(o,"__esModule",{value:!0}),o.ColorMethods=void 0;var a=r(t(748)),n=r(t(398));function l(e){var o="+".concat(e),t=e.replace(/^.*?([a-z]*)$/,"$1"),r=2*parseFloat(o);return{width:"+".concat(r).concat(t),height:o,depth:o,lspace:e}}o.ColorMethods={},o.ColorMethods.Color=function(e,o){var t=e.GetBrackets(o,""),r=e.GetArgument(o),a=e.configuration.packageData.get("color").model.getColor(t,r),n=e.itemFactory.create("style").setProperties({styles:{mathcolor:a}});e.stack.env.color=a,e.Push(n)},o.ColorMethods.TextColor=function(e,o){var t=e.GetBrackets(o,""),r=e.GetArgument(o),a=e.configuration.packageData.get("color").model.getColor(t,r),n=e.stack.env.color;e.stack.env.color=a;var l=e.ParseArg(o);n?e.stack.env.color=n:delete e.stack.env.color;var i=e.create("node","mstyle",[l],{mathcolor:a});e.Push(i)},o.ColorMethods.DefineColor=function(e,o){var t=e.GetArgument(o),r=e.GetArgument(o),a=e.GetArgument(o);e.configuration.packageData.get("color").model.defineColor(r,t,a)},o.ColorMethods.ColorBox=function(e,o){var t=e.GetArgument(o),r=n.default.internalMath(e,e.GetArgument(o)),i=e.configuration.packageData.get("color").model,u=e.create("node","mpadded",r,{mathbackground:i.getColor("named",t)});a.default.setProperties(u,l(e.options.color.padding)),e.Push(u)},o.ColorMethods.FColorBox=function(e,o){var t=e.GetArgument(o),r=e.GetArgument(o),i=n.default.internalMath(e,e.GetArgument(o)),u=e.options.color,d=e.configuration.packageData.get("color").model,c=e.create("node","mpadded",i,{mathbackground:d.getColor("named",r),style:"border: ".concat(u.borderWidth," solid ").concat(d.getColor("named",t))});a.default.setProperties(c,l(u.padding)),e.Push(c)}},358:function(e,o,t){var r=this&&this.__values||function(e){var o="function"==typeof Symbol&&Symbol.iterator,t=o&&e[o],r=0;if(t)return t.call(e);if(e&&"number"==typeof 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\ No newline at end of file
diff --git a/devel/_static/mathjax/input/tex/extensions/colortbl.js b/devel/_static/mathjax/input/tex/extensions/colortbl.js
new file mode 100644
index 0000000000..d034383144
--- /dev/null
+++ b/devel/_static/mathjax/input/tex/extensions/colortbl.js
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/devel/_static/mathjax/input/tex/extensions/colorv2.js b/devel/_static/mathjax/input/tex/extensions/colorv2.js
new file mode 100644
index 0000000000..3f50c7dcae
--- /dev/null
+++ b/devel/_static/mathjax/input/tex/extensions/colorv2.js
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/devel/_static/mathjax/input/tex/extensions/configmacros.js b/devel/_static/mathjax/input/tex/extensions/configmacros.js
new file mode 100644
index 0000000000..aa673a7cde
--- /dev/null
+++ b/devel/_static/mathjax/input/tex/extensions/configmacros.js
@@ -0,0 +1 @@
+!function(){"use strict";var t,a,e,n={667:function(t,a){a.q=void 0,a.q="3.2.2"},359:function(t,a,e){var n,o=this&&this.__values||function(t){var a="function"==typeof Symbol&&Symbol.iterator,e=a&&t[a],n=0;if(e)return e.call(t);if(t&&"number"==typeof t.length)return{next:function(){return t&&n>=t.length&&(t=void 0),{value:t&&t[n++],done:!t}}};throw new TypeError(a?"Object is not iterable.":"Symbol.iterator is not defined.")},i=this&&this.__importDefault||function(t){return t&&t.__esModule?t:{default:t}};Object.defineProperty(a,"__esModule",{value:!0}),a.ConfigMacrosConfiguration=void 0;var r=e(251),p=e(74),l=e(871),s=i(e(945)),u=e(924),c=i(e(432)),M=e(975),x="configmacros-map",f="configmacros-env-map";a.ConfigMacrosConfiguration=r.Configuration.create("configmacros",{init:function(t){new l.CommandMap(x,{},{}),new l.EnvironmentMap(f,s.default.environment,{},{}),t.append(r.Configuration.local({handler:{macro:[x],environment:[f]},priority:3}))},config:function(t,a){!function(t){var a,e,n=t.parseOptions.handlers.retrieve(x),i=t.parseOptions.options.macros;try{for(var r=o(Object.keys(i)),p=r.next();!p.done;p=r.next()){var l=p.value,s="string"==typeof i[l]?[i[l]]:i[l],M=Array.isArray(s[2])?new u.Macro(l,c.default.MacroWithTemplate,s.slice(0,2).concat(s[2])):new u.Macro(l,c.default.Macro,s);n.add(l,M)}}catch(t){a={error:t}}finally{try{p&&!p.done&&(e=r.return)&&e.call(r)}finally{if(a)throw a.error}}}(a),function(t){var a,e,n=t.parseOptions.handlers.retrieve(f),i=t.parseOptions.options.environments;try{for(var r=o(Object.keys(i)),p=r.next();!p.done;p=r.next()){var l=p.value;n.add(l,new u.Macro(l,c.default.BeginEnv,[!0].concat(i[l])))}}catch(t){a={error:t}}finally{try{p&&!p.done&&(e=r.return)&&e.call(r)}finally{if(a)throw a.error}}}(a)},items:(n={},n[M.BeginEnvItem.prototype.kind]=M.BeginEnvItem,n),options:{macros:(0,p.expandable)({}),environments:(0,p.expandable)({})}})},955:function(t,a){MathJax._.components.global.isObject,MathJax._.components.global.combineConfig,MathJax._.components.global.combineDefaults,a.r8=MathJax._.components.global.combineWithMathJax,MathJax._.components.global.MathJax},74:function(t,a){Object.defineProperty(a,"__esModule",{value:!0}),a.isObject=MathJax._.util.Options.isObject,a.APPEND=MathJax._.util.Options.APPEND,a.REMOVE=MathJax._.util.Options.REMOVE,a.OPTIONS=MathJax._.util.Options.OPTIONS,a.Expandable=MathJax._.util.Options.Expandable,a.expandable=MathJax._.util.Options.expandable,a.makeArray=MathJax._.util.Options.makeArray,a.keys=MathJax._.util.Options.keys,a.copy=MathJax._.util.Options.copy,a.insert=MathJax._.util.Options.insert,a.defaultOptions=MathJax._.util.Options.defaultOptions,a.userOptions=MathJax._.util.Options.userOptions,a.selectOptions=MathJax._.util.Options.selectOptions,a.selectOptionsFromKeys=MathJax._.util.Options.selectOptionsFromKeys,a.separateOptions=MathJax._.util.Options.separateOptions,a.lookup=MathJax._.util.Options.lookup},251:function(t,a){Object.defineProperty(a,"__esModule",{value:!0}),a.Configuration=MathJax._.input.tex.Configuration.Configuration,a.ConfigurationHandler=MathJax._.input.tex.Configuration.ConfigurationHandler,a.ParserConfiguration=MathJax._.input.tex.Configuration.ParserConfiguration},945:function(t,a){Object.defineProperty(a,"__esModule",{value:!0}),a.default=MathJax._.input.tex.ParseMethods.default},924:function(t,a){Object.defineProperty(a,"__esModule",{value:!0}),a.Symbol=MathJax._.input.tex.Symbol.Symbol,a.Macro=MathJax._.input.tex.Symbol.Macro},871:function(t,a){Object.defineProperty(a,"__esModule",{value:!0}),a.parseResult=MathJax._.input.tex.SymbolMap.parseResult,a.AbstractSymbolMap=MathJax._.input.tex.SymbolMap.AbstractSymbolMap,a.RegExpMap=MathJax._.input.tex.SymbolMap.RegExpMap,a.AbstractParseMap=MathJax._.input.tex.SymbolMap.AbstractParseMap,a.CharacterMap=MathJax._.input.tex.SymbolMap.CharacterMap,a.DelimiterMap=MathJax._.input.tex.SymbolMap.DelimiterMap,a.MacroMap=MathJax._.input.tex.SymbolMap.MacroMap,a.CommandMap=MathJax._.input.tex.SymbolMap.CommandMap,a.EnvironmentMap=MathJax._.input.tex.SymbolMap.EnvironmentMap},975:function(t,a){Object.defineProperty(a,"__esModule",{value:!0}),a.BeginEnvItem=MathJax._.input.tex.newcommand.NewcommandItems.BeginEnvItem},432:function(t,a){Object.defineProperty(a,"__esModule",{value:!0}),a.default=MathJax._.input.tex.newcommand.NewcommandMethods.default}},o={};function i(t){var a=o[t];if(void 0!==a)return a.exports;var e=o[t]={exports:{}};return n[t].call(e.exports,e,e.exports,i),e.exports}t=i(955),a=i(667),e=i(359),MathJax.loader&&MathJax.loader.checkVersion("[tex]/configmacros",a.q,"tex-extension"),(0,t.r8)({_:{input:{tex:{configmacros:{ConfigMacrosConfiguration:e}}}}})}();
\ No newline at end of file
diff --git a/devel/_static/mathjax/input/tex/extensions/empheq.js b/devel/_static/mathjax/input/tex/extensions/empheq.js
new file mode 100644
index 0000000000..c0c0a7b33c
--- /dev/null
+++ b/devel/_static/mathjax/input/tex/extensions/empheq.js
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/devel/_static/mathjax/input/tex/extensions/enclose.js b/devel/_static/mathjax/input/tex/extensions/enclose.js
new file mode 100644
index 0000000000..5d8e7a79be
--- /dev/null
+++ b/devel/_static/mathjax/input/tex/extensions/enclose.js
@@ -0,0 +1 @@
+!function(){"use strict";var a,t,e,o={667:function(a,t){t.q=void 0,t.q="3.2.2"},272:function(a,t,e){var o=this&&this.__importDefault||function(a){return a&&a.__esModule?a:{default:a}};Object.defineProperty(t,"__esModule",{value:!0}),t.EncloseConfiguration=t.EncloseMethods=t.ENCLOSE_OPTIONS=void 0;var n=e(251),r=e(871),i=o(e(398));t.ENCLOSE_OPTIONS={"data-arrowhead":1,color:1,mathcolor:1,background:1,mathbackground:1,"data-padding":1,"data-thickness":1},t.EncloseMethods={},t.EncloseMethods.Enclose=function(a,e){var o=a.GetArgument(e).replace(/,/g," "),n=a.GetBrackets(e,""),r=a.ParseArg(e),l=i.default.keyvalOptions(n,t.ENCLOSE_OPTIONS);l.notation=o,a.Push(a.create("node","menclose",[r],l))},new r.CommandMap("enclose",{enclose:"Enclose"},t.EncloseMethods),t.EncloseConfiguration=n.Configuration.create("enclose",{handler:{macro:["enclose"]}})},955:function(a,t){MathJax._.components.global.isObject,MathJax._.components.global.combineConfig,MathJax._.components.global.combineDefaults,t.r8=MathJax._.components.global.combineWithMathJax,MathJax._.components.global.MathJax},251:function(a,t){Object.defineProperty(t,"__esModule",{value:!0}),t.Configuration=MathJax._.input.tex.Configuration.Configuration,t.ConfigurationHandler=MathJax._.input.tex.Configuration.ConfigurationHandler,t.ParserConfiguration=MathJax._.input.tex.Configuration.ParserConfiguration},398:function(a,t){Object.defineProperty(t,"__esModule",{value:!0}),t.default=MathJax._.input.tex.ParseUtil.default},871:function(a,t){Object.defineProperty(t,"__esModule",{value:!0}),t.parseResult=MathJax._.input.tex.SymbolMap.parseResult,t.AbstractSymbolMap=MathJax._.input.tex.SymbolMap.AbstractSymbolMap,t.RegExpMap=MathJax._.input.tex.SymbolMap.RegExpMap,t.AbstractParseMap=MathJax._.input.tex.SymbolMap.AbstractParseMap,t.CharacterMap=MathJax._.input.tex.SymbolMap.CharacterMap,t.DelimiterMap=MathJax._.input.tex.SymbolMap.DelimiterMap,t.MacroMap=MathJax._.input.tex.SymbolMap.MacroMap,t.CommandMap=MathJax._.input.tex.SymbolMap.CommandMap,t.EnvironmentMap=MathJax._.input.tex.SymbolMap.EnvironmentMap}},n={};function r(a){var t=n[a];if(void 0!==t)return t.exports;var e=n[a]={exports:{}};return o[a].call(e.exports,e,e.exports,r),e.exports}a=r(955),t=r(667),e=r(272),MathJax.loader&&MathJax.loader.checkVersion("[tex]/enclose",t.q,"tex-extension"),(0,a.r8)({_:{input:{tex:{enclose:{EncloseConfiguration:e}}}}})}();
\ No newline at end of file
diff --git a/devel/_static/mathjax/input/tex/extensions/extpfeil.js b/devel/_static/mathjax/input/tex/extensions/extpfeil.js
new file mode 100644
index 0000000000..63430b5b9c
--- /dev/null
+++ b/devel/_static/mathjax/input/tex/extensions/extpfeil.js
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/devel/_static/mathjax/input/tex/extensions/gensymb.js b/devel/_static/mathjax/input/tex/extensions/gensymb.js
new file mode 100644
index 0000000000..7a83e61743
--- /dev/null
+++ b/devel/_static/mathjax/input/tex/extensions/gensymb.js
@@ -0,0 +1 @@
+!function(){"use strict";var a,t,n,e={667:function(a,t){t.q=void 0,t.q="3.2.2"},82:function(a,t,n){Object.defineProperty(t,"__esModule",{value:!0}),t.GensymbConfiguration=void 0;var e=n(251),o=n(108);new(n(871).CharacterMap)("gensymb-symbols",(function(a,t){var n=t.attributes||{};n.mathvariant=o.TexConstant.Variant.NORMAL,n.class="MathML-Unit";var e=a.create("token","mi",n,t.char);a.Push(e)}),{ohm:"\u2126",degree:"\xb0",celsius:"\u2103",perthousand:"\u2030",micro:"\xb5"}),t.GensymbConfiguration=e.Configuration.create("gensymb",{handler:{macro:["gensymb-symbols"]}})},955:function(a,t){MathJax._.components.global.isObject,MathJax._.components.global.combineConfig,MathJax._.components.global.combineDefaults,t.r8=MathJax._.components.global.combineWithMathJax,MathJax._.components.global.MathJax},251:function(a,t){Object.defineProperty(t,"__esModule",{value:!0}),t.Configuration=MathJax._.input.tex.Configuration.Configuration,t.ConfigurationHandler=MathJax._.input.tex.Configuration.ConfigurationHandler,t.ParserConfiguration=MathJax._.input.tex.Configuration.ParserConfiguration},871:function(a,t){Object.defineProperty(t,"__esModule",{value:!0}),t.parseResult=MathJax._.input.tex.SymbolMap.parseResult,t.AbstractSymbolMap=MathJax._.input.tex.SymbolMap.AbstractSymbolMap,t.RegExpMap=MathJax._.input.tex.SymbolMap.RegExpMap,t.AbstractParseMap=MathJax._.input.tex.SymbolMap.AbstractParseMap,t.CharacterMap=MathJax._.input.tex.SymbolMap.CharacterMap,t.DelimiterMap=MathJax._.input.tex.SymbolMap.DelimiterMap,t.MacroMap=MathJax._.input.tex.SymbolMap.MacroMap,t.CommandMap=MathJax._.input.tex.SymbolMap.CommandMap,t.EnvironmentMap=MathJax._.input.tex.SymbolMap.EnvironmentMap},108:function(a,t){Object.defineProperty(t,"__esModule",{value:!0}),t.TexConstant=MathJax._.input.tex.TexConstants.TexConstant}},o={};function i(a){var t=o[a];if(void 0!==t)return t.exports;var n=o[a]={exports:{}};return e[a](n,n.exports,i),n.exports}a=i(955),t=i(667),n=i(82),MathJax.loader&&MathJax.loader.checkVersion("[tex]/gensymb",t.q,"tex-extension"),(0,a.r8)({_:{input:{tex:{gensymb:{GensymbConfiguration:n}}}}})}();
\ No newline at end of file
diff --git a/devel/_static/mathjax/input/tex/extensions/html.js b/devel/_static/mathjax/input/tex/extensions/html.js
new file mode 100644
index 0000000000..2cefbebcf7
--- /dev/null
+++ b/devel/_static/mathjax/input/tex/extensions/html.js
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new file mode 100644
index 0000000000..99cc630361
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diff --git a/devel/_static/mathjax/input/tex/extensions/mhchem.js b/devel/_static/mathjax/input/tex/extensions/mhchem.js
new file mode 100644
index 0000000000..d953ef72a6
--- /dev/null
+++ b/devel/_static/mathjax/input/tex/extensions/mhchem.js
@@ -0,0 +1,34 @@
+!function(){"use strict";var t,e,o,n={667:function(t,e){e.q=void 0,e.q="3.2.2"},78:function(t,e,o){var n=this&&this.__importDefault||function(t){return t&&t.__esModule?t:{default:t}};Object.defineProperty(e,"__esModule",{value:!0}),e.MhchemConfiguration=void 0;var a=o(251),r=o(871),i=n(o(402)),p=n(o(360)),c=o(939),u=o(652),s={};s.Macro=p.default.Macro,s.xArrow=c.AmsMethods.xArrow,s.Machine=function(t,e,o){var n,a=t.GetArgument(e);try{n=u.mhchemParser.toTex(a,o)}catch(t){throw new i.default(t[0],t[1])}t.string=n+t.string.substr(t.i),t.i=0},new r.CommandMap("mhchem",{ce:["Machine","ce"],pu:["Machine","pu"],longrightleftharpoons:["Macro","\\stackrel{\\textstyle{-}\\!\\!{\\rightharpoonup}}{\\smash{{\\leftharpoondown}\\!\\!{-}}}"],longRightleftharpoons:["Macro","\\stackrel{\\textstyle{-}\\!\\!{\\rightharpoonup}}{\\smash{\\leftharpoondown}}"],longLeftrightharpoons:["Macro","\\stackrel{\\textstyle\\vphantom{{-}}{\\rightharpoonup}}{\\smash{{\\leftharpoondown}\\!\\!{-}}}"],longleftrightarrows:["Macro","\\stackrel{\\longrightarrow}{\\smash{\\longleftarrow}\\Rule{0px}{.25em}{0px}}"],tripledash:["Macro","\\vphantom{-}\\raise2mu{\\kern2mu\\tiny\\text{-}\\kern1mu\\text{-}\\kern1mu\\text{-}\\kern2mu}"],xleftrightarrow:["xArrow",8596,6,6],xrightleftharpoons:["xArrow",8652,5,7],xRightleftharpoons:["xArrow",8652,5,7],xLeftrightharpoons:["xArrow",8652,5,7]},s),e.MhchemConfiguration=a.Configuration.create("mhchem",{handler:{macro:["mhchem"]}})},955:function(t,e){MathJax._.components.global.isObject,MathJax._.components.global.combineConfig,MathJax._.components.global.combineDefaults,e.r8=MathJax._.components.global.combineWithMathJax,MathJax._.components.global.MathJax},251:function(t,e){Object.defineProperty(e,"__esModule",{value:!0}),e.Configuration=MathJax._.input.tex.Configuration.Configuration,e.ConfigurationHandler=MathJax._.input.tex.Configuration.ConfigurationHandler,e.ParserConfiguration=MathJax._.input.tex.Configuration.ParserConfiguration},871:function(t,e){Object.defineProperty(e,"__esModule",{value:!0}),e.parseResult=MathJax._.input.tex.SymbolMap.parseResult,e.AbstractSymbolMap=MathJax._.input.tex.SymbolMap.AbstractSymbolMap,e.RegExpMap=MathJax._.input.tex.SymbolMap.RegExpMap,e.AbstractParseMap=MathJax._.input.tex.SymbolMap.AbstractParseMap,e.CharacterMap=MathJax._.input.tex.SymbolMap.CharacterMap,e.DelimiterMap=MathJax._.input.tex.SymbolMap.DelimiterMap,e.MacroMap=MathJax._.input.tex.SymbolMap.MacroMap,e.CommandMap=MathJax._.input.tex.SymbolMap.CommandMap,e.EnvironmentMap=MathJax._.input.tex.SymbolMap.EnvironmentMap},402:function(t,e){Object.defineProperty(e,"__esModule",{value:!0}),e.default=MathJax._.input.tex.TexError.default},360:function(t,e){Object.defineProperty(e,"__esModule",{value:!0}),e.default=MathJax._.input.tex.base.BaseMethods.default},939:function(t,e){Object.defineProperty(e,"__esModule",{value:!0}),e.AmsMethods=MathJax._.input.tex.ams.AmsMethods.AmsMethods,e.NEW_OPS=MathJax._.input.tex.ams.AmsMethods.NEW_OPS},652:function(t,e){
+/*!
+ *************************************************************************
+ *
+ *  mhchemParser.ts
+ *  4.1.1
+ *
+ *  Parser for the \ce command and \pu command for MathJax and Co.
+ *
+ *  mhchem's \ce is a tool for writing beautiful chemical equations easily.
+ *  mhchem's \pu is a tool for writing physical units easily.
+ *
+ *  ----------------------------------------------------------------------
+ *
+ *  Copyright (c) 2015-2021 Martin Hensel
+ *
+ *  Licensed under the Apache License, Version 2.0 (the "License");
+ *  you may not use this file except in compliance with the License.
+ *  You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ *  Unless required by applicable law or agreed to in writing, software
+ *  distributed under the License is distributed on an "AS IS" BASIS,
+ *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ *  See the License for the specific language governing permissions and
+ *  limitations under the License.
+ *
+ *  ----------------------------------------------------------------------
+ *
+ *  https://github.com/mhchem/mhchemParser
+ *
+ */
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diff --git a/devel/_static/mathjax/input/tex/extensions/newcommand.js b/devel/_static/mathjax/input/tex/extensions/newcommand.js
new file mode 100644
index 0000000000..ea84f3347f
--- /dev/null
+++ b/devel/_static/mathjax/input/tex/extensions/newcommand.js
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/devel/_static/mathjax/input/tex/extensions/noerrors.js b/devel/_static/mathjax/input/tex/extensions/noerrors.js
new file mode 100644
index 0000000000..414f01bd93
--- /dev/null
+++ b/devel/_static/mathjax/input/tex/extensions/noerrors.js
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/devel/_static/mathjax/input/tex/extensions/noundefined.js b/devel/_static/mathjax/input/tex/extensions/noundefined.js
new file mode 100644
index 0000000000..520f1b8af4
--- /dev/null
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new file mode 100644
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index 0000000000..2dfe558d2e
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diff --git a/devel/_static/mathjax/input/tex/extensions/setoptions.js b/devel/_static/mathjax/input/tex/extensions/setoptions.js
new file mode 100644
index 0000000000..dfd4330c64
--- /dev/null
+++ b/devel/_static/mathjax/input/tex/extensions/setoptions.js
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new file mode 100644
index 0000000000..ff47975c2b
--- /dev/null
+++ b/devel/_static/mathjax/input/tex/extensions/tagformat.js
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new file mode 100644
index 0000000000..8ec99d183b
--- /dev/null
+++ b/devel/_static/mathjax/input/tex/extensions/textcomp.js
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diff --git a/devel/_static/mathjax/input/tex/extensions/textmacros.js b/devel/_static/mathjax/input/tex/extensions/textmacros.js
new file mode 100644
index 0000000000..6c9b93ffd6
--- /dev/null
+++ b/devel/_static/mathjax/input/tex/extensions/textmacros.js
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/devel/_static/mathjax/input/tex/extensions/unicode.js b/devel/_static/mathjax/input/tex/extensions/unicode.js
new file mode 100644
index 0000000000..32688d34c3
--- /dev/null
+++ b/devel/_static/mathjax/input/tex/extensions/unicode.js
@@ -0,0 +1 @@
+!function(){"use strict";var t,e,a,n={667:function(t,e){e.q=void 0,e.q="3.2.2"},376:function(t,e,a){var n=this&&this.__importDefault||function(t){return t&&t.__esModule?t:{default:t}};Object.defineProperty(e,"__esModule",{value:!0}),e.UnicodeConfiguration=e.UnicodeMethods=void 0;var o=a(251),i=n(a(402)),r=a(871),u=n(a(398)),l=n(a(748)),c=a(992);e.UnicodeMethods={};var p={};e.UnicodeMethods.Unicode=function(t,e){var a=t.GetBrackets(e),n=null,o=null;a&&(a.replace(/ /g,"").match(/^(\d+(\.\d*)?|\.\d+),(\d+(\.\d*)?|\.\d+)$/)?(n=a.replace(/ /g,"").split(/,/),o=t.GetBrackets(e)):o=a);var r=u.default.trimSpaces(t.GetArgument(e)).replace(/^0x/,"x");if(!r.match(/^(x[0-9A-Fa-f]+|[0-9]+)$/))throw new i.default("BadUnicode","Argument to \\unicode must be a number");var d=parseInt(r.match(/^x/)?"0"+r:r);p[d]?o||(o=p[d][2]):p[d]=[800,200,o,d],n&&(p[d][0]=Math.floor(1e3*parseFloat(n[0])),p[d][1]=Math.floor(1e3*parseFloat(n[1])));var M=t.stack.env.font,s={};o?(p[d][2]=s.fontfamily=o.replace(/'/g,"'"),M&&(M.match(/bold/)&&(s.fontweight="bold"),M.match(/italic|-mathit/)&&(s.fontstyle="italic"))):M&&(s.mathvariant=M);var x=t.create("token","mtext",s,(0,c.numeric)(r));l.default.setProperty(x,"unicode",!0),t.Push(x)},new r.CommandMap("unicode",{unicode:"Unicode"},e.UnicodeMethods),e.UnicodeConfiguration=o.Configuration.create("unicode",{handler:{macro:["unicode"]}})},955:function(t,e){MathJax._.components.global.isObject,MathJax._.components.global.combineConfig,MathJax._.components.global.combineDefaults,e.r8=MathJax._.components.global.combineWithMathJax,MathJax._.components.global.MathJax},992:function(t,e){Object.defineProperty(e,"__esModule",{value:!0}),e.options=MathJax._.util.Entities.options,e.entities=MathJax._.util.Entities.entities,e.add=MathJax._.util.Entities.add,e.remove=MathJax._.util.Entities.remove,e.translate=MathJax._.util.Entities.translate,e.numeric=MathJax._.util.Entities.numeric},251:function(t,e){Object.defineProperty(e,"__esModule",{value:!0}),e.Configuration=MathJax._.input.tex.Configuration.Configuration,e.ConfigurationHandler=MathJax._.input.tex.Configuration.ConfigurationHandler,e.ParserConfiguration=MathJax._.input.tex.Configuration.ParserConfiguration},748:function(t,e){Object.defineProperty(e,"__esModule",{value:!0}),e.default=MathJax._.input.tex.NodeUtil.default},398:function(t,e){Object.defineProperty(e,"__esModule",{value:!0}),e.default=MathJax._.input.tex.ParseUtil.default},871:function(t,e){Object.defineProperty(e,"__esModule",{value:!0}),e.parseResult=MathJax._.input.tex.SymbolMap.parseResult,e.AbstractSymbolMap=MathJax._.input.tex.SymbolMap.AbstractSymbolMap,e.RegExpMap=MathJax._.input.tex.SymbolMap.RegExpMap,e.AbstractParseMap=MathJax._.input.tex.SymbolMap.AbstractParseMap,e.CharacterMap=MathJax._.input.tex.SymbolMap.CharacterMap,e.DelimiterMap=MathJax._.input.tex.SymbolMap.DelimiterMap,e.MacroMap=MathJax._.input.tex.SymbolMap.MacroMap,e.CommandMap=MathJax._.input.tex.SymbolMap.CommandMap,e.EnvironmentMap=MathJax._.input.tex.SymbolMap.EnvironmentMap},402:function(t,e){Object.defineProperty(e,"__esModule",{value:!0}),e.default=MathJax._.input.tex.TexError.default}},o={};function i(t){var e=o[t];if(void 0!==e)return e.exports;var a=o[t]={exports:{}};return n[t].call(a.exports,a,a.exports,i),a.exports}t=i(955),e=i(667),a=i(376),MathJax.loader&&MathJax.loader.checkVersion("[tex]/unicode",e.q,"tex-extension"),(0,t.r8)({_:{input:{tex:{unicode:{UnicodeConfiguration:a}}}}})}();
\ No newline at end of file
diff --git a/devel/_static/mathjax/input/tex/extensions/upgreek.js b/devel/_static/mathjax/input/tex/extensions/upgreek.js
new file mode 100644
index 0000000000..5115498225
--- /dev/null
+++ b/devel/_static/mathjax/input/tex/extensions/upgreek.js
@@ -0,0 +1 @@
+!function(){"use strict";var a,t,e,p={667:function(a,t){t.q=void 0,t.q="3.2.2"},927:function(a,t,e){Object.defineProperty(t,"__esModule",{value:!0}),t.UpgreekConfiguration=void 0;var p=e(251),n=e(871),o=e(108);new n.CharacterMap("upgreek",(function(a,t){var e=t.attributes||{};e.mathvariant=o.TexConstant.Variant.NORMAL;var p=a.create("token","mi",e,t.char);a.Push(p)}),{upalpha:"\u03b1",upbeta:"\u03b2",upgamma:"\u03b3",updelta:"\u03b4",upepsilon:"\u03f5",upzeta:"\u03b6",upeta:"\u03b7",uptheta:"\u03b8",upiota:"\u03b9",upkappa:"\u03ba",uplambda:"\u03bb",upmu:"\u03bc",upnu:"\u03bd",upxi:"\u03be",upomicron:"\u03bf",uppi:"\u03c0",uprho:"\u03c1",upsigma:"\u03c3",uptau:"\u03c4",upupsilon:"\u03c5",upphi:"\u03d5",upchi:"\u03c7",uppsi:"\u03c8",upomega:"\u03c9",upvarepsilon:"\u03b5",upvartheta:"\u03d1",upvarpi:"\u03d6",upvarrho:"\u03f1",upvarsigma:"\u03c2",upvarphi:"\u03c6",Upgamma:"\u0393",Updelta:"\u0394",Uptheta:"\u0398",Uplambda:"\u039b",Upxi:"\u039e",Uppi:"\u03a0",Upsigma:"\u03a3",Upupsilon:"\u03a5",Upphi:"\u03a6",Uppsi:"\u03a8",Upomega:"\u03a9"}),t.UpgreekConfiguration=p.Configuration.create("upgreek",{handler:{macro:["upgreek"]}})},955:function(a,t){MathJax._.components.global.isObject,MathJax._.components.global.combineConfig,MathJax._.components.global.combineDefaults,t.r8=MathJax._.components.global.combineWithMathJax,MathJax._.components.global.MathJax},251:function(a,t){Object.defineProperty(t,"__esModule",{value:!0}),t.Configuration=MathJax._.input.tex.Configuration.Configuration,t.ConfigurationHandler=MathJax._.input.tex.Configuration.ConfigurationHandler,t.ParserConfiguration=MathJax._.input.tex.Configuration.ParserConfiguration},871:function(a,t){Object.defineProperty(t,"__esModule",{value:!0}),t.parseResult=MathJax._.input.tex.SymbolMap.parseResult,t.AbstractSymbolMap=MathJax._.input.tex.SymbolMap.AbstractSymbolMap,t.RegExpMap=MathJax._.input.tex.SymbolMap.RegExpMap,t.AbstractParseMap=MathJax._.input.tex.SymbolMap.AbstractParseMap,t.CharacterMap=MathJax._.input.tex.SymbolMap.CharacterMap,t.DelimiterMap=MathJax._.input.tex.SymbolMap.DelimiterMap,t.MacroMap=MathJax._.input.tex.SymbolMap.MacroMap,t.CommandMap=MathJax._.input.tex.SymbolMap.CommandMap,t.EnvironmentMap=MathJax._.input.tex.SymbolMap.EnvironmentMap},108:function(a,t){Object.defineProperty(t,"__esModule",{value:!0}),t.TexConstant=MathJax._.input.tex.TexConstants.TexConstant}},n={};function o(a){var t=n[a];if(void 0!==t)return t.exports;var e=n[a]={exports:{}};return p[a](e,e.exports,o),e.exports}a=o(955),t=o(667),e=o(927),MathJax.loader&&MathJax.loader.checkVersion("[tex]/upgreek",t.q,"tex-extension"),(0,a.r8)({_:{input:{tex:{upgreek:{UpgreekConfiguration:e}}}}})}();
\ No newline at end of file
diff --git a/devel/_static/mathjax/input/tex/extensions/verb.js b/devel/_static/mathjax/input/tex/extensions/verb.js
new file mode 100644
index 0000000000..69a6d3399c
--- /dev/null
+++ b/devel/_static/mathjax/input/tex/extensions/verb.js
@@ -0,0 +1 @@
+!function(){"use strict";var t,e,a,n={667:function(t,e){e.q=void 0,e.q="3.2.2"},768:function(t,e,a){var n=this&&this.__importDefault||function(t){return t&&t.__esModule?t:{default:t}};Object.defineProperty(e,"__esModule",{value:!0}),e.VerbConfiguration=e.VerbMethods=void 0;var o=a(251),r=a(108),i=a(871),u=n(a(402));e.VerbMethods={},e.VerbMethods.Verb=function(t,e){var a=t.GetNext(),n=++t.i;if(""===a)throw new u.default("MissingArgFor","Missing argument for %1",e);for(;t.i<t.string.length&&t.string.charAt(t.i)!==a;)t.i++;if(t.i===t.string.length)throw new u.default("NoClosingDelim","Can't find closing delimiter for %1",t.currentCS);var o=t.string.slice(n,t.i).replace(/ /g,"\xa0");t.i++,t.Push(t.create("token","mtext",{mathvariant:r.TexConstant.Variant.MONOSPACE},o))},new i.CommandMap("verb",{verb:"Verb"},e.VerbMethods),e.VerbConfiguration=o.Configuration.create("verb",{handler:{macro:["verb"]}})},955:function(t,e){MathJax._.components.global.isObject,MathJax._.components.global.combineConfig,MathJax._.components.global.combineDefaults,e.r8=MathJax._.components.global.combineWithMathJax,MathJax._.components.global.MathJax},251:function(t,e){Object.defineProperty(e,"__esModule",{value:!0}),e.Configuration=MathJax._.input.tex.Configuration.Configuration,e.ConfigurationHandler=MathJax._.input.tex.Configuration.ConfigurationHandler,e.ParserConfiguration=MathJax._.input.tex.Configuration.ParserConfiguration},871:function(t,e){Object.defineProperty(e,"__esModule",{value:!0}),e.parseResult=MathJax._.input.tex.SymbolMap.parseResult,e.AbstractSymbolMap=MathJax._.input.tex.SymbolMap.AbstractSymbolMap,e.RegExpMap=MathJax._.input.tex.SymbolMap.RegExpMap,e.AbstractParseMap=MathJax._.input.tex.SymbolMap.AbstractParseMap,e.CharacterMap=MathJax._.input.tex.SymbolMap.CharacterMap,e.DelimiterMap=MathJax._.input.tex.SymbolMap.DelimiterMap,e.MacroMap=MathJax._.input.tex.SymbolMap.MacroMap,e.CommandMap=MathJax._.input.tex.SymbolMap.CommandMap,e.EnvironmentMap=MathJax._.input.tex.SymbolMap.EnvironmentMap},108:function(t,e){Object.defineProperty(e,"__esModule",{value:!0}),e.TexConstant=MathJax._.input.tex.TexConstants.TexConstant},402:function(t,e){Object.defineProperty(e,"__esModule",{value:!0}),e.default=MathJax._.input.tex.TexError.default}},o={};function r(t){var e=o[t];if(void 0!==e)return e.exports;var a=o[t]={exports:{}};return n[t].call(a.exports,a,a.exports,r),a.exports}t=r(955),e=r(667),a=r(768),MathJax.loader&&MathJax.loader.checkVersion("[tex]/verb",e.q,"tex-extension"),(0,t.r8)({_:{input:{tex:{verb:{VerbConfiguration:a}}}}})}();
\ No newline at end of file
diff --git a/devel/_static/mathjax/output/chtml.js b/devel/_static/mathjax/output/chtml.js
new file mode 100644
index 0000000000..4d537d4811
--- /dev/null
+++ b/devel/_static/mathjax/output/chtml.js
@@ -0,0 +1 @@
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e,r,o=this.node.attributes.getList("numalign","denomalign"),n=o.numalign,a=o.denomalign,s=t?{type:"d",atop:!0}:{atop:!0},l=this.node.getProperty("withDelims")?i(i({},s),{delims:!0}):i({},s),c="center"!==n?{align:n}:{},h="center"!==a?{align:a}:{},u=this.getUVQ(t),p=u.v,d=u.q;c.style={"padding-bottom":this.em(d)},l.style={"vertical-align":this.em(-p)},this.adaptor.append(this.chtml,this.html("mjx-frac",l,[e=this.html("mjx-num",c),r=this.html("mjx-den",h)])),this.childNodes[0].toCHTML(e),this.childNodes[1].toCHTML(r)},e.prototype.makeBevelled=function(t){var e=this.adaptor;e.setAttribute(this.chtml,"bevelled","ture");var r=e.append(this.chtml,this.html("mjx-num"));this.childNodes[0].toCHTML(r),this.bevel.toCHTML(this.chtml);var o=e.append(this.chtml,this.html("mjx-den"));this.childNodes[1].toCHTML(o);var n=this.getBevelData(t),i=n.u,a=n.v,s=n.delta,l=n.nbox,c=n.dbox;i&&e.setStyle(r,"verticalAlign",this.em(i/l.scale)),a&&e.setStyle(o,"verticalAlign",this.em(a/c.scale));var h=this.em(-s/2);e.setStyle(this.bevel.chtml,"marginLeft",h),e.setStyle(this.bevel.chtml,"marginRight",h)},e.kind=l.MmlMfrac.prototype.kind,e.styles={"mjx-frac":{display:"inline-block","vertical-align":"0.17em",padding:"0 .22em"},'mjx-frac[type="d"]':{"vertical-align":".04em"},"mjx-frac[delims]":{padding:"0 .1em"},"mjx-frac[atop]":{padding:"0 .12em"},"mjx-frac[atop][delims]":{padding:"0"},"mjx-dtable":{display:"inline-table",width:"100%"},"mjx-dtable > *":{"font-size":"2000%"},"mjx-dbox":{display:"block","font-size":"5%"},"mjx-num":{display:"block","text-align":"center"},"mjx-den":{display:"block","text-align":"center"},"mjx-mfrac[bevelled] > mjx-num":{display:"inline-block"},"mjx-mfrac[bevelled] > mjx-den":{display:"inline-block"},'mjx-den[align="right"], mjx-num[align="right"]':{"text-align":"right"},'mjx-den[align="left"], mjx-num[align="left"]':{"text-align":"left"},"mjx-nstrut":{display:"inline-block",height:".054em",width:0,"vertical-align":"-.054em"},'mjx-nstrut[type="d"]':{height:".217em","vertical-align":"-.217em"},"mjx-dstrut":{display:"inline-block",height:".505em",width:0},'mjx-dstrut[type="d"]':{height:".726em"},"mjx-line":{display:"block","box-sizing":"border-box","min-height":"1px",height:".06em","border-top":".06em solid",margin:".06em -.1em",overflow:"hidden"},'mjx-line[type="d"]':{margin:".18em -.1em"}},e}((0,s.CommonMfracMixin)(a.CHTMLWrapper));e.CHTMLmfrac=c},1315:function(t,e,r){var o,n=this&&this.__extends||(o=function(t,e){return o=Object.setPrototypeOf||{__proto__:[]}instanceof Array&&function(t,e){t.__proto__=e}||function(t,e){for(var r in e)Object.prototype.hasOwnProperty.call(e,r)&&(t[r]=e[r])},o(t,e)},function(t,e){if("function"!=typeof e&&null!==e)throw new TypeError("Class extends value "+String(e)+" is not a constructor or null");function r(){this.constructor=t}o(t,e),t.prototype=null===e?Object.create(e):(r.prototype=e.prototype,new r)});Object.defineProperty(e,"__esModule",{value:!0}),e.CHTMLmglyph=void 0;var i=r(6617),a=r(7969),s=r(910),l=function(t){function e(){return null!==t&&t.apply(this,arguments)||this}return n(e,t),e.prototype.toCHTML=function(t){var e=this.standardCHTMLnode(t);if(this.charWrapper)this.charWrapper.toCHTML(e);else{var r=this.node.attributes.getList("src","alt"),o=r.src,n=r.alt,i={width:this.em(this.width),height:this.em(this.height)};this.valign&&(i.verticalAlign=this.em(this.valign));var a=this.html("img",{src:o,style:i,alt:n,title:n});this.adaptor.append(e,a)}},e.kind=s.MmlMglyph.prototype.kind,e.styles={"mjx-mglyph > img":{display:"inline-block",border:0,padding:0}},e}((0,a.CommonMglyphMixin)(i.CHTMLWrapper));e.CHTMLmglyph=l},3271:function(t,e,r){var o,n=this&&this.__extends||(o=function(t,e){return o=Object.setPrototypeOf||{__proto__:[]}instanceof 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mjx-cell":{"text-align":"right"},'[script-align="left"] > mjx-row > mjx-cell':{"text-align":"left"},'[script-align="center"] > mjx-row > mjx-cell':{"text-align":"center"},'[script-align="right"] > mjx-row > mjx-cell':{"text-align":"right"}},e}((0,s.CommonMmultiscriptsMixin)(a.CHTMLmsubsup));e.CHTMLmmultiscripts=h},7013:function(t,e,r){var o,n=this&&this.__extends||(o=function(t,e){return o=Object.setPrototypeOf||{__proto__:[]}instanceof Array&&function(t,e){t.__proto__=e}||function(t,e){for(var r in e)Object.prototype.hasOwnProperty.call(e,r)&&(t[r]=e[r])},o(t,e)},function(t,e){if("function"!=typeof e&&null!==e)throw new TypeError("Class extends value "+String(e)+" is not a constructor or null");function r(){this.constructor=t}o(t,e),t.prototype=null===e?Object.create(e):(r.prototype=e.prototype,new r)});Object.defineProperty(e,"__esModule",{value:!0}),e.CHTMLmn=void 0;var i=r(6617),a=r(2304),s=r(3235),l=function(t){function e(){return null!==t&&t.apply(this,arguments)||this}return 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a=r(6617),s=r(437),l=r(9946),c=function(t){function e(){return null!==t&&t.apply(this,arguments)||this}return n(e,t),e.prototype.toCHTML=function(t){var e,r,o=this.node.attributes,n=o.get("symmetric")&&2!==this.stretch.dir,a=0!==this.stretch.dir;a&&null===this.size&&this.getStretchedVariant([]);var s=this.standardCHTMLnode(t);if(a&&this.size<0)this.stretchHTML(s);else{if(n||o.get("largeop")){var l=this.em(this.getCenterOffset());"0"!==l&&this.adaptor.setStyle(s,"verticalAlign",l)}this.node.getProperty("mathaccent")&&(this.adaptor.setStyle(s,"width","0"),this.adaptor.setStyle(s,"margin-left",this.em(this.getAccentOffset())));try{for(var c=i(this.childNodes),h=c.next();!h.done;h=c.next()){h.value.toCHTML(s)}}catch(t){e={error:t}}finally{try{h&&!h.done&&(r=c.return)&&r.call(c)}finally{if(e)throw e.error}}}},e.prototype.stretchHTML=function(t){var e=this.getText().codePointAt(0);this.font.delimUsage.add(e),this.childNodes[0].markUsed();var r=this.stretch,o=r.stretch,n=[];o[0]&&n.push(this.html("mjx-beg",{},[this.html("mjx-c")])),n.push(this.html("mjx-ext",{},[this.html("mjx-c")])),4===o.length&&n.push(this.html("mjx-mid",{},[this.html("mjx-c")]),this.html("mjx-ext",{},[this.html("mjx-c")])),o[2]&&n.push(this.html("mjx-end",{},[this.html("mjx-c")]));var i={},a=this.bbox,l=a.h,c=a.d,h=a.w;1===r.dir?(n.push(this.html("mjx-mark")),i.height=this.em(l+c),i.verticalAlign=this.em(-c)):i.width=this.em(h);var u=s.DirectionVH[r.dir],p={class:this.char(r.c||e),style:i},d=this.html("mjx-stretchy-"+u,p,n);this.adaptor.append(t,d)},e.kind=l.MmlMo.prototype.kind,e.styles={"mjx-stretchy-h":{display:"inline-table",width:"100%"},"mjx-stretchy-h > *":{display:"table-cell",width:0},"mjx-stretchy-h > * > mjx-c":{display:"inline-block",transform:"scalex(1.0000001)"},"mjx-stretchy-h > * > mjx-c::before":{display:"inline-block",width:"initial"},"mjx-stretchy-h > mjx-ext":{"/* IE */ overflow":"hidden","/* others */ overflow":"clip 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e="function"==typeof Symbol&&Symbol.iterator,r=e&&t[e],o=0;if(r)return r.call(t);if(t&&"number"==typeof t.length)return{next:function(){return t&&o>=t.length&&(t=void 0),{value:t&&t[o++],done:!t}}};throw new TypeError(e?"Object is not iterable.":"Symbol.iterator is not defined.")};Object.defineProperty(e,"__esModule",{value:!0}),e.CHTMLmpadded=void 0;var s=r(6617),l=r(7481),c=r(189),h=function(t){function e(){return null!==t&&t.apply(this,arguments)||this}return n(e,t),e.prototype.toCHTML=function(t){var e,r,o=this.standardCHTMLnode(t),n=[],s={},l=i(this.getDimens(),9),c=l[2],h=l[3],u=l[4],p=l[5],d=l[6],f=l[7],m=l[8];if(p&&(s.width=this.em(c+p)),(h||u)&&(s.margin=this.em(h)+" 0 "+this.em(u)),d+m||f){s.position="relative";var y=this.html("mjx-rbox",{style:{left:this.em(d+m),top:this.em(-f),"max-width":s.width}});d+m&&this.childNodes[0].getBBox().pwidth&&(this.adaptor.setAttribute(y,"width","full"),this.adaptor.setStyle(y,"left",this.em(d))),n.push(y)}o=this.adaptor.append(o,this.html("mjx-block",{style:s},n));try{for(var v=a(this.childNodes),b=v.next();!b.done;b=v.next()){b.value.toCHTML(n[0]||o)}}catch(t){e={error:t}}finally{try{b&&!b.done&&(r=v.return)&&r.call(v)}finally{if(e)throw e.error}}},e.kind=c.MmlMpadded.prototype.kind,e.styles={"mjx-mpadded":{display:"inline-block"},"mjx-rbox":{display:"inline-block",position:"relative"}},e}((0,l.CommonMpaddedMixin)(s.CHTMLWrapper));e.CHTMLmpadded=h},7111:function(t,e,r){var o,n=this&&this.__extends||(o=function(t,e){return o=Object.setPrototypeOf||{__proto__:[]}instanceof Array&&function(t,e){t.__proto__=e}||function(t,e){for(var r in e)Object.prototype.hasOwnProperty.call(e,r)&&(t[r]=e[r])},o(t,e)},function(t,e){if("function"!=typeof e&&null!==e)throw new 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c=l.value;if(this.setCellBaseline(c,a,e,r))break}}catch(t){o={error:t}}finally{try{l&&!l.done&&(n=s.return)&&n.call(s)}finally{if(o)throw o.error}}},e.prototype.setCellBaseline=function(t,e,r,o){var n=t.node.attributes.get("rowalign");if("baseline"===n||"axis"===n){var i=this.adaptor,a=i.lastChild(t.chtml);i.setStyle(a,"height",this.em(r)),i.setStyle(a,"verticalAlign",this.em(-o));var s=t.parent;if(!(s.node.isKind("mlabeledtr")&&t===s.childNodes[0]||"baseline"!==e&&"axis"!==e))return!0}return!1},e.prototype.handleFrame=function(){this.frame&&this.fLine&&this.adaptor.setStyle(this.itable,"border",".07em "+this.node.attributes.get("frame"))},e.prototype.handleWidth=function(){var t=this.adaptor,e=this.getBBox(),r=e.w,o=e.L,n=e.R;t.setStyle(this.chtml,"minWidth",this.em(o+r+n));var i=this.node.attributes.get("width");if((0,h.isPercent)(i))t.setStyle(this.chtml,"width",""),t.setAttribute(this.chtml,"width","full");else if(!this.hasLabels){if("auto"===i)return;i=this.em(this.length2em(i)+2*this.fLine)}var a=t.firstChild(this.chtml);if(t.setStyle(a,"width",i),t.setStyle(a,"minWidth",this.em(r)),o||n){t.setStyle(this.chtml,"margin","");var s=this.node.attributes.get("data-width-includes-label")?"padding":"margin";o===n?t.setStyle(a,s,"0 "+this.em(n)):t.setStyle(a,s,"0 "+this.em(n)+" 0 "+this.em(o))}t.setAttribute(this.itable,"width","full")},e.prototype.handleAlign=function(){var t=a(this.getAlignmentRow(),2),e=t[0],r=t[1];if(null===r)"axis"!==e&&this.adaptor.setAttribute(this.chtml,"align",e);else{var o=this.getVerticalPosition(r,e);this.adaptor.setAttribute(this.chtml,"align","top"),this.adaptor.setStyle(this.chtml,"verticalAlign",this.em(o))}},e.prototype.handleJustify=function(){var t=this.getAlignShift()[0];"center"!==t&&this.adaptor.setAttribute(this.chtml,"justify",t)},e.prototype.handleLabels=function(){if(this.hasLabels){var t=this.labels,e=this.node.attributes,r=this.adaptor,o=e.get("side");r.setAttribute(this.chtml,"side",o),r.setAttribute(t,"align",o),r.setStyle(t,o,"0");var n=a(this.addLabelPadding(o),2),i=n[0],s=n[1];if(s){var l=r.firstChild(this.chtml);this.setIndent(l,i,s)}this.updateRowHeights(),this.addLabelSpacing()}},e.prototype.addLabelPadding=function(t){var e=a(this.getPadAlignShift(t),3),r=e[1],o=e[2],n={};if("right"===t&&!this.node.attributes.get("data-width-includes-label")){var i=this.node.attributes.get("width"),s=this.getBBox(),l=s.w,c=s.L,u=s.R;n.style={width:(0,h.isPercent)(i)?"calc("+i+" + "+this.em(c+u)+")":this.em(c+l+u)}}return this.adaptor.append(this.chtml,this.html("mjx-labels",n,[this.labels])),[r,o]},e.prototype.updateRowHeights=function(){for(var t=this.getTableData(),e=t.H,r=t.D,o=t.NH,n=t.ND,i=this.getRowHalfSpacing(),a=0;a<this.numRows;a++){var s=this.childNodes[a];this.setRowHeight(s,e[a]+r[a]+i[a]+i[a+1]+this.rLines[a]),e[a]!==o[a]||r[a]!==n[a]?this.setRowBaseline(s,e[a]+r[a],r[a]):s.node.isKind("mlabeledtr")&&this.setCellBaseline(s.childNodes[0],"",e[a]+r[a],r[a])}},e.prototype.addLabelSpacing=function(){for(var t=this.adaptor,e=this.node.attributes.get("equalrows"),r=this.getTableData(),o=r.H,n=r.D,i=e?this.getEqualRowHeight():0,a=this.getRowHalfSpacing(),s=this.fLine,l=t.firstChild(this.labels),c=0;c<this.numRows;c++){this.childNodes[c].node.isKind("mlabeledtr")?(s&&t.insert(this.html("mjx-mtr",{style:{height:this.em(s)}}),l),t.setStyle(l,"height",this.em((e?i:o[c]+n[c])+a[c]+a[c+1])),l=t.next(l),s=this.rLines[c]):s+=a[c]+(e?i:o[c]+n[c])+a[c+1]+this.rLines[c]}},e.kind=c.MmlMtable.prototype.kind,e.styles={"mjx-mtable":{"vertical-align":".25em","text-align":"center",position:"relative","box-sizing":"border-box","border-spacing":0,"border-collapse":"collapse"},'mjx-mstyle[size="s"] mjx-mtable':{"vertical-align":".354em"},"mjx-labels":{position:"absolute",left:0,top:0},"mjx-table":{display:"inline-block","vertical-align":"-.5ex","box-sizing":"border-box"},"mjx-table > mjx-itable":{"vertical-align":"middle","text-align":"left","box-sizing":"border-box"},"mjx-labels > mjx-itable":{position:"absolute",top:0},'mjx-mtable[justify="left"]':{"text-align":"left"},'mjx-mtable[justify="right"]':{"text-align":"right"},'mjx-mtable[justify="left"][side="left"]':{"padding-right":"0 ! important"},'mjx-mtable[justify="left"][side="right"]':{"padding-left":"0 ! important"},'mjx-mtable[justify="right"][side="left"]':{"padding-right":"0 ! important"},'mjx-mtable[justify="right"][side="right"]':{"padding-left":"0 ! important"},"mjx-mtable[align]":{"vertical-align":"baseline"},'mjx-mtable[align="top"] > mjx-table':{"vertical-align":"top"},'mjx-mtable[align="bottom"] > mjx-table':{"vertical-align":"bottom"},'mjx-mtable[side="right"] mjx-labels':{"min-width":"100%"}},e}((0,l.CommonMtableMixin)(s.CHTMLWrapper));e.CHTMLmtable=u},8709:function(t,e,r){var o,n=this&&this.__extends||(o=function(t,e){return o=Object.setPrototypeOf||{__proto__:[]}instanceof Array&&function(t,e){t.__proto__=e}||function(t,e){for(var r in e)Object.prototype.hasOwnProperty.call(e,r)&&(t[r]=e[r])},o(t,e)},function(t,e){if("function"!=typeof e&&null!==e)throw new TypeError("Class extends value "+String(e)+" is not a constructor or null");function r(){this.constructor=t}o(t,e),t.prototype=null===e?Object.create(e):(r.prototype=e.prototype,new r)});Object.defineProperty(e,"__esModule",{value:!0}),e.CHTMLmtd=void 0;var i=r(6617),a=r(7805),s=r(2321),l=function(t){function e(){return null!==t&&t.apply(this,arguments)||this}return n(e,t),e.prototype.toCHTML=function(e){t.prototype.toCHTML.call(this,e);var r=this.node.attributes.get("rowalign"),o=this.node.attributes.get("columnalign");r!==this.parent.node.attributes.get("rowalign")&&this.adaptor.setAttribute(this.chtml,"rowalign",r),"center"===o||"mlabeledtr"===this.parent.kind&&this===this.parent.childNodes[0]&&o===this.parent.parent.node.attributes.get("side")||this.adaptor.setStyle(this.chtml,"textAlign",o),this.parent.parent.node.getProperty("useHeight")&&this.adaptor.append(this.chtml,this.html("mjx-tstrut"))},e.kind=s.MmlMtd.prototype.kind,e.styles={"mjx-mtd":{display:"table-cell","text-align":"center",padding:".215em .4em"},"mjx-mtd:first-child":{"padding-left":0},"mjx-mtd:last-child":{"padding-right":0},"mjx-mtable > * > mjx-itable > *:first-child > mjx-mtd":{"padding-top":0},"mjx-mtable > * > mjx-itable > *:last-child > mjx-mtd":{"padding-bottom":0},"mjx-tstrut":{display:"inline-block",height:"1em","vertical-align":"-.25em"},'mjx-labels[align="left"] > mjx-mtr > mjx-mtd':{"text-align":"left"},'mjx-labels[align="right"] > mjx-mtr > mjx-mtd':{"text-align":"right"},"mjx-mtd[extra]":{padding:0},'mjx-mtd[rowalign="top"]':{"vertical-align":"top"},'mjx-mtd[rowalign="center"]':{"vertical-align":"middle"},'mjx-mtd[rowalign="bottom"]':{"vertical-align":"bottom"},'mjx-mtd[rowalign="baseline"]':{"vertical-align":"baseline"},'mjx-mtd[rowalign="axis"]':{"vertical-align":".25em"}},e}((0,a.CommonMtdMixin)(i.CHTMLWrapper));e.CHTMLmtd=l},6359:function(t,e,r){var o,n=this&&this.__extends||(o=function(t,e){return o=Object.setPrototypeOf||{__proto__:[]}instanceof Array&&function(t,e){t.__proto__=e}||function(t,e){for(var r in e)Object.prototype.hasOwnProperty.call(e,r)&&(t[r]=e[r])},o(t,e)},function(t,e){if("function"!=typeof e&&null!==e)throw new TypeError("Class extends value "+String(e)+" is not a constructor or null");function r(){this.constructor=t}o(t,e),t.prototype=null===e?Object.create(e):(r.prototype=e.prototype,new r)});Object.defineProperty(e,"__esModule",{value:!0}),e.CHTMLmtext=void 0;var i=r(6617),a=r(8325),s=r(6277),l=function(t){function e(){return null!==t&&t.apply(this,arguments)||this}return n(e,t),e.kind=s.MmlMtext.prototype.kind,e}((0,a.CommonMtextMixin)(i.CHTMLWrapper));e.CHTMLmtext=l},7500:function(t,e,r){var o,n=this&&this.__extends||(o=function(t,e){return o=Object.setPrototypeOf||{__proto__:[]}instanceof Array&&function(t,e){t.__proto__=e}||function(t,e){for(var r in e)Object.prototype.hasOwnProperty.call(e,r)&&(t[r]=e[r])},o(t,e)},function(t,e){if("function"!=typeof e&&null!==e)throw new TypeError("Class extends value "+String(e)+" is not a constructor or null");function r(){this.constructor=t}o(t,e),t.prototype=null===e?Object.create(e):(r.prototype=e.prototype,new r)});Object.defineProperty(e,"__esModule",{value:!0}),e.CHTMLmlabeledtr=e.CHTMLmtr=void 0;var i=r(6617),a=r(4818),s=r(4818),l=r(4393),c=function(t){function e(){return null!==t&&t.apply(this,arguments)||this}return n(e,t),e.prototype.toCHTML=function(e){t.prototype.toCHTML.call(this,e);var r=this.node.attributes.get("rowalign");"baseline"!==r&&this.adaptor.setAttribute(this.chtml,"rowalign",r)},e.kind=l.MmlMtr.prototype.kind,e.styles={"mjx-mtr":{display:"table-row"},'mjx-mtr[rowalign="top"] > mjx-mtd':{"vertical-align":"top"},'mjx-mtr[rowalign="center"] > mjx-mtd':{"vertical-align":"middle"},'mjx-mtr[rowalign="bottom"] > mjx-mtd':{"vertical-align":"bottom"},'mjx-mtr[rowalign="baseline"] > mjx-mtd':{"vertical-align":"baseline"},'mjx-mtr[rowalign="axis"] > mjx-mtd':{"vertical-align":".25em"}},e}((0,a.CommonMtrMixin)(i.CHTMLWrapper));e.CHTMLmtr=c;var h=function(t){function e(){return null!==t&&t.apply(this,arguments)||this}return n(e,t),e.prototype.toCHTML=function(e){t.prototype.toCHTML.call(this,e);var r=this.adaptor.firstChild(this.chtml);if(r){this.adaptor.remove(r);var o=this.node.attributes.get("rowalign"),n="baseline"!==o&&"axis"!==o?{rowalign:o}:{},i=this.html("mjx-mtr",n,[r]);this.adaptor.append(this.parent.labels,i)}},e.prototype.markUsed=function(){t.prototype.markUsed.call(this),this.jax.wrapperUsage.add(c.kind)},e.kind=l.MmlMlabeledtr.prototype.kind,e.styles={"mjx-mlabeledtr":{display:"table-row"},'mjx-mlabeledtr[rowalign="top"] > mjx-mtd':{"vertical-align":"top"},'mjx-mlabeledtr[rowalign="center"] > mjx-mtd':{"vertical-align":"middle"},'mjx-mlabeledtr[rowalign="bottom"] > mjx-mtd':{"vertical-align":"bottom"},'mjx-mlabeledtr[rowalign="baseline"] > mjx-mtd':{"vertical-align":"baseline"},'mjx-mlabeledtr[rowalign="axis"] > mjx-mtd':{"vertical-align":".25em"}},e}((0,s.CommonMlabeledtrMixin)(c));e.CHTMLmlabeledtr=h},6577:function(t,e,r){var o,n=this&&this.__extends||(o=function(t,e){return o=Object.setPrototypeOf||{__proto__:[]}instanceof Array&&function(t,e){t.__proto__=e}||function(t,e){for(var r in 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r=this.adaptor.append(this.adaptor.append(this.chtml,this.html("mjx-row")),this.html("mjx-base")),o=this.adaptor.append(this.adaptor.append(this.chtml,this.html("mjx-row")),this.html("mjx-under"));this.baseChild.toCHTML(r),this.scriptChild.toCHTML(o);var n=this.baseChild.getOuterBBox(),i=this.scriptChild.getOuterBBox(),a=this.getUnderKV(n,i)[0],s=this.isLineBelow?0:this.getDelta(!0);this.adaptor.setStyle(o,"paddingTop",this.em(a)),this.setDeltaW([r,o],this.getDeltaW([n,i],[0,-s])),this.adjustUnderDepth(o,i)},e.kind=c.MmlMunder.prototype.kind,e.styles={"mjx-over":{"text-align":"left"},'mjx-munder:not([limits="false"])':{display:"inline-table"},"mjx-munder > mjx-row":{"text-align":"left"},"mjx-under":{"padding-bottom":".1em"}},e}((0,a.CommonMunderMixin)(i.CHTMLmsub));e.CHTMLmunder=h;var u=function(t){function e(){return null!==t&&t.apply(this,arguments)||this}return n(e,t),e.prototype.toCHTML=function(e){if(this.hasMovableLimits())return t.prototype.toCHTML.call(this,e),void 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this.adaptor.setAttribute(this.chtml,"limits","false");this.chtml=this.standardCHTMLnode(e);var r=this.adaptor.append(this.chtml,this.html("mjx-over")),o=this.adaptor.append(this.adaptor.append(this.chtml,this.html("mjx-box")),this.html("mjx-munder")),n=this.adaptor.append(this.adaptor.append(o,this.html("mjx-row")),this.html("mjx-base")),i=this.adaptor.append(this.adaptor.append(o,this.html("mjx-row")),this.html("mjx-under"));this.overChild.toCHTML(r),this.baseChild.toCHTML(n),this.underChild.toCHTML(i);var a=this.overChild.getOuterBBox(),s=this.baseChild.getOuterBBox(),l=this.underChild.getOuterBBox();this.adjustBaseHeight(n,s);var c=this.getOverKU(s,a)[0],h=this.getUnderKV(s,l)[0],u=this.getDelta();this.adaptor.setStyle(r,"paddingBottom",this.em(c)),this.adaptor.setStyle(i,"paddingTop",this.em(h)),this.setDeltaW([n,i,r],this.getDeltaW([s,l,a],[0,this.isLineBelow?0:-u,this.isLineAbove?0:u])),this.adjustOverDepth(r,a),this.adjustUnderDepth(i,l)},e.prototype.markUsed=function(){t.prototype.markUsed.call(this),this.jax.wrapperUsage.add(i.CHTMLmsubsup.kind)},e.kind=c.MmlMunderover.prototype.kind,e.styles={'mjx-munderover:not([limits="false"])':{"padding-top":".1em"},'mjx-munderover:not([limits="false"]) > *':{display:"block"}},e}((0,l.CommonMunderoverMixin)(i.CHTMLmsubsup));e.CHTMLmunderover=p},7322:function(t,e,r){var o,n=this&&this.__extends||(o=function(t,e){return o=Object.setPrototypeOf||{__proto__:[]}instanceof Array&&function(t,e){t.__proto__=e}||function(t,e){for(var r in e)Object.prototype.hasOwnProperty.call(e,r)&&(t[r]=e[r])},o(t,e)},function(t,e){if("function"!=typeof e&&null!==e)throw new 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r.error}}n.append(n.firstChild(t),s)}},e.prototype.adjustBaseHeight=function(t,e){if(this.node.attributes.get("accent")){var r=this.font.params.x_height*e.scale;e.h<r&&(this.adaptor.setStyle(t,"paddingTop",this.em(r-e.h)),e.h=r)}},e.kind="scriptbase",e}((0,r(7091).CommonScriptbaseMixin)(s.CHTMLWrapper));e.CHTMLscriptbase=l},7795:function(t,e,r){var o,n=this&&this.__extends||(o=function(t,e){return o=Object.setPrototypeOf||{__proto__:[]}instanceof Array&&function(t,e){t.__proto__=e}||function(t,e){for(var r in e)Object.prototype.hasOwnProperty.call(e,r)&&(t[r]=e[r])},o(t,e)},function(t,e){if("function"!=typeof e&&null!==e)throw new TypeError("Class extends value "+String(e)+" is not a constructor or null");function r(){this.constructor=t}o(t,e),t.prototype=null===e?Object.create(e):(r.prototype=e.prototype,new r)});Object.defineProperty(e,"__esModule",{value:!0}),e.CHTMLxml=e.CHTMLannotationXML=e.CHTMLannotation=e.CHTMLsemantics=void 0;var i=r(6617),a=r(3191),s=r(9167),l=r(8921),c=function(t){function e(){return null!==t&&t.apply(this,arguments)||this}return n(e,t),e.prototype.toCHTML=function(t){var e=this.standardCHTMLnode(t);this.childNodes.length&&this.childNodes[0].toCHTML(e)},e.kind=s.MmlSemantics.prototype.kind,e}((0,a.CommonSemanticsMixin)(i.CHTMLWrapper));e.CHTMLsemantics=c;var h=function(t){function e(){return null!==t&&t.apply(this,arguments)||this}return n(e,t),e.prototype.toCHTML=function(e){t.prototype.toCHTML.call(this,e)},e.prototype.computeBBox=function(){return this.bbox},e.kind=s.MmlAnnotation.prototype.kind,e}(i.CHTMLWrapper);e.CHTMLannotation=h;var u=function(t){function e(){return null!==t&&t.apply(this,arguments)||this}return n(e,t),e.kind=s.MmlAnnotationXML.prototype.kind,e.styles={"mjx-annotation-xml":{"font-family":"initial","line-height":"normal"}},e}(i.CHTMLWrapper);e.CHTMLannotationXML=u;var p=function(t){function e(){return null!==t&&t.apply(this,arguments)||this}return 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t.concat(o||Array.prototype.slice.call(e))},s=this&&this.__values||function(t){var e="function"==typeof Symbol&&Symbol.iterator,r=e&&t[e],o=0;if(r)return r.call(t);if(t&&"number"==typeof t.length)return{next:function(){return t&&o>=t.length&&(t=void 0),{value:t&&t[o++],done:!t}}};throw new TypeError(e?"Object is not iterable.":"Symbol.iterator is not defined.")};Object.defineProperty(e,"__esModule",{value:!0}),e.CommonMmultiscriptsMixin=e.ScriptNames=e.NextScript=void 0;var l=r(3717);e.NextScript={base:"subList",subList:"supList",supList:"subList",psubList:"psupList",psupList:"psubList"},e.ScriptNames=["sup","sup","psup","psub"],e.CommonMmultiscriptsMixin=function(t){return function(t){function r(){for(var e=[],r=0;r<arguments.length;r++)e[r]=arguments[r];var o=t.apply(this,a([],i(e),!1))||this;return o.scriptData=null,o.firstPrescript=0,o.getScriptData(),o}return n(r,t),r.prototype.combinePrePost=function(t,e){var r=new l.BBox(t);return 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r=this.node.attributes.get("accentunder"),o=this.font.params,n=e.h*e.rscale,i=o.rule_thickness*o.separation_factor,a=this.baseHasAccentUnder?i:0,s=this.isLineBelow?3*o.rule_thickness:i,l=(r?s:Math.max(o.big_op_spacing2,o.big_op_spacing4-n))-a;return[l,-(t.d*t.rscale+l+n)]},e.prototype.getDeltaW=function(t,e){var r,o,n,l;void 0===e&&(e=[0,0,0]);var c=this.node.attributes.get("align"),h=t.map((function(t){return t.w*t.rscale}));h[0]-=this.baseRemoveIc&&!this.baseCore.node.attributes.get("largeop")?this.baseIc:0;var u=Math.max.apply(Math,a([],i(h),!1)),p=[],d=0;try{for(var f=s(h.keys()),m=f.next();!m.done;m=f.next()){var y=m.value;p[y]=("center"===c?(u-h[y])/2:"right"===c?u-h[y]:0)+e[y],p[y]<d&&(d=-p[y])}}catch(t){r={error:t}}finally{try{m&&!m.done&&(o=f.return)&&o.call(f)}finally{if(r)throw r.error}}if(d)try{for(var v=s(p.keys()),b=v.next();!b.done;b=v.next()){y=b.value;p[y]+=d}}catch(t){n={error:t}}finally{try{b&&!b.done&&(l=v.return)&&l.call(v)}finally{if(n)throw 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diff --git a/devel/_static/mathjax/output/chtml/fonts/tex.js b/devel/_static/mathjax/output/chtml/fonts/tex.js
new file mode 100644
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diff --git a/devel/_static/mathjax/output/svg.js b/devel/_static/mathjax/output/svg.js
new file mode 100644
index 0000000000..758a9b5c06
--- /dev/null
+++ b/devel/_static/mathjax/output/svg.js
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t.concat(o||Array.prototype.slice.call(e))},s=this&&this.__values||function(t){var e="function"==typeof Symbol&&Symbol.iterator,r=e&&t[e],o=0;if(r)return r.call(t);if(t&&"number"==typeof t.length)return{next:function(){return t&&o>=t.length&&(t=void 0),{value:t&&t[o++],done:!t}}};throw new TypeError(e?"Object is not iterable.":"Symbol.iterator is not defined.")};Object.defineProperty(e,"__esModule",{value:!0}),e.CommonMmultiscriptsMixin=e.ScriptNames=e.NextScript=void 0;var l=r(3717);e.NextScript={base:"subList",subList:"supList",supList:"subList",psubList:"psupList",psupList:"psubList"},e.ScriptNames=["sup","sup","psup","psub"],e.CommonMmultiscriptsMixin=function(t){return function(t){function r(){for(var e=[],r=0;r<arguments.length;r++)e[r]=arguments[r];var o=t.apply(this,a([],i(e),!1))||this;return o.scriptData=null,o.firstPrescript=0,o.getScriptData(),o}return n(r,t),r.prototype.combinePrePost=function(t,e){var r=new l.BBox(t);return 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s=this.getBaseWidth(),l=this.getAdjustedIc(),c=n(this.getUVQ(),2),h=c[0],u=c[1];t.combine(i,s,u),t.combine(a,s+l,h),t.w+=this.font.params.scriptspace,t.clean(),this.setChildPWidths(e)},e.prototype.getUVQ=function(t,e){void 0===t&&(t=this.subChild.getOuterBBox()),void 0===e&&(e=this.supChild.getOuterBBox());var r=this.baseCore.getOuterBBox();if(this.UVQ)return this.UVQ;var o=this.font.params,i=3*o.rule_thickness,a=this.length2em(this.node.attributes.get("subscriptshift"),o.sub2),s=this.baseCharZero(r.d*this.baseScale+o.sub_drop*t.rscale),l=n([this.getU(),Math.max(s,a)],2),c=l[0],h=l[1],u=c-e.d*e.rscale-(t.h*t.rscale-h);if(u<i){h+=i-u;var p=.8*o.x_height-(c-e.d*e.rscale);p>0&&(c+=p,h-=p)}return c=Math.max(this.length2em(this.node.attributes.get("superscriptshift"),c),c),h=Math.max(this.length2em(this.node.attributes.get("subscriptshift"),h),h),u=c-e.d*e.rscale-(t.h*t.rscale-h),this.UVQ=[c,-h,u],this.UVQ},e}(t),e.useIC=!1,e}},8589:function(t,e,r){var o,n=this&&this.__extends||(o=function(t,e){return o=Object.setPrototypeOf||{__proto__:[]}instanceof Array&&function(t,e){t.__proto__=e}||function(t,e){for(var r in e)Object.prototype.hasOwnProperty.call(e,r)&&(t[r]=e[r])},o(t,e)},function(t,e){if("function"!=typeof e&&null!==e)throw new TypeError("Class extends value "+String(e)+" is not a constructor or null");function r(){this.constructor=t}o(t,e),t.prototype=null===e?Object.create(e):(r.prototype=e.prototype,new r)}),i=this&&this.__read||function(t,e){var r="function"==typeof Symbol&&t[Symbol.iterator];if(!r)return t;var o,n,i=r.call(t),a=[];try{for(;(void 0===e||e-- >0)&&!(o=i.next()).done;)a.push(o.value)}catch(t){n={error:t}}finally{try{o&&!o.done&&(r=i.return)&&r.call(i)}finally{if(n)throw n.error}}return a},a=this&&this.__spreadArray||function(t,e,r){if(r||2===arguments.length)for(var o,n=0,i=e.length;n<i;n++)!o&&n in e||(o||(o=Array.prototype.slice.call(e,0,n)),o[n]=e[n]);return 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n=(o-(e[t]+r[t]))/2;n<1e-5||(e[t]+=n,r[t]+=n)},e.prototype.recordPWidthCell=function(t,e){t.childNodes[0]&&t.childNodes[0].getBBox().pwidth&&this.pwidthCells.push([t,e])},e.prototype.computeBBox=function(t,e){void 0===e&&(e=!1);var r,o,n=this.getTableData(),a=n.H,s=n.D;if(this.node.attributes.get("equalrows")){var l=this.getEqualRowHeight();r=(0,h.sum)([].concat(this.rLines,this.rSpace))+l*this.numRows}else r=(0,h.sum)(a.concat(s,this.rLines,this.rSpace));r+=2*(this.fLine+this.fSpace[1]);var u=this.getComputedWidths();o=(0,h.sum)(u.concat(this.cLines,this.cSpace))+2*(this.fLine+this.fSpace[0]);var p=this.node.attributes.get("width");"auto"!==p&&(o=Math.max(this.length2em(p,0)+2*this.fLine,o));var d=i(this.getBBoxHD(r),2),f=d[0],y=d[1];t.h=f,t.d=y,t.w=o;var m=i(this.getBBoxLR(),2),v=m[0],g=m[1];t.L=v,t.R=g,(0,c.isPercent)(p)||this.setColumnPWidths()},e.prototype.setChildPWidths=function(t,e,r){var 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a=i(n.value,2),l=a[0],c=a[1];l.setChildPWidths(!1,r[c])&&(l.invalidateBBox(),l.getBBox())}}catch(e){t={error:e}}finally{try{n&&!n.done&&(e=o.return)&&e.call(o)}finally{if(t)throw t.error}}},e.prototype.getBBoxHD=function(t){var e=i(this.getAlignmentRow(),2),r=e[0],o=e[1];if(null===o){var n=this.font.params.axis_height,a=t/2;return{top:[0,t],center:[a,a],bottom:[t,0],baseline:[a,a],axis:[a+n,a-n]}[r]||[a,a]}var s=this.getVerticalPosition(o,r);return[s,t-s]},e.prototype.getBBoxLR=function(){if(this.hasLabels){var t=this.node.attributes,e=t.get("side"),r=i(this.getPadAlignShift(e),2),o=r[0],n=r[1],a=this.hasLabels&&!!t.get("data-width-includes-label");return a&&this.frame&&this.fSpace[0]&&(o-=this.fSpace[0]),"center"!==n||a?"left"===e?[o,0]:[0,o]:[o,o]}return[0,0]},e.prototype.getPadAlignShift=function(t){var e=this.getTableData().L+this.length2em(this.node.attributes.get("minlabelspacing")),r=i(null==this.styles?["",""]:[this.styles.get("padding-left"),this.styles.get("padding-right")],2),o=r[0],n=r[1];(o||n)&&(e=Math.max(e,this.length2em(o||"0"),this.length2em(n||"0")));var a=i(this.getAlignShift(),2),s=a[0],l=a[1];return s===t&&(l="left"===t?Math.max(e,l)-e:Math.min(-e,l)+e),[e,s,l]},e.prototype.getAlignShift=function(){return this.isTop?t.prototype.getAlignShift.call(this):[this.container.getChildAlign(this.containerI),0]},e.prototype.getWidth=function(){return this.pWidth||this.getBBox().w},e.prototype.getEqualRowHeight=function(){var t=this.getTableData(),e=t.H,r=t.D,o=Array.from(e.keys()).map((function(t){return e[t]+r[t]}));return Math.max.apply(Math,o)},e.prototype.getComputedWidths=function(){var t=this,e=this.getTableData().W,r=Array.from(e.keys()).map((function(r){return"number"==typeof t.cWidths[r]?t.cWidths[r]:e[r]}));return this.node.attributes.get("equalcolumns")&&(r=Array(r.length).fill((0,h.max)(r))),r},e.prototype.getColumnWidths=function(){var t=this.node.attributes.get("width");if(this.node.attributes.get("equalcolumns"))return this.getEqualColumns(t);var e=this.getColumnAttributes("columnwidth",0);return"auto"===t?this.getColumnWidthsAuto(e):(0,c.isPercent)(t)?this.getColumnWidthsPercent(e):this.getColumnWidthsFixed(e,this.length2em(t))},e.prototype.getEqualColumns=function(t){var e,r=Math.max(1,this.numCols);if("auto"===t){var o=this.getTableData().W;e=(0,h.max)(o)}else if((0,c.isPercent)(t))e=this.percent(1/r);else{var n=(0,h.sum)([].concat(this.cLines,this.cSpace))+2*this.fSpace[0];e=Math.max(0,this.length2em(t)-n)/r}return Array(this.numCols).fill(e)},e.prototype.getColumnWidthsAuto=function(t){var e=this;return t.map((function(t){return"auto"===t||"fit"===t?null:(0,c.isPercent)(t)?t:e.length2em(t)}))},e.prototype.getColumnWidthsPercent=function(t){var 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diff --git a/devel/_static/mathjax/sre/mathmaps/base.json b/devel/_static/mathjax/sre/mathmaps/base.json
new file mode 100644
index 0000000000..c7f35e2af0
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new file mode 100644
index 0000000000..26740a96ae
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new file mode 100644
index 0000000000..2464e1f964
--- /dev/null
+++ b/devel/_static/mathjax/sre/mathmaps/da.json
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diff --git a/devel/_static/mathjax/sre/mathmaps/de.json b/devel/_static/mathjax/sre/mathmaps/de.json
new file mode 100644
index 0000000000..429575ddf5
--- /dev/null
+++ b/devel/_static/mathjax/sre/mathmaps/de.json
@@ -0,0 +1,146 @@
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(grammar:EndRoot); [t] \"Wurzel Ende\" (pause:short)"],["Action","negative-sqrt-endroot","[n] . (grammar:EndRoot); [t] \"Wurzel Ende\" (pause:short)"],["Action","sqrt-posnegsqrootend","[n] . (grammar:EndRoot); [t] \"Wurzel Ende\" (pause:short)"],["Action","negative-sqrt-posnegsqrootend","[n] . (grammar:EndRoot); [t] \"Wurzel Ende\" (pause:short)"],["Action","cubic","[t] \"Kubikwurzel aus\"; [n] children/*[2] (pause:short, grammar:!EndRoot)"],["Action","cubic-nested","[p] (pause:short); [t] \"Kubikwurzel aus\"; [n] children/*[2] (pause:short, grammar:!EndRoot)"],["Action","root","[t] \"die\" (grammar:article); [n] children/*[1] (grammar:ordinal); [t] \"Wurzel aus\"; [n] children/*[2] (pause:short, grammar:!EndRoot)"],["Action","root-nested","[p] (pause:short); [t] \"die\" (grammar:article); [n] children/*[1] (grammar:ordinal); [t] \"Wurzel aus\"; [n] children/*[2] (pause:short, grammar:!EndRoot)"],["Action","root-endroot","[n] . (grammar:EndRoot); [t] \"Wurzel Ende\" (pause:short)"],["Action","root-posnegsqrootend","[n] . (grammar:EndRoot); [t] \"Wurzel Ende\" (pause:short)"],["Action","negative","[t] \"minus\"; [n] children/*[1]"],["Action","positive","[t] \"plus\"; [n] children/*[1]"],["Action","angle-measure","[t] \"das Maß des Winkels\"; [n] children/*[2] (grammar:angle)"],["Action","set-prefix-operators-masculine","[t] \"der\" (grammar:article); [n] . (grammar:!prefix); [t] \"von\""],["Action","set-prefix-operators-feminine","[t] \"die\" (grammar:article); [n] . (grammar:!prefix); [t] \"von\""],["Action","division","[n] children/*[1]; [t] \"geteilt durch\"; [n] children/*[2]"],["Action","natural-numbers","[t] \"die\" (grammar:article:plural); [t] \"natürlichen Zahlen\""],["Action","integers","[t] \"die\" (grammar:article:plural); [t] \"ganzen Zahlen\""],["Action","rational-numbers","[t] \"die\" (grammar:article:plural); [t] \"rationalen Zahlen\""],["Action","real-numbers","[t] \"die\" (grammar:article:plural); [t] \"reellen Zahlen\""],["Action","complex-numbers","[t] \"die\" (grammar:article:plural); [t] \"komplexen Zahlen\""],["Action","natural-numbers-with-zero","[t] \"die\" (grammar:article:plural); [t] \"natürlichen Zahlen mit Null\""],["Action","positive-integers","[t] \"die\" (grammar:article:plural); [t] \"positiven ganzen Zahlen\""],["Action","negative-integers","[t] \"die\" (grammar:article:plural); [t] \"negativen ganzen Zahlen\""],["Action","positive-rational-numbers","[t] \"die\" (grammar:article:plural); [t] \"positiven rationalen Zahlen\""],["Action","negative-rational-numbers","[t] \"die\" (grammar:article:plural); [t] \"negativen rationalen Zahlen\""],["Action","fences-neutral","[p] (pause:short); [t] \"der Betrag von\"; [n] children/*[1] (pause:short)"],["Action","fences-neutral-absend","[p] (pause:short); [t] \"der Betrag von\"; [n] children/*[1] (pause:short); [t] \"Ende Betrag\" (pause:short)"],["Action","fences-neutral-cardinality","[p] (pause:short); [t] \"die Mächtigkeit der Menge\"; [n] children/*[1] (pause:short)"],["Action","fences-neutral-determinant","[p] (pause:short); [t] \"die Determinante von\"; [n] children/*[1] (pause:short)"],["Action","fences-metric","[p] (pause:short); [t] \"die Metrik von\" (span:.); [n] children/*[1] (pause:short)"],["Action","fences-metric-absend","[p] (pause:short); [t] \"die Metrik von\" (span:content/*[1]); [n] children/*[1] (pause:short); [t] \"Ende Metrik\" (span:content/*[1], pause:short)"],["Action","matrix","[t] \"die\" (grammar:article); [t] count(children/*); [t] \"mal\"; [t] count(children/*[1]/children/*); [t] \"Matrize\" (pause:long); [m] children/* (ctxtFunc:CTFnodeCounter, context:\"Zeile-:\", pause:long)"],["Action","matrix-simple","[t] \"die\" (grammar:article); [t] count(children/*); [t] \"mal\"; [t] count(children/*[1]/children/*); [t] \"Matrize\" (pause:long); [m] children/* (ctxtFunc:CTFnodeCounter, context:\"Zeile-:\", pause:long, grammar:simpleDet)"],["Action","matrix-trivial","[t] \"die 1 mal 1 Matrize mit Element\"; [n] children/*[1] (pause:long)"],["Action","determinant","[t] \"die\" (grammar:article); [t] \"Determinante der\"; [t] count(children/*); [t] \"mal\"; [t] count(children/*[1]/children/*); [t] \"Matrize\" (pause:long); [m] children/* (ctxtFunc:CTFnodeCounter, context:\"Zeile-:\", pause:long, grammar:simpleDet)"],["Action","determinant-simple","[t] \"die\" (grammar:article); [t] \"Determinante der\"; [t] count(children/*); [t] \"mal\"; [t] count(children/*[1]/children/*); [t] \"Matrize\" (pause:long); [m] children/* (ctxtFunc:CTFnodeCounter, context:\"Zeile-:\", pause:long)"],["Action","matrix-vector","[t] \"die\" (grammar:article); [t] count(children/*); [t] \"mal\"; [t] count(children/*[1]/children/*); [t] \"Spaltenmatrize\" (pause:long); [m] children/* (ctxtFunc:CTFnodeCounter, context:\"Zeile-:\", pause:long, grammar:simpleDet)"],["Action","matrix-vector-simple","[t] \"die\" (grammar:article); [t] count(children/*); [t] \"mal\"; [t] count(children/*[1]/children/*); [t] \"Spaltenmatrize\" (pause:long); [m] children/* (sepFunc:CTFpauseSeparator, separator:\"short\", pause:long, grammar:simpleDet)"],["Action","matrix-vector-simple-silentcolnum","[t] \"die\" (grammar:article); [t] count(children/*); [t] \"mal\"; [t] count(children/*[1]/children/*); [t] \"Spaltenmatrize\" (pause:long); [m] children/* (sepFunc:CTFpauseSeparator, separator:\"short\", pause:long, grammar:simpleDet)"],["Action","matrix-row-vector","[t] \"die\" (grammar:article); [t] count(children/*); [t] \"mal\"; [t] count(children/*[1]/children/*); [t] \"Zeilenmatrize\" (pause:long); [m] children/*[1]/children/* (ctxtFunc:CTFnodeCounter, context:\"Spalte-:\", pause:long, grammar:simpleDet)"],["Action","matrix-row-vector-simple","[t] \"die\" (grammar:article); [t] count(children/*); [t] \"mal\"; [t] count(children/*[1]/children/*); [t] \"Zeilenmatrize\" (pause:long); [m] children/*[1]/children/* (sepFunc:CTFpauseSeparator, separator:\"short\", pause:long, grammar:simpleDet)"],["Action","matrix-row-vector-simple-silentcolnum","[t] \"die\" (grammar:article); [t] count(children/*); [t] \"mal\"; [t] count(children/*[1]/children/*); [t] \"Zeilenmatrize\" (pause:long); [m] children/*[1]/children/* (sepFunc:CTFpauseSeparator, separator:\"short\", pause:long, grammar:simpleDet)"],["Action","matrix-row","[m] children/* (ctxtFunc:CTFnodeCounter, context:\"Spalte-,- \", sepFunc:CTFpauseSeparator, separator:\"medium\", pause:long)"],["Action","matrix-end-matrix","[n] . (grammar:EndMatrix); [t] \"Ende Matrize\""],["Action","matrix-end-vector","[n] . (grammar:EndMatrix); [t] \"Ende Matrize\""],["Action","matrix-end-determinant","[n] . (grammar:EndMatrix); [t] \"Ende Determinante\""],["Action","vector","[t] \"der\" (grammar:article); [t] count(children/*); [t] \"mal\"; [t] count(children/*[1]/children/*); [t] \"Spaltenvektor\" (pause:long); [m] children/* (ctxtFunc:CTFnodeCounter, context:\"Zeile-:\", pause:long, grammar:simpleDet)"],["Action","vector-simple","[t] \"der\" (grammar:article); [t] count(children/*); [t] \"mal\"; [t] count(children/*[1]/children/*); [t] \"Spaltenvektor\" (pause:long); [m] children/* (sepFunc:CTFpauseSeparator, separator:\"short\", pause:long, grammar:simpleDet)"],["Action","row-vector","[t] \"der\" (grammar:article); [t] count(children/*); [t] \"mal\"; [t] count(children/*[1]/children/*); [t] \"Zeilenvektor\" (pause:long); [m] children/*[1]/children/* (ctxtFunc:CTFnodeCounter, context:\"Spalte-:\", pause:long, grammar:simpleDet)"],["Action","row-vector-simple","[t] \"der\" (grammar:article); [t] count(children/*); [t] \"mal\"; [t] count(children/*[1]/children/*); [t] \"Zeilenvektor\" (pause:long); [m] children/*[1]/children/* (sepFunc:CTFpauseSeparator, separator:\"short\", pause:long, grammar:simpleDet)"],["Action","vector-end-matrix","[n] . (grammar:EndMatrix); [t] \"Ende Matrize\""],["Action","vector-end-vector","[n] . (grammar:EndMatrix); [t] \"Ende Vektor\""],["Action","vector-end-vector-endvector","[n] . (grammar:EndMatrix); [t] \"Ende Vektor\""],["Action","vector-end-determinant","[n] . (grammar:EndMatrix); [t] \"Ende Determinante\""],["Action","binomial","[n] children/*[1]/children/*[1]; [t] \"über\"; [n] children/*[2]/children/*[1]"],["Action","lines-summary","[p] (pause:short); [t] count(children/*); [t] \"Zeilen\"; [n] . (grammar:layoutSummary)"],["Action","cases-summary","[p] (pause:short); [t] count(children/*); [t] \"Fälle\"; [n] . (grammar:layoutSummary)"],["Action","lines","[p] (pause:short); [m] children/* (ctxtFunc:CTFnodeCounter, context:\"Zeile-:\", sepFunc:CTFpauseSeparator, separator:\"long\", pause:long)"],["Action","blank-cell","[t] \"leer\""],["Action","blank-line","[t] \"leer\""],["Action","blank-cell-empty","[t] \"leer\""],["Action","blank-line-empty","[t] \"leer\""],["Action","cases","[p] (pause:short); [m] children/* (ctxtFunc:CTFnodeCounter, context:\"Fall-:\", sepFunc:CTFpauseSeparator, separator:\"long\", pause:long)"],["Action","lines-cases-summary","[p] (pause:short); [t] count(children/*); [t] \"Fälle\"; [n] . (grammar:layoutSummary)"],["Action","lines-cases","[p] (pause:short); [m] children/* (ctxtFunc:CTFnodeCounter, context:\"Fall-:\", sepFunc:CTFpauseSeparator, separator:\"long\", pause:long)"],["Action","lines-equations-summary","[p] (pause:short); [t] count(children/*); [t] \"Gleichungen\"; [n] . (grammar:layoutSummary)"],["Action","lines-equations","[p] (pause:short); [m] children/* (ctxtFunc:CTFnodeCounter, context:\"Gleichung-:\", sepFunc:CTFpauseSeparator, separator:\"long\", pause:long)"],["Action","lines-steps-summary","[p] (pause:short); [t] count(children/*); [t] \"Rechenschritte\"; [n] . (grammar:layoutSummary)"],["Action","lines-steps","[p] (pause:short); [m] children/* (ctxtFunc:CTFnodeCounter, context:\"Schritt-:\", sepFunc:CTFpauseSeparator, separator:\"long\", pause:long)"],["Action","lines-rows-summary","[p] (pause:short); [t] count(children/*); [t] \"Zeilen\"; [n] . (grammar:layoutSummary)"],["Action","lines-rows","[p] (pause:short); [m] children/* (ctxtFunc:CTFnodeCounter, context:\"Zeile-:\", sepFunc:CTFpauseSeparator, separator:\"long\", pause:long)"],["Action","lines-constraints-summary","[p] (pause:short); [t] count(children/*); [t] \"Bedingungen\"; [n] . (grammar:layoutSummary)"],["Action","lines-constraints","[p] (pause:short); [m] children/* (ctxtFunc:CTFnodeCounter, context:\"Bedingung-:\", sepFunc:CTFpauseSeparator, separator:\"long\", pause:long)"],["Action","bigop","[n] children/*[1]; [t] \"über\"; [n] children/*[2] (pause:short)"],["Action","limboth","[n] children/*[1]; [t] \"von\"; [n] children/*[2]; [t] \"bis\"; [n] children/*[3]"],["Action","limlower","[n] children/*[1]; [t] \"über\"; [n] children/*[2] (pause:short)"],["Action","limupper","[n] children/*[1]; [t] \"unter\"; [n] children/*[2] (pause:short)"],["Action","integral","[t] \"das\"; [n] children/*[1]; [t] \"über\"; [n] children/*[2] (pause:short); [n] children/*[3] (pause:short)"],["Action","integral-novar","[t] \"das\"; [n] children/*[1]; [t] \"über\"; [n] children/*[2] (pause:short)"],["Action","overscript","[n] children/*[1]; [t] \"unter\"; [n] children/*[2] (pause:short)"],["Action","overscript-limits","[n] children/*[1]; [t] \"bis\"; [n] children/*[2]"],["Action","underscript","[n] children/*[1]; [t] \"über\"; [n] children/*[2] (pause:short)"],["Action","underscript-limits","[n] children/*[1]; [t] \"von\"; [n] children/*[2]"],["Action","mixed-number","[n] children/*[1]; [n] children/*[2]"],["Action","number-with-chars","[t] \"Zahl\"; [m] CQFspaceoutNumber (grammar:protected)"],["Action","decimal-period","[t] \"Dezimalbruch\"; [n] children/*[1] (grammar:spaceout); [t] \"Komma mit Periode\"; [n] children/*[3]/children/*[1] (grammar:spaceout)"],["Action","decimal-period-float","[t] \"Dezimalbruch\"; [n] children/*[1] (grammar:spaceout); [t] \"mit Periode\"; [n] children/*[2]/children/*[1] (grammar:spaceout)"],["Action","decimal-period-singular","[t] \"Dezimalbruch\"; [n] children/*[1] (grammar:spaceout); [t] \"Komma mit Periode\"; [n] children/*[3]/children/*[1] (grammar:spaceout)"],["Action","decimal-period-singular-float","[t] \"Dezimalbruch\"; [n] children/*[1] (grammar:spaceout); [t] \"mit Periode\"; [n] children/*[2]/children/*[1] (grammar:spaceout)"],["Action","decimal-point","[t] \"Komma\""],["Action","line-segment","[t] \"die Strecke\"; [n] children/*[1]/children/*[1]; [n] children/*[1]/children/*[2] (pause:short)"],["Action","conjugate","[t] \"die komplexe Konjugation von\"; [n] children/*[1]"],["Action","defined-by","[t] \"ist definiert als\" (pause:short)"],["Action","adorned-sign","[n] children/*[1]; [t] \"Zeichen mit darüberstehendem\"; [n] children/*[2]"],["Action","factorial","[t] \"Fakultät\""],["Action","left-super","[t] \"linker oberer Index\"; [n] text()"],["Action","left-super-list","[t] \"linker oberer Index\"; [m] children/*"],["Action","left-sub","[t] \"linker unterer Index\"; [n] text()"],["Action","left-sub-list","[t] \"linker unterer Index\"; [m] children/*"],["Action","right-super","[t] \"rechter oberer Index\"; [n] text()"],["Action","right-super-list","[t] \"rechter oberer Index\"; [m] children/*"],["Action","right-sub","[t] \"rechter unterer Index\"; [n] text()"],["Action","right-sub-list","[t] \"rechter unterer Index\"; [m] children/*"],["Action","choose","[n] children/*[4] (grammar:combinatorics); [t] \"aus\"; [n] children/*[2] (grammar:combinatorics)"],["Action","permute","[n] children/*[4] (grammar:combinatorics); [t] \"Permutionen von\"; [n] children/*[2] (grammar:combinatorics)"],["Action","unit-square","[t] \"Quadrat\" (join:\"\"); [n] children/*[1] (grammar:lowercase)"],["Action","unit-cubic","[t] \"Kubik\" (join:\"\"); [n] children/*[1] (grammar:lowercase)"],["Action","unit-reciprocal","[n] children/*[1]; [t] \"invers\""],["Action","unit-reciprocal-singular","[t] \"pro\"; [n] children/*[1]"],["Action","unit-divide","[n] children/*[1]; [t] \"pro\"; [n] children/*[2]"]]}
+,
+"de/rules/mathspeak_german.min": 
+{"locale":"de","domain":"mathspeak","modality":"speech","inherits":"base","rules":[["Ignore","vulgar-fraction"],["Precondition","font-number","default","self::number[@font!=\"normal\"]","not(contains(@grammar, \"ignoreFont\"))"],["Precondition","font-double-struck","default","self::*[@font=\"double-struck\"]","name(self::*)!=\"number\"","string-length(text())=1","not(contains(@grammar, \"ignoreFont\"))"],["Precondition","font-number-double-struck","default","self::number[@font=\"double-struck\"]","string-length(text())=1","not(contains(@grammar, \"ignoreFont\"))"],["Precondition","double-overscore","default","self::overscore","children/*[2][@role=\"overaccent\"]","name(children/*[1])=\"overscore\"","children/*[1]/children/*[2][@role=\"overaccent\"]"],["Precondition","double-overscore-brief","brief","self::overscore","children/*[2][@role=\"overaccent\"]","name(children/*[1])=\"overscore\"","children/*[1]/children/*[2][@role=\"overaccent\"]"],["Specialized","double-overscore-brief","brief","sbrief"],["Precondition","double-underscore","default","self::underscore","children/*[2][@role=\"underaccent\"]","name(children/*[1])=\"underscore\"","children/*[1]/children/*[2][@role=\"underaccent\"]"],["Precondition","double-underscore-brief","brief","self::underscore","children/*[2][@role=\"underaccent\"]","name(children/*[1])=\"underscore\"","children/*[1]/children/*[2][@role=\"underaccent\"]"],["Specialized","double-underscore-brief","brief","sbrief"],["Precondition","overbar","default","self::overscore","contains(@role,\"letter\")","children/*[2][@role=\"overaccent\"]","children/*[2][contains(@annotation, \"accent:bar\")]"],["Precondition","underbar","default","self::underscore","contains(@role,\"letter\")","children/*[2][@role=\"underaccent\"]","children/*[2][contains(@annotation, \"accent:bar\")]"],["Precondition","overtilde","default","self::overscore","children/*[2][@role=\"overaccent\"]","contains(@role,\"letter\")","children/*[2][contains(@annotation, \"accent:tilde\")]"],["Precondition","undertilde","default","self::underscore","contains(@role,\"letter\")","children/*[2][@role=\"underaccent\"]","children/*[2][contains(@annotation, \"accent:tilde\")]"],["Alias","overbar","self::enclose","@role=\"top\""],["Alias","underbar","self::enclose","@role=\"bottom\""],["Precondition","binomial-brief","brief","self::vector","@role=\"binomial\""],["Precondition","cases-brief","brief","self::cases"]]}
+,
+"de/rules/mathspeak_german_actions.min": 
+{"locale":"de","domain":"mathspeak","modality":"speech","kind":"actions","rules":[["Action","collapsed","[n] . (engine:modality=summary, grammar:collapsed); [t] \"kollabiert\""],["Action","blank-cell-empty","[t] \"leer\""],["Action","blank-line-empty","[t] \"leer\""],["Action","font","[t] @font (grammar:localFont); [n] . (grammar:ignoreFont=@font)"],["Action","font-number","[t] @font (grammar:localFontNumber); [n] . (grammar:ignoreFont=@font)"],["Action","font-double-struck","[n] . (grammar:ignoreFont=@font); [t] @font (grammar:localFont)"],["Action","font-number-double-struck","[n] . (grammar:ignoreFont=@font); [t] @font (grammar:localFontNumber)"],["Action","mixed-number","[n] children/*[1]; [n] children/*[2]"],["Action","number-with-chars","[t] \"Zahl\"; [m] CQFspaceoutNumber (grammar:protected)"],["Action","number-as-upper-word","[t] \"Wort groß\"; [t] CSFspaceoutText"],["Action","number-baseline","[t] \"Grundlinie\"; [n] . (grammar:baseline)"],["Action","number-baseline-brief","[t] \"Grund\"; [n] . (grammar:baseline)"],["Action","number-baseline-font","[t] \"Grundlinie\"; [t] @font (grammar:localFont); [n] . (grammar:ignoreFont=@font)"],["Action","number-baseline-font-brief","[t] \"Grund\"; [t] @font (grammar:localFont); [n] . (grammar:ignoreFont=@font)"],["Action","negative-number","[t] \"minus\"; [n] children/*[1]"],["Action","negative","[t] \"minus\"; [n] children/*[1]"],["Action","division","[n] children/*[1]; [t] \"geteilt durch\"; [n] children/*[2]"],["Action","subtraction","[m] children/* (separator:\"minus\")"],["Action","fences-neutral","[t] \"Anfang Betrag\"; [n] children/*[1]; [t] \"Ende Betrag\""],["Action","fences-neutral-sbrief","[t] \"Betrag\"; [n] children/*[1]; [t] \"Ende Betrag\""],["Action","fences-metric","[t] \"Anfang Metrik\"; [n] children/*[1]; [t] \"Ende Metrik\""],["Action","fences-metric-sbrief","[t] \"Metrik\"; [n] children/*[1]; [t] \"Ende Metrik\""],["Action","empty-set","[t] \"leere Menge\""],["Action","fences-set","[t] \"Anfang Menge\"; [n] children/*[1]; [t] \"Ende Menge\""],["Action","fences-set-sbrief","[t] \"Menge\"; [n] children/*[1]; [t] \"Ende Menge\""],["Action","factorial","[t] \"Fakultät\""],["Action","minus","[t] \"minus\""],["Action","vulgar-fraction","[t] CSFvulgarFraction (grammar:correctOne)"],["Action","continued-fraction-outer","[t] \"Kettenbruch\"; [n] children/*[1]; [t] \"durch\"; [n] children/*[2]"],["Action","continued-fraction-inner","[t] \"Anfang Bruch\"; [n] children/*[1]; [t] \"durch\"; [n] children/*[2]"],["Action","continued-fraction-inner-sbrief","[t] \"Bruch\"; [n] children/*[1]; [t] \"durch\"; [n] children/*[2]"],["Action","limboth-end","[n] children/*[1]; [t] CSFunderscript; [n] children/*[2]; [t] CSFoverscript; [n] children/*[3]; [t] \"Ende Überschrift\""],["Action","limlower-end","[n] children/*[1]; [t] CSFunderscript; [n] children/*[2]; [t] \"Ende Unterschrift\""],["Action","limupper-end","[n] children/*[1]; [t] CSFoverscript; [n] children/*[2]; [t] \"Ende Überschrift\""],["Action","integral","[n] children/*[1]; [t] \"Index\"; [n] children/*[2]; [t] \"Hoch\"; [n] children/*[3]; [t] \"Grundlinie\""],["Action","integral-brief","[n] children/*[1]; [t] \"Index\"; [n] children/*[2]; [t] \"Hoch\"; [n] children/*[3]; [t] \"Base\""],["Action","square","[n] children/*[1]; [t] \"Quadrat\""],["Action","cube","[n] children/*[1]; [t] \"Kubik\""],["Action","prime","[n] children/*[1]; [n] children/*[2]"],["Action","counted-prime","[t] count(children/*) (grammar:numbers2alpha); [t] \"Strich\""],["Action","counted-prime-multichar","[t] string-length(text()) (grammar:numbers2alpha); [t] \"Strich\""],["Action","overscore","[t] \"modifiziert oben\"; [n] children/*[1]; [t] \"mit\"; [n] children/*[2]"],["Action","overscore-brief","[t] \"mod oben\"; [n] children/*[1]; [t] \"mit\"; [n] children/*[2]"],["Action","double-overscore","[t] \"modifiziert oben oben\"; [n] children/*[1]; [t] \"mit\"; [n] children/*[2]"],["Action","double-overscore-brief","[t] \"mod oben oben\"; [n] children/*[1]; [t] \"mit\"; [n] children/*[2]"],["Action","underscore","[t] \"modifiziert unten\"; [n] children/*[1]; [t] \"mit\"; [n] children/*[2]"],["Action","underscore-brief","[t] \"mod unten\"; [n] children/*[1]; [t] \"mit\"; [n] children/*[2]"],["Action","double-underscore","[t] \"modifiziert unten unten\"; [n] children/*[1]; [t] \"mit\"; [n] children/*[2]"],["Action","double-underscore-brief","[t] \"mod unten unten\"; [n] children/*[1]; [t] \"mit\"; [n] children/*[2]"],["Action","overbar","[n] children/*[1]; [t] \"Überstrich\""],["Action","underbar","[n] children/*[1]; [t] \"Unterstrich\""],["Action","overtilde","[n] children/*[1]; [t] \"Tilde oben\""],["Action","undertilde","[n] children/*[1]; [t] \"Tilde unten\""],["Action","matrix","[t] \"Anfang\"; [t] count(children/*); [t] \"mal\"; [t] count(children/*[1]/children/*); [t] \"Matrize\"; [m] children/* (ctxtFunc:CTFordinalCounter, context:\"Zeile \"); [t] \"Ende Matrize\""],["Action","matrix-sbrief","[t] count(children/*); [t] \"mal\"; [t] count(children/*[1]/children/*); [t] \"Matrize\"; [m] children/* (ctxtFunc:CTFordinalCounter, context:\"Zeile \"); [t] \"Ende Matrize\""],["Action","matrix-row","[m] children/* (ctxtFunc:CTFordinalCounter, context:\"Spalte\", pause:200)"],["Action","row-with-label","[t] \"mit Bezeichner\"; [n] content/*[1]; [t] \"Ende Bezeichner\" (pause:200); [m] children/* (ctxtFunc:CTFordinalCounter, context:\"Spalte\")"],["Action","row-with-label-brief","[t] \"Bezeichner\"; [n] content/*[1]; [m] children/* (ctxtFunc:CTFordinalCounter, context:\"Spalte\")"],["Action","row-with-text-label","[t] \"Bezeichner\"; [t] CSFRemoveParens; [m] children/* (ctxtFunc:CTFordinalCounter, context:\"Spalte\")"],["Action","empty-row","[t] \"leer\""],["Action","empty-cell","[t] \"leer\" (pause:300)"],["Action","determinant","[t] \"Anfang\"; [t] count(children/*); [t] \"mal\"; [t] count(children/*[1]/children/*); [t] \"Determinante\"; [m] children/* (ctxtFunc:CTFordinalCounter, context:\"Zeile \"); [t] \"Ende Determinante\""],["Action","determinant-sbrief","[t] count(children/*); [t] \"mal\"; [t] count(children/*[1]/children/*); [t] \"Determinante\"; [m] children/* (ctxtFunc:CTFordinalCounter, context:\"Zeile \"); [t] \"Ende Determinante\""],["Action","determinant-simple","[t] \"Anfang\"; [t] count(children/*); [t] \"mal\"; [t] count(children/*[1]/children/*); [t] \"Determinante\"; [m] children/* (ctxtFunc:CTFordinalCounter, context:\"Zeile\", grammar:simpleDet); [t] \"Ende Determinante\""],["Action","determinant-simple-sbrief","[t] count(children/*); [t] \"mal\"; [t] count(children/*[1]/children/*); [t] \"Determinante\"; [m] children/* (ctxtFunc:CTFordinalCounter, context:\"Zeile\", grammar:simpleDet); [t] \"Ende Determinante\""],["Action","layout","[t] \"Anfang Anordnung\"; [m] children/* (ctxtFunc:CTFordinalCounter, context:\"Zeile \"); [t] \"Ende Anordnung\""],["Action","layout-sbrief","[t] \"Anordnung\"; [m] children/* (ctxtFunc:CTFordinalCounter, context:\"Zeile \"); [t] \"Ende Anordnung\""],["Action","binomial","[t] \"Anfang Binomialkoeffizient\"; [n] children/*[2]/children/*[1]; [t] \"aus\"; [n] children/*[1]/children/*[1]; [t] \"Ende Binomialkoeffizient\""],["Action","binomial-brief","[t] \"Anfang Binomial\"; [n] children/*[2]/children/*[1]; [t] \"aus\"; [n] children/*[1]/children/*[1]; [t] \"Ende Binomial\""],["Action","binomial-sbrief","[t] \"Binomial\"; [n] children/*[2]/children/*[1]; [t] \"aus\"; [n] children/*[1]/children/*[1]; [t] \"Ende Binomial\""],["Action","cases","[t] \"Anfang Fallunterscheidung\"; [t] \"große\"; [n] content/*[1]; [m] children/* (ctxtFunc:CTFordinalCounter, context:\"Zeile \"); [t] \"Ende Fallunterscheidung\""],["Action","cases-brief","[t] \"Anfang Fälle\"; [t] \"große\"; [n] content/*[1]; [m] children/* (ctxtFunc:CTFordinalCounter, context:\"Zeile \"); [t] \"Ende Fälle\""],["Action","cases-sbrief","[t] \"Fälle\"; [t] \"große\"; [n] content/*[1]; [m] children/* (ctxtFunc:CTFordinalCounter, context:\"Zeile \"); [t] \"Ende Fälle\""],["Action","line-with-label","[t] \"mit Bezeichner\"; [n] content/*[1]; [t] \"Ende Bezeichner\" (pause:200); [m] children/*"],["Action","line-with-label-brief","[t] \"Bezeichner\"; [n] content/*[1] (pause:200); [m] children/*"],["Action","line-with-text-label","[t] \"Bezeichner\"; [t] CSFRemoveParens; [m] children/*"],["Action","empty-line","[t] \"leer\""],["Action","empty-line-with-label","[t] \"mit Bezeichner\"; [n] content/*[1]; [t] \"Ende Bezeichner\" (pause:200); [t] \"leer\""],["Action","empty-line-with-label-brief","[t] \"Bezeichner\"; [n] content/*[1] (pause:200); [t] \"leer\""],["Action","enclose","[t] \"Anfang Umschließung\"; [t] @role (grammar:localEnclose); [n] children/*[1]; [t] \"Ende Umschließung\""],["Action","leftbar","[t] \"senkrechter Strich\"; [n] children/*[1]"],["Action","rightbar","[n] children/*[1]; [t] \"senkrechter Strich\""],["Action","crossout","[t] \"durchgestrichen\"; [n] children/*[1]; [t] \"Ende duchgestrichen\""],["Action","cancel","[t] \"durchgestrichen\"; [n] children/*[1]/children/*[1]; [t] \"mit\"; [n] children/*[2]; [t] \"Ende duchgestrichen\""],["Action","cancel-reverse","[t] \"durchgestrichen\"; [n] children/*[2]/children/*[1]; [t] \"mit\"; [n] children/*[1]; [t] \"Ende duchgestrichen\""],["Action","multi-inference","[t] \"Schlussregel\"; [m] content/*; [t] \"mit Folgerung\"; [n] children/*[1]; [t] \"aus\"; [t] count(children/*[2]/children/*); [t] \"Prämissen\""],["Action","inference","[t] \"Schlussregel\"; [m] content/*; [t] \"mit Folgerung\"; [n] children/*[1]; [t] \"aus\"; [t] count(children/*[2]/children/*); [t] \"Prämisse\""],["Action","premise","[m] children/* (ctxtFunc:CTFordinalCounter, context:\"Prämisse \")"],["Action","conclusion","[n] children/*[1]"],["Action","label","[t] \"Regel\"; [n] children/*[1]"],["Action","axiom","[t] \"Axiom\"; [m] children/*[1]"],["Action","empty-axiom","[t] \"leeres Axiom\""]]}
+,
+"de/rules/prefix_german.min": 
+{"modality":"prefix","locale":"de","inherits":"base","domain":"default","rules":[]}
+,
+"de/rules/prefix_german_actions.min": 
+{"modality":"prefix","locale":"de","domain":"default","kind":"actions","rules":[["Action","numerator","[t] \"Zähler\" (pause:200)"],["Action","denominator","[t] \"Nenner\" (pause:200)"],["Action","base","[t] \"Basis\" (pause:200)"],["Action","exponent","[t] \"Exponent\" (pause:200)"],["Action","subscript","[t] \"Index\" (pause:200)"],["Action","overscript","[t] \"Oberer Grenzwert\" (pause:200)"],["Action","underscript","[t] \"Unterer Grenzwert\" (pause:200)"],["Action","radicand","[t] \"Radikand\" (pause:200)"],["Action","index","[t] \"Wurzelexponent\" (pause:200)"],["Action","leftsub","[t] \"linker unterer Index\" (pause:200)"],["Action","leftsub-counted","[t] CSFordinalPosition; [t] \"linker unterer Index\" (pause:200)"],["Action","leftsuper","[t] \"linker oberer Index\" (pause:200)"],["Action","leftsuper-counted","[t] CSFordinalPosition; [t] \"linker oberer Index\" (pause:200)"],["Action","rightsub","[t] \"rechter unterer Index\" (pause:200)"],["Action","rightsub-counted","[t] CSFordinalPosition; [t] \"rechter unterer Index\" (pause:200)"],["Action","rightsuper","[t] \"rechter oberer Index\" (pause:200)"],["Action","rightsuper-counted","[t] CSFordinalPosition; [t] \"rechter oberer Index\" (pause:200)"],["Action","choice","[t] \"Grundgesamtheit\" (pause:200)"],["Action","select","[t] \"Stichprobengröße\" (pause:200)"],["Action","row","[t] CSFordinalPosition; [t] \"Zeile\" (pause:200)"],["Action","cell","[n] ../..; [t] CSFordinalPosition; [t] \"Spalte\" (pause:200)"],["Action","cell-simple","[t] CSFordinalPosition; [t] \"Spalte\" (pause:200)"]]}
+,
+"de/rules/summary_german.min": 
+{"modality":"summary","locale":"de","inherits":"base","rules":[]}
+,
+"de/rules/summary_german_actions.min": 
+{"modality":"summary","locale":"de","kind":"actions","rules":[["Action","abstr-identifier-long","[t] \"langer Bezeichner\""],["Action","abstr-identifier","[t] \"Bezeichner\""],["Action","abstr-number-long","[t] \"lange Zahl\""],["Action","abstr-number","[t] \"Zahl\""],["Action","abstr-mixed-number-long","[t] \"langer gemischter Bruch\""],["Action","abstr-mixed-number","[t] \"gemischter Bruch\""],["Action","abstr-text","[t] \"Text\""],["Action","abstr-function","[t] \"Funktionsausdruck\""],["Action","abstr-function-brief","[t] \"Funktion\""],["Action","abstr-lim","[t] \"Grenzwertfunktion\""],["Action","abstr-lim-brief","[t] \"Grenzwert\""],["Action","abstr-fraction","[t] \"Bruch\""],["Action","abstr-fraction-brief","[t] \"Bruch\""],["Action","abstr-continued-fraction","[t] \"Kettenbruch\""],["Action","abstr-continued-fraction-brief","[t] \"Kettenbruch\""],["Action","abstr-sqrt","[t] \"Quadratwurzel\""],["Action","abstr-sqrt-nested","[t] \"verschachtelte Quadratwurzel\""],["Action","abstr-root-end","[t] \"Wurzel mit Exponent\"; [n] children/*[1] (engine:modality=speech); [t] \"Ende Exponent\""],["Action","abstr-root","[t] \"Wurzel mit Exponent\"; [n] children/*[1] (engine:modality=speech)"],["Action","abstr-root-brief","[t] \"Wurzel\""],["Action","abstr-root-nested-end","[t] \"verschachtelte Wurzel mit Exponent\"; [n] children/*[1] (engine:modality=speech); [t] \"Ende Exponent\""],["Action","abstr-root-nested","[t] \"verschachtelte Wurzel mit Exponent\"; [n] children/*[1] (engine:modality=speech)"],["Action","abstr-root-nested-brief","[t] \"verschachtelte Wurzel\""],["Action","abstr-superscript","[t] \"Potenz\""],["Action","abstr-subscript","[t] \"Index\""],["Action","abstr-subsup","[t] \"Potenz mit Index\""],["Action","abstr-infixop","[t] @role (grammar:localRole); [t] \"mit\"; [t] count(./children/*); [t] \"Elementen\""],["Action","abstr-infixop-var","[t] @role (grammar:localRole); [t] \"mit veränderlicher Anzahl an Elementen\""],["Action","abstr-infixop-brief","[t] @role (grammar:localRole)"],["Action","abstr-addition","[t] \"Summe mit\"; [t] count(./children/*); [t] \"Summanden\""],["Action","abstr-addition-brief","[t] \"Summe\""],["Action","abstr-addition-var","[t] \"Summe mit veränderlicher Anzahl an Summanden\""],["Action","abstr-multiplication","[t] \"Produkt mit\"; [t] count(./children/*); [t] \"Faktoren\""],["Action","abstr-multiplication-brief","[t] \"Produkt\""],["Action","abstr-multiplication-var","[t] \"Produkt mit veränderlicher Anzahl an Faktoren\""],["Action","abstr-vector","[t] count(./children/*); [t] \"dimensionaler Vektor\""],["Action","abstr-vector-brief","[t] \"Vektor\""],["Action","abstr-vector-var","[t] \"n dimensionaler Vektor\""],["Action","abstr-binomial","[t] \"Binomialkoeffizient\""],["Action","abstr-determinant","[t] count(./children/*); [t] \"dimensionale Determinante\""],["Action","abstr-determinant-brief","[t] \"Determinante\""],["Action","abstr-determinant-var","[t] \"n dimensionale Determinante\""],["Action","abstr-squarematrix","[t] count(./children/*); [t] \"dimensionale quadratische Matrize\""],["Action","abstr-squarematrix-brief","[t] \"quadratische Matrize\""],["Action","abstr-rowvector","[t] count(./children/row/children/*); [t] \"dimensionaler Zeilenvektor\""],["Action","abstr-rowvector-brief","[t] \"Zeilenvektor\""],["Action","abstr-rowvector-var","[t] \"n dimensionaler Zeilenvektor\""],["Action","abstr-matrix","[t] count(children/*); [t] \"mal\"; [t] count(children/*[1]/children/*); [t] \"Matrize\""],["Action","abstr-matrix-brief","[t] \"Matrize\""],["Action","abstr-matrix-var","[t] \"n mal m dimensionale Matrize\""],["Action","abstr-cases","[t] \"Fallunterscheidung\"; [t] \"mit\"; [t] count(children/*); [t] \"Fällen\""],["Action","abstr-cases-brief","[t] \"Fallunterscheidung\""],["Action","abstr-cases-var","[t] \"Fallunterscheidung mit veränderlicher Anzahl an Fällen\""],["Action","abstr-punctuated","[t] \"mit\"; [n] content/*[1]; [t] \"getrennte Liste der Länge\"; [t] count(children/*) - count(content/*)"],["Action","abstr-punctuated-brief","[t] \"mit\"; [n] content/*[1]; [t] \"getrennte Liste\""],["Action","abstr-punctuated-var","[t] \"mit\"; [n] content/*[1]; [t] \"getrennte Liste\"; [t] \"veränderlicher Länge\""],["Action","abstr-bigop","[n] content/*[1]"],["Action","abstr-integral","[t] \"Integral\""],["Action","abstr-relation","[t] @role (grammar:localRole)"],["Action","abstr-relation-seq","[t] @role (join:\"\", grammar:localRole); [t] \"ssequenz\"; [t] \"mit\"; [t] count(./children/*); [t] \"Elementen\""],["Action","abstr-relation-seq-brief","[t] @role (join:\"\", grammar:localRole); [t] \"ssequenz\""],["Action","abstr-relation-var","[t] @role (join:\"\", grammar:localRole); [t] \"ssequenz\"; [t] \"mit veränderlicher Anzahl an Elementen\""],["Action","abstr-multirel","[t] \"Relationsequenz\"; [t] \"mit\"; [t] count(./children/*); [t] \"Elementen\""],["Action","abstr-multirel-brief","[t] \"Relationsequenz\""],["Action","abstr-multirel-var","[t] \"Relationsequenz mit veränderlicher Anzahl an Elementen\""],["Action","abstr-table","[t] \"Tabelle mit\"; [t] count(children/*); [t] \"Zeilen und\"; [t] count(children/*[1]/children/*); [t] \"Spalten\""],["Action","abstr-line","[t] \"in\"; [t] @role (grammar:localRole)"],["Action","abstr-row","[t] \"in\"; [t] @role (grammar:localRole); [t] count(preceding-sibling::..); [t] \"mit\"; [t] count(children/*); [t] \"Spalten\""],["Action","abstr-cell","[t] \"in\"; [t] @role (grammar:localRole)"]]}
+}
+
diff --git a/devel/_static/mathjax/sre/mathmaps/en.json b/devel/_static/mathjax/sre/mathmaps/en.json
new file mode 100644
index 0000000000..d111f0e7b5
--- /dev/null
+++ b/devel/_static/mathjax/sre/mathmaps/en.json
@@ -0,0 +1,158 @@
+{
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+,
+"en/messages/messages.min": 
+{"kind":"messages","locale":"en","messages":{"MS":{"START":"Start","FRAC_V":"Fraction","FRAC_B":"Frac","FRAC_S":"Frac","END":"End","FRAC_OVER":"Over","TWICE":"Twice","NEST_FRAC":"Nest","ENDFRAC":"EndFrac","SUPER":"Super","SUB":"Sub","SUP":"Sup","SUPERSCRIPT":"Superscript","SUBSCRIPT":"Subscript","BASELINE":"Baseline","BASE":"Base","NESTED":"Nested","NEST_ROOT":"Nest","STARTROOT":"StartRoot","ENDROOT":"EndRoot","ROOTINDEX":"RootIndex","ROOT":"Root","INDEX":"Index","UNDER":"Under","UNDERSCRIPT":"Underscript","OVER":"Over","OVERSCRIPT":"Overscript","ENDSCRIPTS":"Endscripts"},"MSroots":{},"font":{"bold":"bold","bold-fraktur":"bold fraktur","bold-italic":"bold italic","bold-script":"bold script","caligraphic":"calligraphic","caligraphic-bold":"calligraphic bold","double-struck":"double struck","double-struck-italic":"double struck italic","fraktur":"fraktur","fullwidth":"fullwidth","italic":"italic","monospace":"monospace","normal":"normal","oldstyle":"oldstyle","oldstyle-bold":"oldstyle bold","script":"script","sans-serif":"sans serif","sans-serif-italic":"sans serif italic","sans-serif-bold":"sans serif bold","sans-serif-bold-italic":"sans serif bold italic","unknown":"unknown"},"embellish":{"super":"super","sub":"sub","circled":"circled","parenthesized":"parenthesized","period":["period","postfixCombiner"],"negative-circled":"black circled","double-circled":"double circled","circled-sans-serif":"circled sans serif","negative-circled-sans-serif":"black circled sans serif","comma":["comma","postfixCombiner"],"squared":"squared","negative-squared":"black squared"},"role":{"addition":"addition","multiplication":"multiplication","subtraction":"subtraction","division":"division","equality":"equality","inequality":"inequality","element":"element","arrow":"arrow","determinant":"determinant","rowvector":"row vector","binomial":"binomial","squarematrix":"square matrix","multiline":"multiple lines","matrix":"matrix","vector":"vector","cases":"case statement","table":"table","unknown":"unknown"},"enclose":{"longdiv":"long division","actuarial":"actuarial symbol","radical":"square root","box":"box","roundedbox":"rounded box","circle":"circle","left":"left vertical-line","right":"right vertical-line","top":"overbar","bottom":"underbar","updiagonalstrike":"crossout","downdiagonalstrike":"crossout","verticalstrike":"vertical strikeout","horizontalstrike":"crossout","madruwb":"Arabic factorial symbol","updiagonalarrow":"diagonal arrow","phasorangle":"phasor angle","unknown":"long division"},"navigate":{"COLLAPSIBLE":"collapsible","EXPANDABLE":"expandable","LEVEL":"Level"},"regexp":{"TEXT":"a-zA-Z","NUMBER":"((\\d{1,3})(?=(,| ))((,| )\\d{3})*(\\.\\d+)?)|^\\d*\\.\\d+|^\\d+","DECIMAL_MARK":"\\.","DIGIT_GROUP":",","JOINER_SUBSUPER":" ","JOINER_FRAC":""},"unitTimes":""}}
+,
+"en/messages/numbers.min": 
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diff --git a/devel/_static/mathjax/sre/mathmaps/es.json b/devel/_static/mathjax/sre/mathmaps/es.json
new file mode 100644
index 0000000000..0de855eea8
--- /dev/null
+++ b/devel/_static/mathjax/sre/mathmaps/es.json
@@ -0,0 +1,140 @@
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+,
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+{"locale":"es","inherits":"base","modality":"prefix","domain":"default","rules":[]}
+,
+"es/rules/prefix_spanish_actions.min": 
+{"locale":"es","modality":"prefix","domain":"default","kind":"actions","rules":[["Action","numerator","[t] \"numerador\" (pause:200)"],["Action","denominator","[t] \"denominador\" (pause:200)"],["Action","base","[t] \"base\" (pause:200)"],["Action","exponent","[t] \"exponente\" (pause:200)"],["Action","subscript","[t] \"subíndice\" (pause:200)"],["Action","overscript","[t] \"sobreíndice\" (pause:200)"],["Action","underscript","[t] \"bajoíndice\" (pause:200)"],["Action","radicand","[t] \"radicand\" (pause:200)"],["Action","index","[t] \"índice\" (pause:200)"],["Action","leftsub","[t] \"subíndice izquierdo\" (pause:200)"],["Action","leftsub-counted","[t] CSFordinalPosition (grammar:gender=\"m\"); [t] \"subíndice izquierdo\" (pause:200)"],["Action","leftsuper","[t] \"superíndice izquierdo\" (pause:200)"],["Action","leftsuper-counted","[t] CSFordinalPosition (grammar:gender=\"m\"); [t] \"superíndice izquierdo\" (pause:200)"],["Action","rightsub","[t] \"subíndice derecho\" (pause:200)"],["Action","rightsub-counted","[t] CSFordinalPosition (grammar:gender=\"m\"); [t] \"subíndice derecho\" (pause:200)"],["Action","rightsuper","[t] \"superíndice derecho\" (pause:200)"],["Action","rightsuper-counted","[t] CSFordinalPosition (grammar:gender=\"m\"); [t] \"superíndice derecho\" (pause:200)"],["Action","choice","[t] \"cantidad de elección\" (pause:200)"],["Action","select","[t] \"cantidad de selección\" (pause:200)"],["Action","row","[t] CSFordinalPosition (grammar:gender=\"f\"); [t] \"fila\" (pause:200)"],["Action","cell","[n] ../..; [t] CSFordinalPosition (grammar:gender=\"f\"); [t] \"columna\" (pause:200)"],["Action","cell-simple","[t] CSFordinalPosition (grammar:gender=\"f\"); [t] \"columna\" (pause:200)"]]}
+,
+"es/rules/summary_spanish.min": 
+{"locale":"es","modality":"summary","inherits":"base","rules":[]}
+,
+"es/rules/summary_spanish_actions.min": 
+{"locale":"es","modality":"summary","kind":"actions","rules":[["Action","abstr-identifier-long","[t] \"identificador largo\""],["Action","abstr-identifier","[t] \"identificador\""],["Action","abstr-number-long","[t] \"número largo\""],["Action","abstr-number","[t] \"número\""],["Action","abstr-mixed-number-long","[t] \"número largo mixto\""],["Action","abstr-mixed-number","[t] \"número mixto\""],["Action","abstr-text","[t] \"texto\""],["Action","abstr-function","[t] \"expresión funcional\""],["Action","abstr-function-brief","[t] \"función\""],["Action","abstr-lim","[t] \"función de límite\""],["Action","abstr-lim-brief","[t] \"límite\""],["Action","abstr-fraction","[t] \"fracción\""],["Action","abstr-fraction-brief","[t] \"frac\""],["Action","abstr-continued-fraction","[t] \"fracción continua\""],["Action","abstr-continued-fraction-brief","[t] \"frac continua\""],["Action","abstr-sqrt","[t] \"raíz cuadrada\""],["Action","abstr-sqrt-nested","[t] \"raíz cuadrada anidada\""],["Action","abstr-root-end","[t] \"raíz del índice\"; [n] children/*[1] (engine:modality=speech); [t] \"finalizar índice\""],["Action","abstr-root","[t] \"raíz del índice\"; [n] children/*[1] (engine:modality=speech)"],["Action","abstr-root-brief","[t] \"raíz\""],["Action","abstr-root-nested-end","[t] \"raíz anidada del índice\"; [n] children/*[1] (engine:modality=speech); [t] \"finalizar índice\""],["Action","abstr-root-nested","[t] \"raíz anidada del índice\"; [n] children/*[1] (engine:modality=speech)"],["Action","abstr-root-nested-brief","[t] \"raíz anidada\""],["Action","abstr-superscript","[t] \"potencia\""],["Action","abstr-subscript","[t] \"subíndice\""],["Action","abstr-subsup","[t] \"potencia con subíndice\""],["Action","abstr-infixop","[t] @role (grammar:localRole); [t] \"con\"; [t] count(./children/*); [t] \"elementos\""],["Action","abstr-infixop-var","[t] @role (grammar:localRole); [t] \"con una cantidad variable de elementos\""],["Action","abstr-infixop-brief","[t] @role (grammar:localRole)"],["Action","abstr-addition","[t] \"suma con\"; [t] count(./children/*); [t] \"sumandos\""],["Action","abstr-addition-brief","[t] \"suma\""],["Action","abstr-addition-var","[t] \"suma con número variable de sumandos\""],["Action","abstr-multiplication","[t] \"producto con\"; [t] count(./children/*); [t] \"factores\""],["Action","abstr-multiplication-brief","[t] \"producto\""],["Action","abstr-multiplication-var","[t] \"producto con una cantidad variable de factores\""],["Action","abstr-vector","[t] \"vector de dimensión\"; [t] count(./children/*)"],["Action","abstr-vector-brief","[t] \"vector\""],["Action","abstr-vector-var","[t] \"vector de dimensión n\""],["Action","abstr-binomial","[t] \"binomio\""],["Action","abstr-determinant","[t] \"determinante de dimensión\"; [t] count(./children/*)"],["Action","abstr-determinant-brief","[t] \"determinante\""],["Action","abstr-determinant-var","[t] \"determinante de dimensión n\""],["Action","abstr-squarematrix","[t] \"matriz cuadrada de dimensión\"; [t] count(./children/*)"],["Action","abstr-squarematrix-brief","[t] \"matriz cuadrada\""],["Action","abstr-rowvector","[t] \"vector fila de dimensión\"; 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diff --git a/devel/_static/mathjax/sre/mathmaps/fr.json b/devel/_static/mathjax/sre/mathmaps/fr.json
new file mode 100644
index 0000000000..e1140b6d3e
--- /dev/null
+++ b/devel/_static/mathjax/sre/mathmaps/fr.json
@@ -0,0 +1,146 @@
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+,
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+{"locale":"fr","modality":"speech","domain":"mathspeak","inherits":"romance","rules":[["Ignore","subscript-simple","default"],["Ignore","prime-subscript-simple","default"],["Precondition","overbar-enclose","default","self::enclose","@role=\"top\""],["Precondition","underbar-enclose","default","self::enclose","@role=\"bottom\""]]}
+,
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+,
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+{"locale":"fr","inherits":"base","modality":"prefix","domain":"default","rules":[]}
+,
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+{"locale":"fr","modality":"prefix","domain":"default","kind":"actions","rules":[["Action","numerator","[t] \"numérateur\" (pause:200)"],["Action","denominator","[t] \"dénominateur\" (pause:200)"],["Action","base","[t] \"base\" (pause:200)"],["Action","exponent","[t] \"exposant\" (pause:200)"],["Action","subscript","[t] \"indice\" (pause:200)"],["Action","overscript","[t] \"indice suscrit\" (pause:200)"],["Action","underscript","[t] \"indice souscrit\" (pause:200)"],["Action","radicand","[t] \"radicande\" (pause:200)"],["Action","index","[t] \"indice\" (pause:200)"],["Action","leftsub","[t] \"indice inférieur gauche\" (pause:200)"],["Action","leftsub-counted","[t] CSFordinalPosition (grammar:gender=\"m\"); [t] \"indice inférieur gauche\" (pause:200)"],["Action","leftsuper","[t] \"indice supérieur gauche\" (pause:200)"],["Action","leftsuper-counted","[t] CSFordinalPosition (grammar:gender=\"m\"); [t] \"indice supérieur gauche\" (pause:200)"],["Action","rightsub","[t] \"indice inférieur droite\" (pause:200)"],["Action","rightsub-counted","[t] CSFordinalPosition (grammar:gender=\"m\"); [t] \"indice inférieur droite\" (pause:200)"],["Action","rightsuper","[t] \"indice supérieur droite\" (pause:200)"],["Action","rightsuper-counted","[t] CSFordinalPosition (grammar:gender=\"m\"); [t] \"indice supérieur droite\" (pause:200)"],["Action","choice","[t] \"nombre d'éléments disponibles\" (pause:200)"],["Action","select","[t] \"nombre d'éléments choisis\" (pause:200)"],["Action","row","[t] CSFordinalPosition (grammar:gender=\"f\"); [t] \"rangée\" (pause:200)"],["Action","cell","[n] ../..; [t] CSFordinalPosition (grammar:gender=\"f\"); [t] \"colonne\" (pause:200)"],["Action","cell-simple","[t] CSFordinalPosition (grammar:gender=\"f\"); [t] \"colonne\" (pause:200)"]]}
+,
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+{"locale":"fr","modality":"summary","inherits":"romance","rules":[]}
+,
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+{"locale":"fr","modality":"summary","kind":"actions","rules":[["Action","collapsed-masculine","[t] \"compressé\""],["Action","collapsed-feminine","[t] \"compressée\""],["Action","abstr-identifier-long","[t] \"identifiant long\"; [n] . (grammar:gender=\"m\")"],["Action","abstr-identifier","[t] \"identifiant\"; [n] . (grammar:gender=\"m\")"],["Action","abstr-number-long","[t] \"nombre long\"; [n] . (grammar:gender=\"m\")"],["Action","abstr-number","[t] \"nombre\"; [n] . (grammar:gender=\"m\")"],["Action","abstr-mixed-number-long","[t] \"nombre fractionnaire long\"; [n] . (grammar:gender=\"m\")"],["Action","abstr-mixed-number","[t] \"nombre fractionnaire\"; [n] . (grammar:gender=\"m\")"],["Action","abstr-text","[t] \"texte\"; [n] . (grammar:gender=\"m\")"],["Action","abstr-function","[t] \"expression fonctionnelle\"; [n] . (grammar:gender=\"f\")"],["Action","abstr-function-brief","[t] \"fonction\"; [n] . 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diff --git a/devel/_static/mathjax/sre/mathmaps/hi.json b/devel/_static/mathjax/sre/mathmaps/hi.json
new file mode 100644
index 0000000000..d08f778ae3
--- /dev/null
+++ b/devel/_static/mathjax/sre/mathmaps/hi.json
@@ -0,0 +1,146 @@
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[t] \"का प्रतिफलन\"; [n] children/*[1]/children/*[1] (pause:short)"],["Action","appl-times","[p] (pause:short); [n] children/*[1]; [t] \"गुना\"; [n] children/*[2] (pause:short)"],["Action","function-prefix","[n] children/*[1]; [n] children/*[2]"],["Action","function-prefix-simple-arg","[n] children/*[1]; [n] children/*[2]"],["Action","function-prefix-embell","[p] (pause:short); [n] children/*[1]; [n] children/*[2] (pause:short)"],["Action","function-prefix-fenced-or-frac-arg","[p] (pause:short); [n] children/*[1]; [t] \"का फलन\"; [n] children/*[2] (pause:short)"],["Action","function-prefix-subscript","[p] (pause:short); [n] children/*[1]; [t] \"का फलन\" (pause:short); [n] children/*[2] (pause:short)"],["Action","function-ln","[n] children/*[1]; [n] children/*[2]"],["Action","function-ln-pause","[n] children/*[1]; [n] children/*[2] (pause:short)"],["Action","function-ln-of","[n] children/*[1]; [t] \"का फलन\"; [n] children/*[2] (pause:short)"],["Action","function-prefix-as-exp","[n] children/*[1]; [t] \"का फलन\" (pause:short); [n] children/*[2] (pause:short)"],["Action","function-prefix-subscript-as-exp","[n] children/*[1]; [t] \"का फलन\" (pause:short); [n] children/*[2] (pause:short)"],["Action","function-prefix-hyper","[p] (pause:short); [n] children/*[1]; [t] \"का फलन\"; [n] children/*[2] (pause:short)"],["Action","function-prefix-inverse","[p] (pause:short); [t] \"व्युत्क्रम\"; [n] children/*[1]/children/*[1]; [t] \"का फलन\"; [n] children/*[2] (pause:short)"],["Action","appl-triginverse","[p] (pause:short); [n] children/*[1]; [t] \"का फलन\"; [n] children/*[2] (pause:short)"],["Action","function-prefix-arc-simple","[p] (pause:short); [t] \"चाप\"; [n] children/*[1]/children/*[1]; [n] children/*[2] (pause:short)"],["Action","function-prefix-arc-simple-fenced","[p] (pause:short); [t] \"चाप\"; [n] children/*[1]/children/*[1] (pause:short); [n] children/*[2] (pause:short)"],["Action","function-prefix-arc","[p] (pause:short); [t] \"चाप\"; [n] children/*[1]/children/*[1]; [t] \"का फलन\"; [n] children/*[2] (pause:short)"],["Action","function-inverse","[n] children/*[1]; [t] \"प्रतिलोम\""],["Action","superscript-prefix-function","[n] children/*[1]; [n] children/*[2] (grammar:ordinal); [t] \"घातांक\""],["Action","superscript","[n] children/*[1]; [t] \"का घात\" (pause:short); [n] children/*[2] (pause:short); [t] \"घातांक समाप्त\" (pause:short)"],["Action","superscript-simple-exponent","[n] children/*[1]; [t] \"का घात\"; [n] children/*[2]; [t] \"घातांक\" (pause:short)"],["Action","superscript-simple-exponent-end","[n] children/*[1]; [t] \"का घात\"; [n] children/*[2]; [t] \"घातांक\""],["Action","superscript-ordinal","[n] children/*[1]; [t] \"का घात\"; [n] children/*[2] (grammar:ordinal); [t] \"घातांक\" (pause:short)"],["Action","superscript-non-ordinal","[n] children/*[1]; [t] \"का घात\"; [n] children/*[2]; [t] \"घातांक\" (pause:short)"],["Action","superscript-simple-function","[n] children/*[1]; [n] children/*[2] (grammar:ordinal); [t] \"घातांक\" (pause:short)"],["Action","superscript-simple-function-none","[n] . (grammar:functions_none)"],["Action","superscript-ordinal-number","[n] children/*[1]; [t] \"का घात\"; [n] children/*[2] (pause:short, grammar:ordinal)"],["Action","superscript-ordinal-negative","[n] children/*[1]; [t] \"का घात\"; [n] children/*[2] (pause:short)"],["Action","superscript-ordinal-default","[n] children/*[1]; [t] \"का घात\" (pause:short); [n] children/*[2] (pause:short); [t] \"घातांक समाप्त\" (pause:short)"],["Action","superscript-ordinal-power-number","[n] children/*[1]; [t] \"का घात\"; [n] children/*[2] (grammar:ordinal); [t] \"घातांक\" (pause:short)"],["Action","superscript-ordinal-power-negative","[n] children/*[1]; [t] \"का घात\"; [n] children/*[2]; [t] \"घातांक\" (pause:short)"],["Action","superscript-ordinal-power-identifier","[n] children/*[1]; [t] \"का घात\"; [n] children/*[2] (grammar:ordinal); [t] \"घातांक\" (pause:short)"],["Action","superscript-ordinal-power-default","[n] children/*[1]; [t] \"का घात\" (pause:short); [n] children/*[2] (pause:short); [t] \"घातांक समाप्त\" (pause:short)"],["Action","superscript-power","[n] children/*[1]; [t] \"का घात\"; [n] children/*[2] (pause:short, grammar:afterPower)"],["Action","superscript-power-default","[n] children/*[1]; [t] \"का घात\" (pause:short); [n] children/*[2] (pause:short); [t] \"घातांक समाप्त\" (pause:short)"],["Action","exponent","[n] text() (join:\"-\"); [t] \"वां\""],["Action","exponent-number","[t] CSFordinalExponent"],["Action","exponent-ordinal","[t] CSFwordOrdinal"],["Action","exponent-ordinal-zero","[t] \"शून्य\""],["Action","exponent-ordinalpower","[t] CSFwordOrdinal"],["Action","exponent-ordinalpower-zero","[t] \"शून्य\""],["Action","square","[n] children/*[1]; [t] \"वर्गित\""],["Action","cube","[n] children/*[1]; [t] \"घनित\""],["Action","fences-points","[t] \"निर्देशांक वाला बिंदु\"; [n] children/*[1]"],["Action","fences-interval","[n] children/*[1]/children/*[1]; [t] \"से\"; [n] children/*[1]/children/*[3]; [t] \"तक का अन्तराल\" (pause:short); [n] . (grammar:interval)"],["Action","interval-open","[n] children/*[1]/children/*[1]; [t] \"अथवा\"; [n] children/*[1]/children/*[3]; [t] \"असम्मिलित\""],["Action","interval-closed-open","[n] children/*[1]/children/*[1]; [t] \"सम्मिलित\" (pause:short); [t] \"परन्तु\"; [n] children/*[1]/children/*[3]; [t] \"असम्मिलित\""],["Action","interval-open-closed","[n] children/*[1]/children/*[1]; [t] \"असम्मिलित\" (pause:short); [t] \"परन्तु\"; [n] children/*[1]/children/*[3]; [t] \"सम्मिलित\""],["Action","interval-closed","[n] children/*[1]/children/*[1]; [t] \"और\"; [n] children/*[1]/children/*[3]; [t] \"सम्मिलित\""],["Action","interval-open-inf-r","[n] children/*[1]/children/*[1]; [t] \"असम्मिलित\""],["Action","interval-open-inf-l","[n] children/*[1]/children/*[3]; [t] \"असम्मिलित\""],["Action","interval-closed-open-inf","[n] children/*[1]/children/*[1]; [t] \"सम्मिलित\""],["Action","interval-open-closed-inf","[n] children/*[1]/children/*[3]; [t] \"सम्मिलित\""],["Action","set-empty","[t] \"रिक्त समुच्चय\""],["Action","set-extended","[n] children/*[1]/children/*[1]; [t] \"का समुच्चय\"; [t] \"जिस में\"; [n] children/*[1]/children/*[3]"],["Action","set-collection","[t] \"समुच्चय\"; [n] children/*[1]"],["Action","subscript","[p] (pause:short); [n] children/*[1]; [t] \"पाद\"; [n] children/*[2] (pause:short)"],["Action","logarithm-base","[n] children/*[1]; [t] \"आधार\"; [n] children/*[2]"],["Action","subscript-index","[n] children/*[1]; [t] \"पाद\"; [n] children/*[2]"],["Action","fraction","[p] (pause:short); [t] \"भिन्न जिसका अंश\"; [n] children/*[1] (pause:short); [t] \"और हर\"; [n] children/*[2] (pause:short)"],["Action","fraction-none","[p] (pause:short); [t] \"भिन्न जिसका अंश\"; [n] children/*[1] (pause:short); [t] \"और हर\"; [n] children/*[2] (pause:short)"],["Action","simple-fraction","[p] (pause:short); [n] children/*[1]; [t] \"के हर में\"; [n] children/*[2] (pause:short)"],["Action","simple-vulgar-fraction","[p] (pause:short); [n] children/*[1]; [t] \"के हर में\"; [n] children/*[2] (pause:short)"],["Action","simple-text-fraction","[p] (pause:short); [n] children/*[1]; [t] \"के हर में\"; [n] children/*[2] (pause:short)"],["Action","vulgar-fraction","[t] CSFvulgarFraction"],["Action","fraction-over","[p] (pause:short); [n] children/*[1]; [t] \"के हर में\"; [n] children/*[2] (pause:short)"],["Action","fraction-overendfrac","[p] (pause:short); [n] children/*[1]; [t] \"के हर में\"; [n] children/*[2] (pause:short); [t] \"भिन्न समाप्त\" (pause:short)"],["Action","fraction-fracover","[p] (pause:short); [t] \"भिन्न\"; [n] children/*[1]; [t] \"के हर में\"; [n] children/*[2] (pause:short)"],["Action","fraction-per","[p] (pause:short); [n] children/*[1]; [t] \"प्रति\"; [n] children/*[2] (pause:short)"],["Action","fraction-generalendfrac","[p] (pause:short); [t] \"भिन्न जिसका अंश\"; [n] children/*[1] (pause:short); [t] \"और हर\"; [n] children/*[2] (pause:short); [t] \"भिन्न समाप्त\" (pause:short)"],["Action","fraction-general","[p] (pause:short); [t] \"भिन्न जिसका अंश\"; [n] children/*[1] (pause:short); [t] \"और हर\"; [n] children/*[2] (pause:short)"],["Action","simple-vulgar-fraction-ordinal","[t] CSFvulgarFraction"],["Action","fraction-endfrac","[p] (pause:short); [n] . (grammar:endfrac); [t] \"भिन्न समाप्त\" (pause:short)"],["Action","vulgar-fraction-endfrac","[p] (pause:short); [n] children/*[1]; [t] \"के हर में\"; [n] children/*[2] (pause:short)"],["Action","simple-vulgar-fraction-endfrac","[t] CSFvulgarFraction"],["Action","sqrt","[n] children/*[1] (grammar:!EndRoot); [t] \"का वर्गमूल\" (pause:short)"],["Action","sqrt-nested","[p] (pause:\"short\"); [n] children/*[1] (grammar:!EndRoot); [t] \"का वर्गमूल\" (pause:short)"],["Action","negative-sqrt","[n] children/*[1]/children/*[1] (grammar:!EndRoot); [t] \"का ऋणात्मक वर्गमूल\" (pause:short)"],["Action","negative-sqrt-default","[p] (pause:\"short\"); [n] children/*[1]/children/*[1] (grammar:!EndRoot); [t] \"का ऋणात्मक वर्गमूल\" (pause:short)"],["Action","sqrt-plus-minus","[n] children/*[1] (grammar:!EndRoot); [t] \"का धनात्मक वर्गमूल\" (pause:short)"],["Action","sqrt-nested-plus-minus","[n] children/*[1] (grammar:!EndRoot); [t] \"का धनात्मक वर्गमूल\" (pause:short)"],["Action","sqrt-plus-minus-posnegsqrootend","[n] children/*[1] (grammar:!EndRoot); [t] \"का धनात्मक वर्गमूल\" (pause:short)"],["Action","sqrt-nested-plus-minus-posnegsqrootend","[n] children/*[1] (grammar:!EndRoot); [t] \"का धनात्मक वर्गमूल\" (pause:short)"],["Action","sqrt-endroot","[n] . (grammar:EndRoot); [t] \"मूल समाप्त\" (pause:short)"],["Action","negative-sqrt-endroot","[n] . (grammar:EndRoot); [t] \"मूल समाप्त\" (pause:short)"],["Action","sqrt-posnegsqrootend","[n] . (grammar:EndRoot); [t] \"मूल समाप्त\" (pause:short)"],["Action","negative-sqrt-posnegsqrootend","[n] . (grammar:EndRoot); [t] \"मूल समाप्त\" (pause:short)"],["Action","cubic","[n] children/*[2] (grammar:!EndRoot); [t] \"का घनमूल\" (pause:short)"],["Action","cubic-nested","[n] children/*[2] (grammar:!EndRoot); [t] \"का घनमूल\" (pause:short)"],["Action","root","[n] children/*[2] (grammar:!EndRoot); [n] children/*[1] (grammar:ordinal); [t] \"का मूल\" (pause:short)"],["Action","root-nested","[n] children/*[2] (grammar:!EndRoot); [n] children/*[1] (grammar:ordinal); [t] \"का मूल\" (pause:short)"],["Action","root-endroot","[n] . (grammar:EndRoot); [t] \"मूल समाप्त\" (pause:short)"],["Action","root-posnegsqrootend","[n] . (grammar:EndRoot); [t] \"मूल समाप्त\" (pause:short)"],["Action","negative","[t] \"ऋण\"; [n] children/*[1]"],["Action","positive","[t] \"धनात्मक\"; [n] children/*[1]"],["Action","angle-measure","[n] content/*[1]; [n] children/*[2] (grammar:angle); [t] \"का मापन\""],["Action","set-prefix-operators","[n] . (grammar:!prefix); [t] \"का फलन\""],["Action","division","[n] children/*[1]; [t] \"द्वारा विभाजित\"; [n] children/*[2]"],["Action","operators-after-power","[m] children/* (rate:\"0.5\")"],["Action","natural-numbers","[t] \"प्राकृत संख्याएं\""],["Action","integers","[t] \"पूर्णांक\""],["Action","rational-numbers","[t] \"परिमेय संख्याएं\""],["Action","real-numbers","[t] \"वास्तविक संख्याएं\""],["Action","complex-numbers","[t] \"सम्मिश्र संख्याएं\""],["Action","natural-numbers-with-zero","[t] \"शून्य सहित प्राकृत संख्याएं\""],["Action","positive-integers","[t] \"धनात्मक पूर्णांक\""],["Action","negative-integers","[t] \"ऋणात्मक पूर्णांक\""],["Action","positive-rational-numbers","[t] \"धनात्मक परिमेय संख्याएं\""],["Action","negative-rational-numbers","[t] \"ऋणात्मक परिमेय संख्याएं\""],["Action","fences-neutral","[p] (pause:short); [n] children/*[1]; [t] \"का निरपेक्ष मान\" (pause:short)"],["Action","fences-neutral-absend","[p] (pause:short); [t] \"निरपेक्ष मान प्रारम्भ\"; [n] children/*[1] (pause:short); [t] \"निरपेक्ष मान समाप्त\" (pause:short)"],["Action","fences-neutral-cardinality","[p] (pause:short); [n] children/*[1]; [t] \"की गणनीयता\" (pause:short)"],["Action","fences-neutral-determinant","[p] (pause:short); [n] children/*[1]; [t] \"का सारणिक\" (pause:short)"],["Action","fences-metric","[p] (pause:short); [t] \"की दूरी फलन\" (span:.); [n] children/*[1] (pause:short)"],["Action","fences-metric-absend","[p] (pause:short); [t] \"की दूरी फलन\" (span:content/*[1]); [n] children/*[1] (pause:short); [t] \"दूरी फलन समाप्त\" (span:content/*[1], pause:short)"],["Action","matrix","[t] count(children/*); [t] \"बाय\"; [t] count(children/*[1]/children/*); [t] \"आव्यूह\" (pause:long); [m] children/* (ctxtFunc:CTFnodeCounter, context:\"पंक्ति-:\", pause:long)"],["Action","matrix-simple","[t] count(children/*); [t] \"बाय\"; [t] count(children/*[1]/children/*); [t] \"आव्यूह\" (pause:long); [m] children/* (ctxtFunc:CTFnodeCounter, context:\"पंक्ति-:\", pause:long, grammar:simpleDet)"],["Action","matrix-trivial","[t] \"१ बाय १ आव्यूह में भर्ती\"; [n] children/*[1] (pause:long)"],["Action","determinant","[t] count(children/*); [t] \"बाय\"; [t] count(children/*[1]/children/*); [t] \"आव्यूह\"; [t] \"का सारणिक\" (pause:long); [m] children/* (ctxtFunc:CTFnodeCounter, context:\"पंक्ति-:\", pause:long, grammar:simpleDet)"],["Action","determinant-simple","[t] count(children/*); [t] \"बाय\"; [t] count(children/*[1]/children/*); [t] \"आव्यूह\"; [t] \"का सारणिक\" (pause:long); [m] children/* (ctxtFunc:CTFnodeCounter, context:\"पंक्ति-:\", pause:long)"],["Action","matrix-vector","[t] count(children/*); [t] \"बाय\"; [t] count(children/*[1]/children/*); [t] \"स्तंभ आव्यूह\" (pause:long); [m] children/* (ctxtFunc:CTFnodeCounter, context:\"पंक्ति-:\", pause:long, grammar:simpleDet)"],["Action","matrix-vector-simple","[t] count(children/*); [t] \"बाय\"; [t] count(children/*[1]/children/*); [t] \"स्तंभ आव्यूह\" (pause:long); [m] children/* (sepFunc:CTFpauseSeparator, separator:\"short\", pause:long, grammar:simpleDet)"],["Action","matrix-vector-simple-silentcolnum","[t] count(children/*); [t] \"बाय\"; [t] count(children/*[1]/children/*); [t] \"स्तंभ आव्यूह\" (pause:long); [m] children/* (sepFunc:CTFpauseSeparator, separator:\"short\", pause:long, grammar:simpleDet)"],["Action","matrix-row-vector","[t] count(children/*); [t] \"बाय\"; [t] count(children/*[1]/children/*); [t] \"पंक्ति आव्यूह\" (pause:long); [m] children/*[1]/children/* (ctxtFunc:CTFnodeCounter, context:\"स्तंभ-:\", pause:long, grammar:simpleDet)"],["Action","matrix-row-vector-simple","[t] count(children/*); [t] \"बाय\"; [t] count(children/*[1]/children/*); [t] \"पंक्ति आव्यूह\" (pause:long); [m] children/*[1]/children/* (sepFunc:CTFpauseSeparator, separator:\"short\", pause:long, grammar:simpleDet)"],["Action","matrix-row-vector-simple-silentcolnum","[t] count(children/*); [t] \"बाय\"; [t] count(children/*[1]/children/*); [t] \"पंक्ति आव्यूह\" (pause:long); [m] children/*[1]/children/* (sepFunc:CTFpauseSeparator, separator:\"short\", pause:long, grammar:simpleDet)"],["Action","matrix-row","[m] children/* (ctxtFunc:CTFnodeCounter, context:\"स्तंभ-,- \", sepFunc:CTFpauseSeparator, separator:\"medium\", pause:long)"],["Action","matrix-end-matrix","[n] . (grammar:EndMatrix); [t] \"आव्यूह समाप्त\""],["Action","matrix-end-vector","[n] . (grammar:EndMatrix); [t] \"आव्यूह समाप्त\""],["Action","matrix-end-determinant","[n] . (grammar:EndMatrix); [t] \"सारणिक समाप्त\""],["Action","vector","[t] count(children/*); [t] \"बाय\"; [t] count(children/*[1]/children/*); [t] \"स्तंभ सदिश\" (pause:long); [m] children/* (ctxtFunc:CTFnodeCounter, context:\"पंक्ति-:\", pause:long, grammar:simpleDet)"],["Action","vector-simple","[t] count(children/*); [t] \"बाय\"; [t] count(children/*[1]/children/*); [t] \"स्तंभ सदिश\" (pause:long); [m] children/* (sepFunc:CTFpauseSeparator, separator:\"short\", pause:long, grammar:simpleDet)"],["Action","row-vector","[t] count(children/*); [t] \"बाय\"; [t] count(children/*[1]/children/*); [t] \"पंक्ति सदिश\" (pause:long); [m] children/*[1]/children/* (ctxtFunc:CTFnodeCounter, context:\"स्तंभ-:\", pause:long, grammar:simpleDet)"],["Action","row-vector-simple","[t] count(children/*); [t] \"बाय\"; [t] count(children/*[1]/children/*); [t] \"पंक्ति सदिश\" (pause:long); [m] children/*[1]/children/* (sepFunc:CTFpauseSeparator, separator:\"short\", pause:long, grammar:simpleDet)"],["Action","vector-end-matrix","[n] . (grammar:EndMatrix); [t] \"आव्यूह समाप्त\""],["Action","vector-end-vector","[n] . (grammar:EndMatrix); [t] \"सदिश समाप्त\""],["Action","vector-end-vector-endvector","[n] . (grammar:EndMatrix); [t] \"सदिश समाप्त\""],["Action","vector-end-determinant","[n] . (grammar:EndMatrix); [t] \"सारणिक समाप्त\""],["Action","binomial","[n] children/*[1]/children/*[1]; [t] \"चयन करें\"; [n] children/*[2]/children/*[1]"],["Action","lines-summary","[p] (pause:short); [t] count(children/*); [t] \"रेखाएं\"; [n] . (grammar:layoutSummary)"],["Action","cases-summary","[p] (pause:short); [t] count(children/*); [t] \"फलन उपशर्त\"; [n] . (grammar:layoutSummary)"],["Action","lines","[p] (pause:short); [m] children/* (ctxtFunc:CTFnodeCounter, context:\"रेखा-:\", sepFunc:CTFpauseSeparator, separator:\"long\", pause:long)"],["Action","blank-cell","[t] \"रिक्त\""],["Action","blank-line","[t] \"रिक्त\""],["Action","blank-cell-empty","[t] \"रिक्त\""],["Action","blank-line-empty","[t] \"रिक्त\""],["Action","cases","[p] (pause:short); [m] children/* (ctxtFunc:CTFnodeCounter, context:\"फलन उपशर्त-:\", sepFunc:CTFpauseSeparator, separator:\"long\", pause:long)"],["Action","lines-cases-summary","[p] (pause:short); [t] count(children/*); [t] \"फलन उपशर्त\"; [n] . (grammar:layoutSummary)"],["Action","lines-cases","[p] (pause:short); [m] children/* (ctxtFunc:CTFnodeCounter, context:\"फलन उपशर्त-:\", sepFunc:CTFpauseSeparator, separator:\"long\", pause:long)"],["Action","lines-equations-summary","[p] (pause:short); [t] count(children/*); [t] \"समीकरण\"; [n] . (grammar:layoutSummary)"],["Action","lines-equations","[p] (pause:short); [m] children/* (ctxtFunc:CTFnodeCounter, context:\"समीकरण-:\", sepFunc:CTFpauseSeparator, separator:\"long\", pause:long)"],["Action","lines-steps-summary","[p] (pause:short); [t] count(children/*); [t] \"चरण\"; [n] . (grammar:layoutSummary)"],["Action","lines-steps","[p] (pause:short); [m] children/* (ctxtFunc:CTFnodeCounter, context:\"चरण-:\", sepFunc:CTFpauseSeparator, separator:\"long\", pause:long)"],["Action","lines-rows-summary","[p] (pause:short); [t] count(children/*); [t] \"पंक्तियाँ\"; [n] . (grammar:layoutSummary)"],["Action","lines-rows","[p] (pause:short); [m] children/* (ctxtFunc:CTFnodeCounter, context:\"पंक्ति-:\", sepFunc:CTFpauseSeparator, separator:\"long\", pause:long)"],["Action","lines-constraints-summary","[p] (pause:short); [t] count(children/*); [t] \"व्यवरोध\"; [n] . (grammar:layoutSummary)"],["Action","lines-constraints","[p] (pause:short); [m] children/* (ctxtFunc:CTFnodeCounter, context:\"व्यवरोध-:\", sepFunc:CTFpauseSeparator, separator:\"long\", pause:long)"],["Action","bigop","[n] children/*[1]; [t] \"का फलन\"; [n] children/*[2] (pause:short)"],["Action","limboth","[n] children/*[1]; [t] \"से\"; [n] children/*[2]; [t] \"से\"; [n] children/*[3]"],["Action","limlower","[n] children/*[1]; [t] \"के हर में\"; [n] children/*[2] (pause:short)"],["Action","limupper","[n] children/*[1]; [t] \"के निचे\"; [n] children/*[2] (pause:short)"],["Action","integral","[n] children/*[1]; [t] \"का फलन\"; [n] children/*[2] (pause:short); [n] children/*[3] (pause:short)"],["Action","integral-novar","[n] children/*[1]; [t] \"का फलन\"; [n] children/*[2] (pause:short)"],["Action","overscript","[n] children/*[1]; [t] \"के निचे\"; [n] children/*[2] (pause:short)"],["Action","overscript-limits","[n] children/*[1]; [t] \"से\"; [n] children/*[2]"],["Action","underscript","[n] children/*[1]; [t] \"के हर में\"; [n] children/*[2] (pause:short)"],["Action","underscript-limits","[n] children/*[1]; [t] \"से\"; [n] children/*[2]"],["Action","mixed-number","[n] children/*[1]; [t] \"पूर्णांक\"; [n] children/*[2]"],["Action","number-with-chars","[t] \"संख्या\"; [m] CQFspaceoutNumber (grammar:protected)"],["Action","decimal-period","[t] \"पुनरावर्तित दशमलव\"; [n] children/*[1] (grammar:spaceout); [t] \"दशमलव के बाद पुनरावर्तित अंक\"; [n] children/*[3]/children/*[1] (grammar:spaceout)"],["Action","decimal-period-float","[t] \"पुनरावर्तित दशमलव\"; [n] children/*[1] (grammar:spaceout); [t] \"के बाद पुनरावर्तित अंक\"; [n] children/*[2]/children/*[1] (grammar:spaceout)"],["Action","decimal-period-singular","[t] \"पुनरावर्तित दशमलव\"; [n] children/*[1] (grammar:spaceout); [t] \"दशमलव के बाद पुनरावर्तित अंक\"; [n] children/*[3]/children/*[1] (grammar:spaceout)"],["Action","decimal-period-singular-float","[t] \"पुनरावर्तित दशमलव\"; [n] children/*[1] (grammar:spaceout); [t] \"के बाद पुनरावर्तित अंक\"; [n] children/*[2]/children/*[1] (grammar:spaceout)"],["Action","decimal-point","[t] \"दशमलव\""],["Action","line-segment","[t] \"रेखाखंड\"; [n] children/*[1]/children/*[1]; [n] children/*[1]/children/*[2] (pause:short)"],["Action","conjugate","[n] children/*[1]; [t] \"का सम्मिश्र संयुग्मी\""],["Action","defined-by","[t] \"की व्याख्या की जाती है\" (pause:short)"],["Action","adorned-sign","[n] children/*[1]; [t] \"चिन्ह और\"; [n] children/*[2]; [t] \"इस पर\""],["Action","factorial","[t] \"क्रमगुणित\""],["Action","left-super","[t] \"वाम उर्ध्व\"; [n] text()"],["Action","left-super-list","[t] \"वाम उर्ध्व\"; [m] children/*"],["Action","left-sub","[t] \"वाम पाद\"; [n] text()"],["Action","left-sub-list","[t] \"वाम पाद\"; [m] children/*"],["Action","right-super","[t] \"दक्षिण उर्ध्व\"; [n] text()"],["Action","right-super-list","[t] \"दक्षिण उर्ध्व\"; [m] children/*"],["Action","right-sub","[t] \"दक्षिण पाद\"; [n] text()"],["Action","right-sub-list","[t] \"दक्षिण पाद\"; [m] children/*"],["Action","choose","[n] children/*[2] (grammar:combinatorics); [t] \"चयन करें\"; [n] children/*[4] (grammar:combinatorics)"],["Action","permute","[n] children/*[2] (grammar:combinatorics); [t] \"क्रमचय\"; [n] children/*[4] (grammar:combinatorics)"],["Action","unit-square","[t] \"वर्ग\"; [n] children/*[1]"],["Action","unit-cubic","[t] \"घन\"; [n] children/*[1]"],["Action","unit-reciprocal","[t] \"व्युत्क्रम\"; [n] children/*[1]"],["Action","unit-reciprocal-singular","[t] \"प्रति\"; [n] children/*[1] (grammar:singular)"],["Action","unit-divide","[n] children/*[1]; [t] \"प्रति\"; [n] children/*[2] (grammar:singular)"]]}
+,
+"hi/rules/mathspeak_hindi.min": 
+{"domain":"mathspeak","locale":"hi","modality":"speech","inherits":"base","rules":[["Rule","protected","default","[t] text()","self::number[contains(@grammar, \"protected\")]"]]}
+,
+"hi/rules/mathspeak_hindi_actions.min": 
+{"domain":"mathspeak","locale":"hi","modality":"speech","kind":"actions","rules":[["Action","collapsed","[t] \"निपातित\"; [n] . (engine:modality=summary, grammar:collapsed)"],["Action","blank-cell-empty","[t] \"रिक्त\""],["Action","blank-line-empty","[t] \"रिक्त\""],["Action","font","[t] @font (grammar:localFont); [n] . (grammar:ignoreFont=@font)"],["Action","mixed-number","[n] children/*[1]; [t] \"पूर्णांक\"; [n] children/*[2]"],["Action","number-with-chars","[t] \"संख्या\"; [m] CQFspaceoutNumber (grammar:protected)"],["Action","number-with-chars-brief","[t] \"संख्या\"; [m] CQFspaceoutNumber (grammar:protected)"],["Action","number-as-upper-word","[t] \"ऊपर का शब्द\"; [m] CQFspaceoutNumber"],["Action","number-baseline","[t] \"आधार रेखा\"; [n] . (grammar:baseline)"],["Action","number-baseline-brief","[t] \"आधार\"; [n] . (grammar:baseline)"],["Action","number-baseline-font","[t] \"आधार रेखा\"; [t] @font (grammar:localFont); [n] . (grammar:ignoreFont=@font)"],["Action","number-baseline-font-brief","[t] \"आधार\"; [t] @font (grammar:localFont); [n] . (grammar:ignoreFont=@font)"],["Action","negative-number","[t] \"ऋण\"; [n] children/*[1]"],["Action","negative","[t] \"बाकी\"; [n] children/*[1]"],["Action","division","[n] children/*[1]; [t] \"divided by\"; [n] children/*[2]"],["Action","subtraction","[m] children/* (separator:\"बाकी\")"],["Action","fences-neutral","[t] \"निरपेक्ष मान आरंभ\"; [n] children/*[1]; [t] \"निरपेक्ष मान समाप्त\""],["Action","fences-neutral-sbrief","[t] \"निरपेक्ष मान\"; [n] children/*[1]; [t] \"निरपेक्ष मान समाप्त\""],["Action","fences-metric","[t] \"दूरी फलन आरंभ\"; [n] children/*[1]; [t] \"दूरी फलन समाप्त\""],["Action","fences-metric-sbrief","[t] \"दूरी फलन\"; [n] children/*[1]; [t] \"दूरी फलन समाप्त\""],["Action","empty-set","[t] \"रिक्त समुच्चय\""],["Action","fences-set","[t] \"समुच्चय आरंभ\"; [n] children/*[1]; [t] \"समुच्चय समाप्त\""],["Action","fences-set-sbrief","[t] \"समुच्चय\"; [n] children/*[1]; [t] \"समुच्चय समाप्त\""],["Action","factorial","[t] \"क्रमगुणित\""],["Action","minus","[t] \"बाकी\""],["Action","continued-fraction-outer","[t] \"भिन्न चालू\"; [n] children/*[1]; [t] \"ऊपर\"; [n] children/*[2]"],["Action","continued-fraction-outer-brief","[t] \"भिन्न चालू\"; [n] children/*[1]; [t] \"ऊपर\"; [n] children/*[2]"],["Action","continued-fraction-inner","[t] \"आरंभ भिन्न\"; [n] children/*[1]; [t] \"ऊपर\"; [n] children/*[2]"],["Action","continued-fraction-inner-brief","[t] \"आरंभ भिन्न\"; [n] children/*[1]; [t] \"ऊपर\"; [n] children/*[2]"],["Action","continued-fraction-inner-sbrief","[t] \"भिन्न\"; [n] children/*[1]; [t] \"ऊपर\"; [n] children/*[2]"],["Action","integral","[n] children/*[1]; [t] \"पादांक\"; [n] children/*[2]; [t] \"उर्ध्वान्क\"; [n] children/*[3]; [t] \"आधार रेखा\""],["Action","integral-brief","[n] children/*[1]; [t] \"अधो\"; [n] children/*[2]; [t] \"उर्ध्व\"; [n] children/*[3]; [t] \"आधार\""],["Action","square","[n] children/*[1]; [t] \"वर्ग\""],["Action","cube","[n] children/*[1]; [t] \"घन\""],["Action","prime","[n] children/*[1]; [n] children/*[2]"],["Action","counted-prime","[t] count(children/*) (grammar:numbers2alpha); [t] \"प्राइम\""],["Action","counted-prime-multichar","[t] string-length(text()) (grammar:numbers2alpha); [t] \"प्राइम\""],["Action","overscore","[t] \"ऊपर संशोधित\"; [n] children/*[1]; [t] \"के साथ\"; [n] children/*[2]"],["Action","overscore-brief","[t] \"ऊपर संशोधित\"; [n] children/*[1]; [t] \"के साथ\"; [n] children/*[2]"],["Action","double-overscore","[t] \"ऊपर के ऊपर संशोधित\"; [n] children/*[1]; [t] \"के साथ\"; [n] children/*[2]"],["Action","double-overscore-brief","[t] \"ऊपर के ऊपर संशोधित\"; [n] children/*[1]; [t] \"के साथ\"; [n] children/*[2]"],["Action","underscore","[t] \"निचे संशोधित\"; [n] children/*[1]; [t] \"के साथ\"; [n] children/*[2]"],["Action","underscore-brief","[t] \"निचे संशोधित\"; [n] children/*[1]; [t] \"के साथ\"; [n] children/*[2]"],["Action","double-underscore","[t] \"निचे के निचे संशोधित\"; [n] children/*[1]; [t] \"के साथ\"; [n] children/*[2]"],["Action","double-underscore-brief","[t] \"निचे के निचे संशोधित\"; [n] children/*[1]; [t] \"के साथ\"; [n] children/*[2]"],["Action","overbar","[n] children/*[1]; [t] \"उर्ध्व दंड\""],["Action","underbar","[n] children/*[1]; [t] \"अधो दंड\""],["Action","overtilde","[n] children/*[1]; [t] \"उर्ध्व अंतर-चिह्न\""],["Action","undertilde","[n] children/*[1]; [t] \"अधो अंतर-चिह्न\""],["Action","matrix","[t] \"आरंभ\"; [t] count(children/*); [t] \"बाय\"; [t] count(children/*[1]/children/*); [t] \"आव्यूह\"; [m] children/* (ctxtFunc:CTFordinalCounter, context:\"पंक्ति \", grammar:gender=\"f\"); [t] \"आव्यूह समाप्त\""],["Action","matrix-sbrief","[t] count(children/*); [t] \"बाय\"; [t] count(children/*[1]/children/*); [t] \"आव्यूह\"; [m] children/* (ctxtFunc:CTFordinalCounter, context:\"पंक्ति \", grammar:gender=\"f\"); [t] \"आव्यूह समाप्त\""],["Action","matrix-row","[m] children/* (ctxtFunc:CTFordinalCounter, context:\"स्तंभ\", pause:200)"],["Action","row-with-label","[t] \"लेबल के साथ\"; [n] content/*[1]; [t] \"लेबल समाप्त\" (pause:200); [m] children/* (ctxtFunc:CTFordinalCounter, context:\"स्तंभ\")"],["Action","row-with-label-brief","[t] \"लेबल\"; [n] content/*[1]; [m] children/* (ctxtFunc:CTFordinalCounter, context:\"स्तंभ\")"],["Action","row-with-text-label","[t] \"लेबल\"; [t] CSFRemoveParens; [m] children/* (ctxtFunc:CTFordinalCounter, context:\"स्तंभ\")"],["Action","empty-row","[t] \"रिक्त\""],["Action","empty-cell","[t] \"रिक्त\" (pause:300)"],["Action","determinant","[t] \"आरंभ\"; [t] count(children/*); [t] \"बाय\"; [t] count(children/*[1]/children/*); [t] \"सारणिक\"; [m] children/* (ctxtFunc:CTFordinalCounter, context:\"पंक्ति \", grammar:gender=\"f\"); [t] \"सारणिक समाप्त\""],["Action","determinant-sbrief","[t] count(children/*); [t] \"बाय\"; [t] count(children/*[1]/children/*); [t] \"सारणिक\"; [m] children/* (ctxtFunc:CTFordinalCounter, context:\"पंक्ति \", grammar:gender=\"f\"); [t] \"सारणिक समाप्त\""],["Action","determinant-simple","[t] \"आरंभ\"; [t] count(children/*); [t] \"बाय\"; [t] count(children/*[1]/children/*); [t] \"सारणिक\"; [m] children/* (ctxtFunc:CTFordinalCounter, context:\"पंक्ति\", grammar:simpleDet); [t] \"सारणिक समाप्त\""],["Action","determinant-simple-sbrief","[t] count(children/*); [t] \"बाय\"; [t] count(children/*[1]/children/*); [t] \"सारणिक\"; [m] children/* (ctxtFunc:CTFordinalCounter, context:\"पंक्ति\", grammar:simpleDet); [t] \"सारणिक समाप्त\""],["Action","layout","[t] \"खाका आरंभ\"; [m] children/* (ctxtFunc:CTFordinalCounter, context:\"पंक्ति \", grammar:gender=\"f\"); [t] \"खाका समाप्त\""],["Action","layout-sbrief","[t] \"खाका\"; [m] children/* (ctxtFunc:CTFordinalCounter, context:\"पंक्ति \", grammar:gender=\"f\"); [t] \"खाका समाप्त\""],["Action","binomial","[t] \"द्विपद अथवा आव्यूह आरंभ\"; [n] children/*[1]/children/*[1]; [t] \"चयन करें\"; [n] children/*[2]/children/*[1]; [t] \"द्विपद अथवा आव्यूह समाप्त\""],["Action","binomial-sbrief","[t] \"द्विपद अथवा आव्यूह\"; [n] children/*[1]/children/*[1]; [t] \"चयन करें\"; [n] children/*[2]/children/*[1]; [t] \"द्विपद अथवा आव्यूह समाप्त\""],["Action","cases","[t] \"खाका आरंभ\"; [t] \"विस्तारित\"; [n] content/*[1]; [m] children/* (ctxtFunc:CTFordinalCounter, context:\"पंक्ति \", grammar:gender=\"f\"); [t] \"खाका समाप्त\""],["Action","cases-sbrief","[t] \"खाका\"; [t] \"विस्तारित\"; [n] content/*[1]; [m] children/* (ctxtFunc:CTFordinalCounter, context:\"पंक्ति \", grammar:gender=\"f\"); [t] \"खाका समाप्त\""],["Action","line-with-label","[t] \"लेबल के साथ\"; [n] content/*[1]; [t] \"लेबल समाप्त\" (pause:200); [m] children/*"],["Action","line-with-label-brief","[t] \"लेबल\"; [n] content/*[1] (pause:200); [m] children/*"],["Action","line-with-text-label","[t] \"लेबल\"; [t] CSFRemoveParens; [m] children/*"],["Action","empty-line","[t] \"रिक्त\""],["Action","empty-line-with-label","[t] \"लेबल के साथ\"; [n] content/*[1]; [t] \"लेबल समाप्त\" (pause:200); [t] \"रिक्त\""],["Action","empty-line-with-label-brief","[t] \"लेबल\"; [n] content/*[1] (pause:200); [t] \"रिक्त\""],["Action","enclose","[t] \"घेरे का आरंभ\"; [t] @role (grammar:localEnclose); [n] children/*[1]; [t] \"घेरा समाप्त\""],["Action","leftbar","[t] \"ऊर्ध्वाधर दंड\"; [n] children/*[1]"],["Action","rightbar","[n] children/*[1]; [t] \"ऊर्ध्वाधर दंड\""],["Action","crossout","[t] \"मिटाया हुआ\"; [n] children/*[1]; [t] \"मिटाया हुआ समाप्त\""],["Action","cancel","[t] \"मिटाया हुआ\"; [n] children/*[1]/children/*[1]; [t] \"के साथ\"; [n] children/*[2]; [t] \"मिटाया हुआ समाप्त\""],["Action","cancel-reverse","[t] \"मिटाया हुआ\"; [n] children/*[2]/children/*[1]; [t] \"के साथ\"; [n] children/*[1]; [t] \"मिटाया हुआ समाप्त\""],["Action","multi-inference","[t] \"अनुमान नियम\"; [m] content/*; [t] \"निष्कर्ष के साथ\"; [n] children/*[1]; [t] \"और\"; [t] count(children/*[2]/children/*); [t] \"पूर्वपक्ष\""],["Action","inference","[t] \"अनुमान नियम\"; [m] content/*; [t] \"निष्कर्ष के साथ\"; [n] children/*[1]; [t] \"और\"; [t] count(children/*[2]/children/*); [t] \"पूर्वपक्ष\""],["Action","premise","[m] children/* (ctxtFunc:CTFordinalCounter, context:\"premise \")"],["Action","conclusion","[n] children/*[1]"],["Action","label","[t] \"सूचक पर्चा\"; [n] children/*[1]"],["Action","axiom","[t] \"अभिगृहीत\"; [m] children/*[1]"],["Action","empty-axiom","[t] \"रिक्त अभिगृहीत\""]]}
+,
+"hi/rules/prefix_hindi.min": 
+{"modality":"prefix","domain":"default","locale":"hi","inherits":"base","rules":[]}
+,
+"hi/rules/prefix_hindi_actions.min": 
+{"modality":"prefix","domain":"default","locale":"hi","kind":"actions","rules":[["Action","numerator","[t] \"अंश\" (pause:200)"],["Action","denominator","[t] \"हर\" (pause:200)"],["Action","base","[t] \"आधार\" (pause:200)"],["Action","exponent","[t] \"घातांक\" (pause:200)"],["Action","subscript","[t] \"पादांक\" (pause:200)"],["Action","overscript","[t] \"उर्ध्वान्क\" (pause:200)"],["Action","underscript","[t] \"निम्नांक\" (pause:200)"],["Action","radicand","[t] \"करणीगत राशि\" (pause:200)"],["Action","index","[t] \"मूलांक\" (pause:200)"],["Action","leftsub","[t] \"वाम पादांक\" (pause:200)"],["Action","leftsub-counted","[t] CSFordinalPosition; [t] \"वाम पादांक\" (pause:200)"],["Action","leftsuper","[t] \"वाम उर्ध्वान्क\" (pause:200)"],["Action","leftsuper-counted","[t] CSFordinalPosition; [t] \"वाम उर्ध्वान्क\" (pause:200)"],["Action","rightsub","[t] \"दक्षिण पादांक\" (pause:200)"],["Action","rightsub-counted","[t] CSFordinalPosition; [t] \"दक्षिण पादांक\" (pause:200)"],["Action","rightsuper","[t] \"दक्षिण उर्ध्वान्क\" (pause:200)"],["Action","rightsuper-counted","[t] CSFordinalPosition; [t] \"दक्षिण उर्ध्वान्क\" (pause:200)"],["Action","choice","[t] \"विकल्प तादात\" (pause:200)"],["Action","select","[t] \"चयन तादात\" (pause:200)"],["Action","row","[t] CSFordinalPosition (grammar:gender=\"f\"); [t] \"पंक्ति\" (pause:200)"],["Action","cell","[n] ../..; [t] CSFordinalPosition; [t] \"स्तंभ\" (pause:200)"],["Action","cell-simple","[t] CSFordinalPosition; [t] \"स्तंभ\" (pause:200)"]]}
+,
+"hi/rules/summary_hindi.min": 
+{"locale":"hi","modality":"summary","inherits":"base","rules":[]}
+,
+"hi/rules/summary_hindi_actions.min": 
+{"locale":"hi","modality":"summary","kind":"actions","rules":[["Action","abstr-identifier-long","[t] \"दीर्घ तत्समक\""],["Action","abstr-identifier","[t] \"तत्समक\""],["Action","abstr-number-long","[t] \"दीर्घ संख्या\""],["Action","abstr-number","[t] \"संख्या\""],["Action","abstr-mixed-number-long","[t] \"दीर्घ मिश्र संख्या\""],["Action","abstr-mixed-number","[t] \"मिश्र संख्या\""],["Action","abstr-text","[t] \"पाठ\""],["Action","abstr-function","[t] \"फलन व्यंजक\""],["Action","abstr-function-brief","[t] \"फलन\""],["Action","abstr-lim","[t] \"फलन सीमा\""],["Action","abstr-lim-brief","[t] \"सीमा\""],["Action","abstr-fraction","[t] \"भिन्न\""],["Action","abstr-fraction-brief","[t] \"अपूर्णांश\""],["Action","abstr-continued-fraction","[t] \"सतत भिन्न\""],["Action","abstr-continued-fraction-brief","[t] \"सतत अपूर्णांश\""],["Action","abstr-sqrt","[t] \"वर्ग मूल\""],["Action","abstr-sqrt-nested","[t] \"नीड़ित वर्ग मूल\""],["Action","abstr-root-end","[t] \"वां मूल\"; [n] children/*[1] (engine:modality=speech); [t] \"मूलांक समाप्त\""],["Action","abstr-root","[n] children/*[1] (engine:modality=speech); [t] \"वां मूल\""],["Action","abstr-root-brief","[t] \"मूल\""],["Action","abstr-root-nested-end","[t] \"नीड़ित मूल्यांक\"; [n] children/*[1] (engine:modality=speech); [t] \"मूलांक समाप्त\""],["Action","abstr-root-nested","[t] \"नीड़ित मूल्यांक\"; [n] children/*[1] (engine:modality=speech)"],["Action","abstr-root-nested-brief","[t] \"नीड़ित मूल\""],["Action","abstr-superscript","[t] \"घातांक\""],["Action","abstr-subscript","[t] \"पादांक\""],["Action","abstr-subsup","[t] \"पादांक वाला घातांक\""],["Action","abstr-infixop","[t] count(./children/*); [t] \"अवयव\"; [t] \"के साथ\"; [t] @role (grammar:localRole)"],["Action","abstr-infixop-var","[t] \"चल तादात के अवयवों के साथ\"; [t] @role (grammar:localRole)"],["Action","abstr-infixop-brief","[t] @role (grammar:localRole)"],["Action","abstr-addition","[t] count(./children/*); [t] \"पद\"; [t] \"के साथ योग\""],["Action","abstr-addition-brief","[t] \"योग\""],["Action","abstr-addition-var","[t] \"चल तादात के पदों का योग\""],["Action","abstr-multiplication","[t] count(./children/*); [t] \"गुणनखंड\"; [t] \"के साथ गुणनफल\""],["Action","abstr-multiplication-brief","[t] \"गुणनफल\""],["Action","abstr-multiplication-var","[t] \"चल तादात के गुणनखंडो का गुणनफल\""],["Action","abstr-vector","[t] count(./children/*); [t] \"विमीय सदिश\""],["Action","abstr-vector-brief","[t] \"सदिश\""],["Action","abstr-vector-var","[t] \"एन विमीय सदिश\""],["Action","abstr-binomial","[t] \"द्विपद\""],["Action","abstr-determinant","[t] count(./children/*); [t] \"विमीय सारणिक\""],["Action","abstr-determinant-brief","[t] \"सारणिक\""],["Action","abstr-determinant-var","[t] \"एन विमीय सारणिक\""],["Action","abstr-squarematrix","[t] count(./children/*); [t] \"विमीय वर्ग आव्यूह\""],["Action","abstr-squarematrix-brief","[t] \"वर्ग आव्यूह\""],["Action","abstr-rowvector","[t] count(./children/row/children/*); [t] \"विमीय पंक्ति सदिश\""],["Action","abstr-rowvector-brief","[t] \"पंक्ति सदिश\""],["Action","abstr-rowvector-var","[t] \"एन विमीय पंक्ति सदिश\""],["Action","abstr-matrix","[t] count(children/*); [t] \"गुणा\"; [t] count(children/*[1]/children/*); [t] \"आव्यूह\""],["Action","abstr-matrix-brief","[t] \"आव्यूह\""],["Action","abstr-matrix-var","[t] \"एन गुणा एम आव्यूह\""],["Action","abstr-cases","[t] count(children/*); [t] \"फलन उपशर्त\"; [t] \"के साथ\"; [t] \"फलन उपशर्त कथन\""],["Action","abstr-cases-brief","[t] \"फलन उपशर्त कथन\""],["Action","abstr-cases-var","[t] \"चल तादात की फलन उपशर्तों वाला फलन उपशर्त कथन\""],["Action","abstr-punctuated","[t] count(children/*) - count(content/*); [t] \"लम्बाई की\"; [n] content/*[1]; [t] \"विभाजित सूचि\""],["Action","abstr-punctuated-brief","[n] content/*[1]; [t] \"विभाजित सूचि\""],["Action","abstr-punctuated-var","[t] \"चल लम्बाई की\"; [n] content/*[1]; [t] \"विभाजित सूचि\""],["Action","abstr-bigop","[n] content/*[1]"],["Action","abstr-integral","[t] \"समाकल\""],["Action","abstr-relation","[t] @role (grammar:localRole)"],["Action","abstr-relation-seq","[t] count(./children/*); [t] \"अवयव\"; [t] \"के साथ\"; [t] @role (grammar:localRole); [t] \"अनुक्रम\""],["Action","abstr-relation-seq-brief","[t] @role (grammar:localRole); [t] \"अनुक्रम\""],["Action","abstr-relation-var","[t] \"चल तादात के अवयवों के साथ\"; [t] @role (grammar:localRole); [t] \"अनुक्रम\""],["Action","abstr-multirel","[t] count(./children/*); [t] \"अवयव\"; [t] \"के साथ\"; [t] \"संबंधानुक्रम\""],["Action","abstr-multirel-brief","[t] \"संबंधानुक्रम\""],["Action","abstr-multirel-var","[t] \"चल तादात के अवयवों का संबंधानुक्रम\""],["Action","abstr-table","[t] count(children/*); [t] \"पक्तियां और\"; [t] count(children/*[1]/children/*); [t] \"स्तंभ\"; [t] \"के साथ सारणी\""],["Action","abstr-line","[t] @role (grammar:localRole); [t] \"में\""],["Action","abstr-row","[t] \"में\"; [t] @role (grammar:localRole); [t] count(preceding-sibling::..); [t] \"के साथ\"; [t] count(children/*); [t] \"स्तंभ\""],["Action","abstr-cell","[t] @role (grammar:localRole); [t] \"में\""]]}
+}
+
diff --git a/devel/_static/mathjax/sre/mathmaps/it.json b/devel/_static/mathjax/sre/mathmaps/it.json
new file mode 100644
index 0000000000..7db3653175
--- /dev/null
+++ b/devel/_static/mathjax/sre/mathmaps/it.json
@@ -0,0 +1,146 @@
+{
+"it/messages/alphabets.min": 
+{"kind":"alphabets","locale":"it","messages":{"latinSmall":["a","b","c","d","e","f","g","h","i","j","k","l","m","n","o","p","q","r","s","t","u","v","w","x","y","z"],"latinCap":["A","B","C","D","E","F","G","H","I","J","K","L","M","N","O","P","Q","R","S","T","U","V","W","X","Y","Z"],"greekSmall":["nabla","alfa","beta","gamma","delta","epsilon","zeta greca","eta","theta","iota","kappa greca","lambda","mu","nu","xi","omicron","pi greco","rho","sigma final","sigma","tau","upsilon","phi","chi","psi","omega","derivata parziale","epsilon","theta","kappa","phi","rho","pi"],"greekCap":["Alfa","Beta","Gamma","Delta","Epsilon","Zeta greca","Eta","Theta","Iota","Kappa greca","Lambda","Mu","Nu","Xi","Omicron","Pi greca","Rho","Theta","Sigma","Tau","Upsilon","Phi","Chi","Psi","Omega"],"capPrefix":{"default":"maiuscola"},"smallPrefix":{"default":""},"digitPrefix":{"default":""}}}
+,
+"it/messages/messages.min": 
+{"kind":"messages","locale":"it","messages":{"MS":{"START":"inizio","FRAC_V":"frazione","FRAC_B":"frazione","FRAC_S":"frazione","END":"fine","FRAC_OVER":"fratto","ONCE":"una volta","TWICE":"due volte","NEST_FRAC":"annidamento","ENDFRAC":"fine frazione","SUPER":"super","SUB":"sub","SUP":"sup","SUPERSCRIPT":"apice","SUBSCRIPT":"pedice","BASELINE":"linea di base","BASE":"base","NESTED":"annidato","NEST_ROOT":"annidamento","STARTROOT":"inizio radice","ENDROOT":"fine radice","ROOTINDEX":"indice radice","ROOT":"radice","INDEX":"indice Radice","UNDER":"sotto","UNDERSCRIPT":"sottoscritto","OVER":"sopra","OVERSCRIPT":"soprascritto","ENDSCRIPTS":"fine script"},"MSroots":{},"font":{"bold":"grassetto","bold-fraktur":"grassetto gotico","bold-italic":"grassetto italico","bold-script":"grassetto script","caligraphic":"calligrafico","caligraphic-bold":"grassetto calligrafico","double-struck":"grassetto da lavagna","double-struck-italic":"grassetto da lavagna italico","fraktur":"gotico","fullwidth":"fullwidth","italic":"italico","monospace":"monospazio","normal":"normale","oldstyle":"vecchio stile","oldstyle-bold":"vecchio stile grassetto","script":"script","sans-serif":"senza grazie","sans-serif-italic":"senza grazie italico","sans-serif-bold":"senza grazie grassetto","sans-serif-bold-italic":"senza grazie grassetto italico","unknown":"sconosciuto"},"embellish":{"super":["apice","prefixCombiner"],"sub":["pedice","prefixCombiner"],"circled":["cerchiato","italianPostfix"],"parenthesized":"tra parentesi","period":"punto","negative-circled":["cerchiato in negativo","italianPostfix"],"double-circled":"doppio cerchiato","circled-sans-serif":"cerchiato senza grazie","negative-circled-sans-serif":"cerchiato in negativo senza grazie","comma":"virgola","squared":"dentro quadrato","negative-squared":"dentro quadrato in negativo"},"role":{"addition":"addizione","multiplication":"moltiplicazione","subtraction":"sottrazione","division":"divisione","equality":"uguaglianza","inequality":"disuguaglianza","element":"elemento","arrow":"freccia","determinant":"determinante","rowvector":"vettore riga","binomial":"binomiale","squarematrix":"matrice quadrata","set empty":"insieme vuoto","set extended":"estensione di insieme","set singleton":"singoletto","set collection":"collezione","label":"eitchetta","multiline":"linee multiple","matrix":"matrice","vector":"vettore","cases":"comando switch","table":"tavola","unknown":"sconosciuto"},"enclose":{"longdiv":"divisione lunga","actuarial":"simbolo attuario","radical":"radice quadrata","box":"riquadro","roundedbox":"riquadro arrotondato","circle":"cerchio","left":"linea verticale sinistra","right":"linea verticale destra","top":"barra sopra","bottom":"barra sotto","updiagonalstrike":"cancellatura","downdiagonalstrike":"cancellatura","verticalstrike":"cancellatura verticale","horizontalstrike":"cancellatura","madruwb":"simbolo fattoriale arabo","updiagonalarrow":"freccia diagonale","phasorangle":"angolo fasore","unknown":"divisione lunga"},"navigate":{"COLLAPSIBLE":"collassabile","EXPANDABLE":"espandibile","LEVEL":"livello"},"regexp":{"TEXT":"a-zA-ZàèìòùéóÀ","NUMBER":"((\\d{1,3})(?=(.| ))((.| )\\d{3})*(\\,\\d+)?)|^\\d*\\,\\d+|^\\d+","DECIMAL_MARK":",","DIGIT_GROUP":"\\.","JOINER_SUBSUPER":" ","JOINER_FRAC":" "},"unitTimes":""}}
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(grammar:!prefix); [t] \"di\""],["Action","division","[n] children/*[1]; [t] \"diviso per\"; [n] children/*[2]"],["Action","operators-after-power","[m] children/* (rate:\"0.5\")"],["Action","natural-numbers","[t] \"i numeri naturali\""],["Action","integers","[t] \"gli interi\""],["Action","rational-numbers","[t] \"i numeri razionali\""],["Action","real-numbers","[t] \"i numeri reali\""],["Action","complex-numbers","[t] \"i numeri complessi\""],["Action","natural-numbers-with-zero","[t] \"i numeri naturali con lo zero\""],["Action","positive-integers","[t] \"gli interi positivi\""],["Action","negative-integers","[t] \"gli interi negativi\""],["Action","positive-rational-numbers","[t] \"i numeri razionali positivi\""],["Action","negative-rational-numbers","[t] \"i numeri razionali negativi\""],["Action","fences-neutral","[p] (pause:short); [t] \"valore assoluto di\"; [n] children/*[1] (pause:short)"],["Action","fences-neutral-absend","[p] (pause:short); [t] \"valore assoluto di\"; [n] children/*[1] (pause:short); [t] \"fine valore assoluto\" (pause:short)"],["Action","fences-neutral-cardinality","[p] (pause:short); [t] \"cardinalità di\"; [n] children/*[1] (pause:short)"],["Action","fences-neutral-determinant","[p] (pause:short); [t] \"determinante della\"; [n] children/*[1] (pause:short)"],["Action","fences-metric","[p] (pause:short); [t] \"metrica di\" (span:.); [n] children/*[1] (pause:short)"],["Action","fences-metric-absend","[p] (pause:short); [t] \"metrica di\" (span:content/*[1]); [n] children/*[1] (pause:short); [t] \"fine metrica\" (span:content/*[1], pause:short)"],["Action","matrix","[t] \"matrice\"; [t] count(children/*); [t] \"per\"; [t] count(children/*[1]/children/*) (pause:long); [m] children/* (ctxtFunc:CTFnodeCounter, context:\"Riga-:\", pause:long)"],["Action","matrix-simple","[t] \"matrice\"; [t] count(children/*); [t] \"per\"; [t] count(children/*[1]/children/*) (pause:long); [m] children/* (ctxtFunc:CTFnodeCounter, context:\"Riga-:\", pause:long, grammar:simpleDet)"],["Action","matrix-trivial","[t] \"matrice 1 per 1 con valore\"; [n] children/*[1] (pause:long)"],["Action","determinant","[t] \"determinante della matrice\"; [t] count(children/*); [t] \"per\"; [t] count(children/*[1]/children/*) (pause:long); [m] children/* (ctxtFunc:CTFnodeCounter, context:\"Riga-:\", pause:long, grammar:simpleDet)"],["Action","determinant-simple","[t] \"determinante della matrice\"; [t] count(children/*); [t] \"per\"; [t] count(children/*[1]/children/*) (pause:long); [m] children/* (ctxtFunc:CTFnodeCounter, context:\"Riga-:\", pause:long)"],["Action","matrix-vector","[t] \"matrice colonna\"; [t] count(children/*); [t] \"per\"; [t] count(children/*[1]/children/*) (pause:long); [m] children/* (ctxtFunc:CTFnodeCounter, context:\"Riga-:\", pause:long, grammar:simpleDet)"],["Action","matrix-vector-simple","[t] \"matrice colonna\"; [t] count(children/*); [t] \"per\"; [t] count(children/*[1]/children/*) (pause:long); [m] children/* (sepFunc:CTFpauseSeparator, separator:\"short\", pause:long, grammar:simpleDet)"],["Action","matrix-vector-simple-silentcolnum","[t] \"matrice colonna\"; [t] count(children/*); [t] \"per\"; [t] count(children/*[1]/children/*) (pause:long); [m] children/* (sepFunc:CTFpauseSeparator, separator:\"short\", pause:long, grammar:simpleDet)"],["Action","matrix-row-vector","[t] \"matrice riga\"; [t] count(children/*); [t] \"per\"; [t] count(children/*[1]/children/*) (pause:long); [m] children/*[1]/children/* (ctxtFunc:CTFnodeCounter, context:\"Colonna-:\", pause:long, grammar:simpleDet)"],["Action","matrix-row-vector-simple","[t] \"matrice riga\"; [t] count(children/*); [t] \"per\"; [t] count(children/*[1]/children/*) (pause:long); [m] children/*[1]/children/* (sepFunc:CTFpauseSeparator, separator:\"short\", pause:long, grammar:simpleDet)"],["Action","matrix-row-vector-simple-silentcolnum","[t] \"matrice riga\"; [t] count(children/*); [t] \"per\"; [t] count(children/*[1]/children/*) (pause:long); [m] children/*[1]/children/* (sepFunc:CTFpauseSeparator, separator:\"short\", pause:long, grammar:simpleDet)"],["Action","matrix-row","[m] children/* (ctxtFunc:CTFnodeCounter, context:\"Colonna-,- \", sepFunc:CTFpauseSeparator, separator:\"medium\", pause:long)"],["Action","matrix-end-matrix","[n] . (grammar:EndMatrix); [t] \"fine matrice\""],["Action","matrix-end-vector","[n] . (grammar:EndMatrix); [t] \"fine matrice\""],["Action","matrix-end-determinant","[n] . (grammar:EndMatrix); [t] \"fine determinante\""],["Action","vector","[t] \"vettore colonna\"; [t] count(children/*); [t] \"per\"; [t] count(children/*[1]/children/*) (pause:long); [m] children/* (ctxtFunc:CTFnodeCounter, context:\"Riga-:\", pause:long, grammar:simpleDet)"],["Action","vector-simple","[t] \"vettore colonna\"; [t] count(children/*); [t] \"per\"; [t] count(children/*[1]/children/*) (pause:long); [m] children/* (sepFunc:CTFpauseSeparator, separator:\"short\", pause:long, grammar:simpleDet)"],["Action","row-vector","[t] \"vettore riga\"; [t] count(children/*); [t] \"per\"; [t] count(children/*[1]/children/*) (pause:long); [m] children/*[1]/children/* (ctxtFunc:CTFnodeCounter, context:\"Colonna-:\", pause:long, grammar:simpleDet)"],["Action","row-vector-simple","[t] \"vettore riga\"; [t] count(children/*); [t] \"per\"; [t] count(children/*[1]/children/*) (pause:long); [m] children/*[1]/children/* (sepFunc:CTFpauseSeparator, separator:\"short\", pause:long, grammar:simpleDet)"],["Action","vector-end-matrix","[n] . (grammar:EndMatrix); [t] \"fine matrice\""],["Action","vector-end-vector","[n] . (grammar:EndMatrix); [t] \"fine vettore\""],["Action","vector-end-vector-endvector","[n] . (grammar:EndMatrix); [t] \"fine vettore\""],["Action","vector-end-determinant","[n] . (grammar:EndMatrix); [t] \"fine determinante\""],["Action","binomial","[t] \"coefficiente binomiale\"; [n] children/*[1]/children/*[1]; [t] \"su\"; [n] children/*[2]/children/*[1]"],["Action","lines-summary","[p] (pause:short); [t] count(children/*); [t] \"linee\"; [n] . (grammar:layoutSummary)"],["Action","cases-summary","[p] (pause:short); [t] count(children/*); [t] \"casi\"; [n] . (grammar:layoutSummary)"],["Action","lines","[p] (pause:short); [m] children/* (ctxtFunc:CTFnodeCounter, context:\"Riga-:\", sepFunc:CTFpauseSeparator, separator:\"long\", pause:long)"],["Action","blank-cell","[t] \"vuoto\""],["Action","blank-line","[t] \"vuota\""],["Action","blank-cell-empty","[t] \"vuota\""],["Action","blank-line-empty","[t] \"vuota\""],["Action","cases","[p] (pause:short); [m] children/* (ctxtFunc:CTFnodeCounter, context:\"Caso-:\", sepFunc:CTFpauseSeparator, separator:\"long\", pause:long)"],["Action","lines-cases-summary","[p] (pause:short); [t] count(children/*); [t] \"caso\"; [n] . (grammar:layoutSummary)"],["Action","lines-cases","[p] (pause:short); [m] children/* (ctxtFunc:CTFnodeCounter, context:\"Caso-:\", sepFunc:CTFpauseSeparator, separator:\"long\", pause:long)"],["Action","lines-equations-summary","[p] (pause:short); [t] count(children/*); [t] \"equazioni\"; [n] . (grammar:layoutSummary)"],["Action","lines-equations","[p] (pause:short); [m] children/* (ctxtFunc:CTFnodeCounter, context:\"Equazioni-:\", sepFunc:CTFpauseSeparator, separator:\"long\", pause:long)"],["Action","lines-steps-summary","[p] (pause:short); [t] count(children/*); [t] \"passi\"; [n] . (grammar:layoutSummary)"],["Action","lines-steps","[p] (pause:short); [m] children/* (ctxtFunc:CTFnodeCounter, context:\"Passo-:\", sepFunc:CTFpauseSeparator, separator:\"long\", pause:long)"],["Action","lines-rows-summary","[p] (pause:short); [t] count(children/*); [t] \"riga\"; [n] . (grammar:layoutSummary)"],["Action","lines-rows","[p] (pause:short); [m] children/* (ctxtFunc:CTFnodeCounter, context:\"Riga-:\", sepFunc:CTFpauseSeparator, separator:\"long\", pause:long)"],["Action","lines-constraints-summary","[p] (pause:short); [t] count(children/*); [t] \"vincoli\"; [n] . (grammar:layoutSummary)"],["Action","lines-constraints","[p] (pause:short); [m] children/* (ctxtFunc:CTFnodeCounter, context:\"Vincoli-:\", sepFunc:CTFpauseSeparator, separator:\"long\", pause:long)"],["Action","bigop","[t] \"il\"; [n] children/*[1]; [t] \"di\"; [n] children/*[2] (pause:short)"],["Action","limboth","[n] children/*[1]; [t] \"da\"; [n] children/*[2]; [t] \"a\"; [n] children/*[3]"],["Action","limlower","[n] children/*[1]; [t] \"sopra\"; [n] children/*[2] (pause:short)"],["Action","limupper","[n] children/*[1]; [t] \"sotto\"; [n] children/*[2] (pause:short)"],["Action","integral","[t] \"il\"; [n] children/*[1]; [t] \"di\"; [n] children/*[2] (pause:short); [n] children/*[3] (pause:short)"],["Action","integral-novar","[t] \"il\"; [n] children/*[1]; [t] \"di\"; [n] children/*[2] (pause:short)"],["Action","overscript","[n] children/*[1]; [t] \"sotto\"; [n] children/*[2] (pause:short)"],["Action","overscript-limits","[n] children/*[1]; [t] \"a\"; [n] children/*[2]"],["Action","underscript","[n] children/*[1]; [t] \"sopra\"; [n] children/*[2] (pause:short)"],["Action","underscript-limits","[n] children/*[1]; [t] \"da\"; [n] children/*[2]"],["Action","mixed-number","[n] children/*[1]; [t] \"e\"; [n] children/*[2]"],["Action","number-with-chars","[t] \"numero\"; [m] CQFspaceoutNumber (grammar:protected)"],["Action","decimal-period","[t] \"decimale periodico\"; [n] children/*[1] (grammar:spaceout); [t] \"virgola\"; [n] children/*[3]/children/*[1] (grammar:spaceout); [t] \"periodico\""],["Action","decimal-period-float","[t] \"decimale periodico\"; [n] children/*[1] (grammar:spaceout); [t] \"seguito da\"; [n] children/*[2]/children/*[1] (grammar:spaceout); [t] \"periodico\""],["Action","decimal-point","[t] \"punto\""],["Action","line-segment","[t] \"linea segmento\"; [n] children/*[1]/children/*[1]; [n] children/*[1]/children/*[2] (pause:short)"],["Action","conjugate","[t] \"coniugato complesso di\"; [n] children/*[1]"],["Action","defined-by","[t] \"è definito essere\" (pause:short)"],["Action","adorned-sign","[t] \"segno\"; [n] children/*[1]; [t] \"con\"; [n] children/*[2]; [t] \"sopra esso\""],["Action","factorial","[t] \"fattoriale\""],["Action","left-super","[t] \"sub sinistro\"; [n] text()"],["Action","left-super-list","[t] \"sub sinistro\"; [m] children/*"],["Action","left-sub","[t] \"sub sotto\"; [n] text()"],["Action","left-sub-list","[t] \"sub sotto\"; [m] children/*"],["Action","right-super","[t] \"sub sopra\"; [n] text()"],["Action","right-super-list","[t] \"sub sopra\"; [m] children/*"],["Action","right-sub","[t] \"sub destro\"; [n] text()"],["Action","right-sub-list","[t] \"sub destro\"; [m] children/*"],["Action","choose","[t] \"combinazione di\"; [n] children/*[2] (grammar:combinatorics); [t] \"per\"; [n] children/*[4] (grammar:combinatorics)"],["Action","permute","[t] \"permutazione di\"; [n] children/*[2] (grammar:combinatorics); [t] \"per\"; [n] children/*[4] (grammar:combinatorics)"],["Action","unit-square","[n] children/*[1]; [t] \"quadrato\""],["Action","unit-cubic","[n] children/*[1]; [t] \"cubico\""],["Action","unit-divide","[n] children/*[1]; [t] \"al\"; [n] children/*[2] (grammar:singular)"]]}
+,
+"it/rules/mathspeak_italian.min": 
+{"domain":"mathspeak","locale":"it","modality":"speech","inherits":"romance","rules":[["Ignore","vulgar-fraction"],["Precondition","squared-masculine","default","self::number[text()=2]","contains(@grammar, \"squared\")"],["Precondition","squared-feminine","default","self::number[text()=2]","contains(@grammar, \"squared\")","parent::*/parent::*[@role=\"simple function\"]"]]}
+,
+"it/rules/mathspeak_italian_actions.min": 
+{"domain":"mathspeak","locale":"it","modality":"speech","kind":"actions","rules":[["Action","collapsed","[n] . (engine:modality=summary, grammar:collapsed)"],["Action","blank-cell-empty","[t] \"vuoto\""],["Action","blank-line-empty","[t] \"vuoto\""],["Action","font","[n] . (grammar:ignoreFont=@font); [t] @font (grammar:localFont)"],["Action","mixed-number","[n] children/*[1]; [t] \"e\"; [n] children/*[2]"],["Action","number-with-chars","[t] \"numero\"; [m] CQFspaceoutNumber (grammar:protected)"],["Action","number-with-chars-brief","[t] \"num\"; [m] CQFspaceoutNumber (grammar:protected)"],["Action","number-as-upper-word","[t] \"parolaMaiuscolo\"; [t] CSFspaceoutText"],["Action","number-baseline","[t] \"linea di base\"; [n] . (grammar:baseline)"],["Action","number-baseline-brief","[t] \"base\"; [n] . (grammar:baseline)"],["Action","number-baseline-font","[t] \"linea di base\"; [n] . (grammar:ignoreFont=@font); [t] @font (grammar:localFont)"],["Action","number-baseline-font-brief","[t] \"base\"; [n] . (grammar:ignoreFont=@font); [t] @font (grammar:localFont)"],["Action","negative-number","[t] \"meno\"; [n] children/*[1]"],["Action","negative","[t] \"meno\"; [n] children/*[1]"],["Action","division","[n] children/*[1]; [t] \"diviso per\"; [n] children/*[2]"],["Action","subtraction","[m] children/* (separator:\"meno\")"],["Action","fences-neutral","[t] \"inizio valore assoluto\"; [n] children/*[1]; [t] \"fine valore assoluto\""],["Action","fences-neutral-sbrief","[t] \"valore assoluto\"; [n] children/*[1]; [t] \"fine valore assoluto\""],["Action","fences-metric","[t] \"inizio metrica\"; [n] children/*[1]; [t] \"fine metrica\""],["Action","fences-metric-sbrief","[t] \"metrica\"; [n] children/*[1]; [t] \"fine metrica\""],["Action","empty-set","[t] \"insieme vuoto\""],["Action","fences-set","[t] \"inizio insieme\"; [n] children/*[1]; [t] \"fine insieme\""],["Action","fences-set-sbrief","[t] \"insieme\"; [n] children/*[1]; [t] \"fine insieme\""],["Action","factorial","[t] \"fattoriale\""],["Action","minus","[t] \"meno\""],["Action","vulgar-fraction","[t] CSFvulgarFraction (grammar:fraction:gender=\"m\")"],["Action","continued-fraction-outer","[t] \"continuo frazione\"; [n] children/*[1]; [t] \"fratto\"; [n] children/*[2]"],["Action","continued-fraction-outer-brief","[t] \"continuo frazione\"; [n] children/*[1]; [t] \"fratto\"; [n] children/*[2]"],["Action","continued-fraction-inner","[t] \"inizio frazione\"; [n] children/*[1]; [t] \"fratto\"; [n] children/*[2]"],["Action","continued-fraction-inner-brief","[t] \"inizio frazione\"; [n] children/*[1]; [t] \"fratto\"; [n] children/*[2]"],["Action","continued-fraction-inner-sbrief","[t] \"frazione\"; [n] children/*[1]; [t] \"fratto\"; [n] children/*[2]"],["Action","integral","[n] children/*[1]; [t] \"pedice\"; [n] children/*[2]; [t] \"apice\"; [n] children/*[3]; [t] \"linea di base\""],["Action","integral-brief","[n] children/*[1]; [t] \"sub\"; [n] children/*[2]; [t] \"sup\"; [n] children/*[3]; [t] \"base\""],["Action","logarithm-base","[n] children/*[1]; [t] \"base\"; [n] children/*[2]"],["Action","squared-masculine","[t] \"quadrato\""],["Action","squared-feminine","[t] \"quadrata\""],["Action","square","[n] children/*[1]; [n] children/*[2] (grammar:squared)"],["Action","cube","[n] children/*[1]; [t] \"cubo\""],["Action","prime","[n] children/*[1]; [n] children/*[2]"],["Action","counted-prime","[t] count(children/*) (grammar:numbers2alpha); [t] \"volte primo\""],["Action","counted-prime-multichar","[t] string-length(text()) (grammar:numbers2alpha); [t] \"volte primo\""],["Action","overscore","[t] \"modificante sopra\"; [n] children/*[1]; [t] \"con\"; [n] children/*[2]"],["Action","overscore-brief","[t] \"mod sopra\"; [n] children/*[1]; [t] \"con\"; [n] children/*[2]"],["Action","double-overscore","[t] \"modificante sopra sopra\"; [n] children/*[1]; [t] \"con\"; [n] children/*[2]"],["Action","double-overscore-brief","[t] \"mod sopra sopra\"; [n] children/*[1]; [t] \"con\"; [n] children/*[2]"],["Action","underscore","[t] \"modificanteSotto\"; [n] children/*[1]; [t] \"con\"; [n] children/*[2]"],["Action","underscore-brief","[t] \"modSotto\"; [n] children/*[1]; [t] \"con\"; [n] children/*[2]"],["Action","double-underscore","[t] \"modificanteSotto sotto\"; [n] children/*[1]; [t] \"con\"; [n] children/*[2]"],["Action","double-underscore-brief","[t] \"modSotto sotto\"; [n] children/*[1]; [t] \"con\"; [n] children/*[2]"],["Action","overbar","[n] children/*[1]; [t] \"con barra sopra\""],["Action","underbar","[n] children/*[1]; [t] \"con barra sotto\""],["Action","overtilde","[n] children/*[1]; [t] \"con tilde sopra\""],["Action","undertilde","[n] children/*[1]; [t] \"con tilde sotto\""],["Action","matrix","[t] \"inizio\"; [t] count(children/*); [t] \"per\"; [t] count(children/*[1]/children/*); [t] \"matrice\"; [m] children/* (ctxtFunc:CTFordinalCounter, context:\"riga \"); [t] \"fine matrice\""],["Action","matrix-sbrief","[t] count(children/*); [t] \"per\"; [t] count(children/*[1]/children/*); [t] \"matrice\"; [m] children/* (ctxtFunc:CTFordinalCounter, context:\"riga \"); [t] \"fine matrice\""],["Action","matrix-row","[m] children/* (ctxtFunc:CTFordinalCounter, context:\"colonna\", pause:200)"],["Action","row-with-label","[t] \"con etichetta\"; [n] content/*[1]; [t] \"fine etichetta\" (pause:200); [m] children/* (ctxtFunc:CTFordinalCounter, context:\"colonna\")"],["Action","row-with-label-brief","[t] \"etichetta\"; [n] content/*[1]; [m] children/* (ctxtFunc:CTFordinalCounter, context:\"colonna\")"],["Action","row-with-text-label","[t] \"etichetta\"; [t] CSFRemoveParens; [m] children/* (ctxtFunc:CTFordinalCounter, context:\"colonna\")"],["Action","empty-row","[t] \"vuoto\""],["Action","empty-cell","[t] \"vuoto\" (pause:300)"],["Action","determinant","[t] \"inizio\"; [t] count(children/*); [t] \"per\"; [t] count(children/*[1]/children/*); [t] \"determinante\"; [m] children/* (ctxtFunc:CTFordinalCounter, context:\"riga \"); [t] \"fine determinante\""],["Action","determinant-sbrief","[t] count(children/*); [t] \"per\"; [t] count(children/*[1]/children/*); [t] \"determinante\"; [m] children/* (ctxtFunc:CTFordinalCounter, context:\"riga \"); [t] \"fine determinante\""],["Action","determinant-simple","[t] \"inizio\"; [t] count(children/*); 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new file mode 100644
index 0000000000..4e0e6e9ffd
--- /dev/null
+++ b/devel/_static/mathjax/sre/mathmaps/nb.json
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(grammar:EndRoot); [t] \"slutt rot\" (pause:short)"],["Action","negative-sqrt-endroot","[n] . (grammar:EndRoot); [t] \"slutt rot\" (pause:short)"],["Action","sqrt-posnegsqrootend","[n] . (grammar:EndRoot); [t] \"slutt rot\" (pause:short)"],["Action","negative-sqrt-posnegsqrootend","[n] . (grammar:EndRoot); [t] \"slutt rot\" (pause:short)"],["Action","cubic","[t] \"kubikkroten av\"; [n] children/*[2] (pause:short, grammar:!EndRoot)"],["Action","cubic-nested","[p] (pause:short); [t] \"kubikkroten av\"; [n] children/*[2] (pause:short, grammar:!EndRoot)"],["Action","root","[n] children/*[1] (grammar:ordinal); [t] \"roten av\"; [n] children/*[2] (pause:short, grammar:!EndRoot)"],["Action","root-nested","[p] (pause:short); [n] children/*[1] (grammar:ordinal); [t] \"roten av\"; [n] children/*[2] (pause:short, grammar:!EndRoot)"],["Action","root-endroot","[n] . (grammar:EndRoot); [t] \"slutt rot\" (pause:short)"],["Action","root-posnegsqrootend","[n] . (grammar:EndRoot); [t] \"slutt rot\" (pause:short)"],["Action","negative","[t] \"minus\" (span:content/*[1]); [n] children/*[1]"],["Action","positive","[t] \"pluss\" (span:content/*[1]); [n] children/*[1]"],["Action","angle-measure","[t] \"vinkelmål\"; [n] content/*[1]; [n] children/*[2] (grammar:angle)"],["Action","set-prefix-operators","[n] . (grammar:!prefix:addArticle); [t] \"av\""],["Action","division","[n] children/*[1]; [t] \"delt på\" (span:content/*[1]); [n] children/*[2]"],["Action","operators-after-power","[m] children/* (rate:\"0.5\")"],["Action","natural-numbers","[t] \"de naturlige tallene\" (span:.)"],["Action","integers","[t] \"heltallene\" (span:.)"],["Action","rational-numbers","[t] \"de rasjonale tallene\" (span:.)"],["Action","real-numbers","[t] \"de reelle tallene\" (span:.)"],["Action","complex-numbers","[t] \"de komplekse tallene\" (span:.)"],["Action","natural-numbers-with-zero","[t] \"de naturlige tallene med null\" (span:.)"],["Action","positive-integers","[t] \"de positive heltallene\" (span:.)"],["Action","negative-integers","[t] \"de negative heltallene\" (span:.)"],["Action","positive-rational-numbers","[t] \"de positive rasjonale tallene\" (span:.)"],["Action","negative-rational-numbers","[t] \"de negative rasjonale tallene\" (span:.)"],["Action","fences-neutral","[p] (pause:short); [t] \"den absolutte verdien av\" (span:.); [n] children/*[1] (pause:short)"],["Action","fences-neutral-absend","[p] (pause:short); [t] \"den absolutte verdien av\" (span:content/*[1]); [n] children/*[1] (pause:short); [t] \"slutt absolutt verdi\" (span:content/*[1], pause:short)"],["Action","fences-neutral-cardinality","[p] (pause:short); [t] \"kardinaliteten til\" (span:.); [n] children/*[1] (pause:short)"],["Action","fences-neutral-determinant","[p] (pause:short); [t] \"determinanten til\" (span:.); [n] children/*[1] (pause:short)"],["Action","fences-metric","[p] (pause:short); [t] \"beregningen av\" (span:.); [n] children/*[1] (pause:short)"],["Action","fences-metric-absend","[p] (pause:short); [t] \"beregningen av\" (span:content/*[1]); [n] children/*[1] (pause:short); [t] \"slutt beregning\" (span:content/*[1], pause:short)"],["Action","matrix","[t] count(children/*); [t] \"ganger\"; [t] count(children/*[1]/children/*); [t] \"matrisen\" (pause:long); [m] children/* (ctxtFunc:CTFnodeCounter, context:\"rad-:\", pause:long)"],["Action","matrix-simple","[t] count(children/*); [t] \"ganger\"; [t] count(children/*[1]/children/*); [t] \"matrisen\" (pause:long); [m] children/* (ctxtFunc:CTFnodeCounter, context:\"rad-:\", pause:long, grammar:simpleDet)"],["Action","matrix-trivial","[t] \"1 ganger 1 matrisen med element\"; [n] children/*[1] (pause:long)"],["Action","determinant","[t] \"determinanten til\"; [t] count(children/*); [t] \"ganger\"; [t] count(children/*[1]/children/*); [t] \"matrisen\" (pause:long); [m] children/* (ctxtFunc:CTFnodeCounter, context:\"rad-:\", pause:long, grammar:simpleDet)"],["Action","determinant-simple","[t] \"determinanten til\"; [t] count(children/*); [t] \"ganger\"; [t] count(children/*[1]/children/*); [t] \"matrisen\" (pause:long); [m] children/* (ctxtFunc:CTFnodeCounter, context:\"rad-:\", pause:long)"],["Action","matrix-vector","[t] count(children/*); [t] \"ganger\"; [t] count(children/*[1]/children/*); [t] \"kolonnematrise\" (pause:long); [m] children/* (ctxtFunc:CTFnodeCounter, context:\"rad-:\", pause:long, grammar:simpleDet)"],["Action","matrix-vector-simple","[t] count(children/*); [t] \"ganger\"; [t] count(children/*[1]/children/*); [t] \"kolonnematrise\" (pause:long); [m] children/* (sepFunc:CTFpauseSeparator, separator:\"short\", pause:long, grammar:simpleDet)"],["Action","matrix-vector-simple-silentcolnum","[t] count(children/*); [t] \"ganger\"; [t] count(children/*[1]/children/*); [t] \"kolonnematrise\" (pause:long); [m] children/* (sepFunc:CTFpauseSeparator, separator:\"short\", pause:long, grammar:simpleDet)"],["Action","matrix-row-vector","[t] count(children/*); [t] \"ganger\"; [t] count(children/*[1]/children/*); [t] \"radmatrise\" (pause:long); [m] children/*[1]/children/* (ctxtFunc:CTFnodeCounter, context:\"kolonne-:\", pause:long, grammar:simpleDet)"],["Action","matrix-row-vector-simple","[t] count(children/*); [t] \"ganger\"; [t] count(children/*[1]/children/*); [t] \"radmatrise\" (pause:long); [m] children/*[1]/children/* (sepFunc:CTFpauseSeparator, separator:\"short\", pause:long, grammar:simpleDet)"],["Action","matrix-row-vector-simple-silentcolnum","[t] count(children/*); [t] \"ganger\"; [t] count(children/*[1]/children/*); [t] \"radmatrise\" (pause:long); [m] children/*[1]/children/* (sepFunc:CTFpauseSeparator, separator:\"short\", pause:long, grammar:simpleDet)"],["Action","matrix-row","[m] children/* (ctxtFunc:CTFnodeCounter, context:\"kolonne-,- \", sepFunc:CTFpauseSeparator, separator:\"medium\", pause:long)"],["Action","matrix-end-matrix","[n] . (grammar:EndMatrix); [t] \"slutt matrise\""],["Action","matrix-end-vector","[n] . (grammar:EndMatrix); [t] \"slutt matrise\""],["Action","matrix-end-determinant","[n] . (grammar:EndMatrix); [t] \"slutt determinant\""],["Action","vector","[t] count(children/*); [t] \"ganger\"; [t] count(children/*[1]/children/*); [t] \"kolonnevektor\" (pause:long); [m] children/* (ctxtFunc:CTFnodeCounter, context:\"rad-:\", pause:long, grammar:simpleDet)"],["Action","vector-simple","[t] count(children/*); [t] \"ganger\"; [t] count(children/*[1]/children/*); [t] \"kolonnevektor\" (pause:long); [m] children/* (sepFunc:CTFpauseSeparator, separator:\"short\", pause:long, grammar:simpleDet)"],["Action","row-vector","[t] count(children/*); [t] \"ganger\"; [t] count(children/*[1]/children/*); [t] \"radvektor\" (pause:long); [m] children/*[1]/children/* (ctxtFunc:CTFnodeCounter, context:\"kolonne-:\", pause:long, grammar:simpleDet)"],["Action","row-vector-simple","[t] count(children/*); [t] \"ganger\"; [t] count(children/*[1]/children/*); [t] \"radvektor\" (pause:long); [m] children/*[1]/children/* (sepFunc:CTFpauseSeparator, separator:\"short\", pause:long, grammar:simpleDet)"],["Action","vector-end-matrix","[n] . 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(grammar:layoutSummary)"],["Action","lines-equations","[p] (pause:short); [m] children/* (ctxtFunc:CTFnodeCounter, context:\"ligning-:\", sepFunc:CTFpauseSeparator, separator:\"long\", pause:long)"],["Action","lines-steps-summary","[p] (pause:short); [t] count(children/*); [t] \"steg\"; [n] . (grammar:layoutSummary)"],["Action","lines-steps","[p] (pause:short); [m] children/* (ctxtFunc:CTFnodeCounter, context:\"steg-:\", sepFunc:CTFpauseSeparator, separator:\"long\", pause:long)"],["Action","lines-rows-summary","[p] (pause:short); [t] count(children/*); [t] \"rader\"; [n] . (grammar:layoutSummary)"],["Action","lines-rows","[p] (pause:short); [m] children/* (ctxtFunc:CTFnodeCounter, context:\"rad-:\", sepFunc:CTFpauseSeparator, separator:\"long\", pause:long)"],["Action","lines-constraints-summary","[p] (pause:short); [t] count(children/*); [t] \"betingelser\"; [n] . (grammar:layoutSummary)"],["Action","lines-constraints","[p] (pause:short); [m] children/* (ctxtFunc:CTFnodeCounter, context:\"betingelse-:\", sepFunc:CTFpauseSeparator, separator:\"long\", pause:long)"],["Action","bigop","[n] children/*[1] (grammar:addArticle); [t] \"over\"; [n] children/*[2] (pause:short)"],["Action","limboth","[n] children/*[1]; [t] \"fra\"; [n] children/*[2]; [t] \"til\"; [n] children/*[3]"],["Action","limlower","[n] children/*[1]; [t] \"over\"; [n] children/*[2] (pause:short)"],["Action","limupper","[n] children/*[1]; [t] \"under\"; [n] children/*[2] (pause:short)"],["Action","integral","[n] children/*[1] (grammar:addArticle); [t] \"over\"; [n] children/*[2] (pause:short); [n] children/*[3] (pause:short)"],["Action","integral-novar","[n] children/*[1] (grammar:addArticle); [t] \"over\"; [n] children/*[2] (pause:short)"],["Action","overscript","[n] children/*[1]; [t] \"under\"; [n] children/*[2] (pause:short)"],["Action","overscript-limits","[n] children/*[1]; [t] \"til\"; [n] children/*[2]"],["Action","underscript","[n] children/*[1]; [t] \"over\"; [n] children/*[2] (pause:short)"],["Action","underscript-limits","[n] children/*[1]; [t] \"fra\"; [n] children/*[2]"],["Action","mixed-number","[n] children/*[1]; [t] \"og\"; [n] children/*[2]"],["Action","number-with-chars","[t] \"tall\"; [m] CQFspaceoutNumber (grammar:protected)"],["Action","decimal-period","[t] \"desimalbrøk\"; [n] children/*[1] (grammar:spaceout); [t] \"komma etterfulgt av gjentatte siffer\"; [n] children/*[3]/children/*[1] (grammar:spaceout)"],["Action","decimal-period-float","[t] \"desimalbrøk\"; [n] children/*[1] (grammar:spaceout); [t] \"etterfulgt av gjentatte siffer\"; [n] children/*[2]/children/*[1] (grammar:spaceout)"],["Action","decimal-period-singular","[t] \"desimalbrøk\"; [n] children/*[1] (grammar:spaceout); [t] \"komma etterfulgt av gjentatt siffer\"; [n] children/*[3]/children/*[1] (grammar:spaceout)"],["Action","decimal-period-singular-float","[t] \"desimalbrøk\"; [n] children/*[1] (grammar:spaceout); [t] \"etterfulgt av gjentatt siffer\"; [n] children/*[2]/children/*[1] (grammar:spaceout)"],["Action","decimal-point","[t] \"komma\""],["Action","line-segment","[t] \"linjestykket\"; [n] children/*[1]/children/*[1]; [n] children/*[1]/children/*[2] (pause:short)"],["Action","conjugate","[t] \"komplekskonjugatet til\"; [n] children/*[1]"],["Action","defined-by","[t] \"er definert som\" (pause:short)"],["Action","adorned-sign","[n] children/*[1]; [t] \"tegn med\"; [n] children/*[2]; [t] \"over\""],["Action","factorial","[t] \"fakultet\""],["Action","left-super","[t] \"venstre øvre indeks\"; [n] text()"],["Action","left-super-list","[t] \"venstre øvre indeks\"; [m] children/*"],["Action","left-sub","[t] \"venstre nedre indeks\"; [n] text()"],["Action","left-sub-list","[t] \"venstre nedre indeks\"; [m] children/*"],["Action","right-super","[t] \"høyre øvre indeks\"; [n] text()"],["Action","right-super-list","[t] \"høyre øvre indeks\"; [m] children/*"],["Action","right-sub","[t] \"høyre nedre indeks\"; [n] text()"],["Action","right-sub-list","[t] \"høyre nedre indeks\"; [m] children/*"],["Action","choose","[n] children/*[2] (grammar:combinatorics); [t] \"over\"; [n] children/*[4] (grammar:combinatorics)"],["Action","permute","[n] children/*[2] (grammar:combinatorics); [t] \"permutasjon av\"; [n] children/*[4] (grammar:combinatorics)"],["Action","unit-square","[t] \"kvadrat\"; [n] children/*[1]"],["Action","unit-cubic","[t] \"kubikk\"; [n] children/*[1]"],["Action","unit-reciprocal","[t] \"invers\"; [n] children/*[1]"],["Action","unit-reciprocal-singular","[t] \"per\"; [n] children/*[1] (grammar:singular)"],["Action","unit-divide","[n] children/*[1]; [t] \"per\"; [n] children/*[2] (grammar:singular)"],["Action","enclose","[t] \"omsluttet av\"; [t] @role (grammar:localEnclose); [n] children/*[1]"],["Action","enclose-end","[t] \"omsluttet av\"; [t] @role (grammar:localEnclose); [n] children/*[1]; [t] \"slutt omsluttet\""],["Action","overbar","[n] children/*[1]; [t] \"horisontal strek\""],["Action","underbar","[n] children/*[1]; [t] \"over horisontal strek\""],["Action","leftbar","[t] \"vertikal strek\"; [n] children/*[1]"],["Action","rightbar","[n] children/*[1]; [t] \"vertikal strek\""],["Action","crossout","[t] \"utkrysset\"; [n] children/*[1]"],["Action","crossout-end","[t] \"utkrysset\"; [n] children/*[1]; [t] \"slutt utkrysset\""],["Action","cancel-over","[n] children/*[1]/children/*[1]; [t] \"utkrysset med\"; [n] children/*[2]"],["Action","cancel-under","[n] children/*[2]/children/*[1]; [t] \"utkrysset med\"; [n] children/*[1]"],["Action","cancel-over-end","[n] children/*[1]/children/*[1]; [t] \"utkrysset med\"; [n] children/*[2]; [t] \"slutt utkrysset\""],["Action","cancel-under-end","[n] children/*[2]/children/*[1]; [t] \"utkrysset med\"; [n] children/*[1]; [t] \"slutt utkrysset\""]]}
+,
+"nb/rules/mathspeak_bokmal.min": 
+{"locale":"nb","domain":"mathspeak","modality":"speech","inherits":"base","rules":[]}
+,
+"nb/rules/mathspeak_bokmal_actions.min": 
+{"locale":"nb","domain":"mathspeak","modality":"speech","kind":"actions","rules":[["Action","collapsed","[t] \"kollapset\"; [n] . (engine:modality=summary, grammar:collapsed)"],["Action","blank-cell-empty","[t] \"tom\""],["Action","blank-line-empty","[t] \"tom\""],["Action","font","[t] @font (grammar:localFont); [n] . (grammar:ignoreFont=@font)"],["Action","mixed-number","[n] children/*[1]; [t] \"og\"; [n] children/*[2]"],["Action","number-with-chars","[t] \"nummer\"; [m] CQFspaceoutNumber (grammar:protected)"],["Action","number-with-chars-brief","[t] \"num\"; [m] CQFspaceoutNumber (grammar:protected)"],["Action","number-as-upper-word","[t] \"ord stor\"; [t] CSFspaceoutText"],["Action","number-baseline","[t] \"grunnlinje\"; [n] . (grammar:baseline)"],["Action","number-baseline-brief","[t] \"grunn\"; [n] . (grammar:baseline)"],["Action","number-baseline-font","[t] \"grunnlinje\"; [t] @font; [n] . (grammar:ignoreFont=@font)"],["Action","number-baseline-font-brief","[t] \"basis\"; [t] @font; [n] . (grammar:ignoreFont=@font)"],["Action","negative-number","[t] \"negativ\"; [n] children/*[1]"],["Action","negative","[t] \"minus\"; [n] children/*[1]"],["Action","division","[n] children/*[1]; [t] \"delt på\"; [n] children/*[2]"],["Action","subtraction","[m] children/* (separator:\"minus\")"],["Action","fences-neutral","[t] \"StartAbsoluttVerdi\"; [n] children/*[1]; [t] \"StoppAbsoluttVerdi\""],["Action","fences-neutral-sbrief","[t] \"AbsoluttVerdi\"; [n] children/*[1]; [t] \"StoppAbsoluttVerdi\""],["Action","fences-metric","[t] \"StartMetrisk\"; [n] children/*[1]; [t] \"StoppMetrisk\""],["Action","fences-metric-sbrief","[t] \"Metrisk\"; [n] children/*[1]; [t] \"StoppMetrisk\""],["Action","empty-set","[t] \"tomt sett\""],["Action","fences-set","[t] \"StartSett\"; [n] children/*[1]; [t] \"StoppSett\""],["Action","fences-set-sbrief","[t] \"Sett\"; [n] children/*[1]; [t] \"StoppSett\""],["Action","factorial","[t] \"faktorisk\""],["Action","minus","[t] \"minus\""],["Action","continued-fraction-outer","[t] \"KontinuerligBrøk\"; [n] children/*[1]; [t] \"Over\"; [n] children/*[2]"],["Action","continued-fraction-outer-brief","[t] \"KontinuerligBrøk\"; [n] children/*[1]; [t] \"Over\"; [n] children/*[2]"],["Action","continued-fraction-inner","[t] \"StartBrøk\"; [n] children/*[1]; [t] \"Over\"; [n] children/*[2]"],["Action","continued-fraction-inner-brief","[t] \"StartBrøk\"; [n] children/*[1]; [t] \"Over\"; [n] children/*[2]"],["Action","continued-fraction-inner-sbrief","[t] \"Brøk\"; [n] children/*[1]; [t] \"Over\"; [n] children/*[2]"],["Action","integral","[n] children/*[1]; [t] \"subskript\"; [n] children/*[2]; [t] \"superskript\"; [n] children/*[3]; [t] \"grunnlinje\""],["Action","integral-brief","[n] children/*[1]; [t] \"sub\"; [n] children/*[2]; [t] \"super\"; [n] children/*[3]; [t] \"grunn\""],["Action","square","[n] children/*[1]; [t] \"i andre potens\""],["Action","cube","[n] children/*[1]; [t] \"i tredje potens\""],["Action","prime","[n] children/*[1]; [n] children/*[2]"],["Action","counted-prime","[t] count(children/*) (grammar:numbers2alpha); [t] \"prim\""],["Action","counted-prime-multichar","[t] string-length(text()) (grammar:numbers2alpha); [t] \"prim\""],["Action","overscore","[t] \"ModifiserOver\"; [n] children/*[1]; [t] \"Med\"; [n] children/*[2]"],["Action","overscore-brief","[t] \"ModOver\"; [n] children/*[1]; [t] \"Med\"; [n] children/*[2]"],["Action","double-overscore","[t] \"ModifiserOver Over\"; [n] children/*[1]; [t] \"Med\"; [n] children/*[2]"],["Action","double-overscore-brief","[t] \"ModOver Over\"; [n] children/*[1]; [t] \"Med\"; [n] children/*[2]"],["Action","underscore","[t] \"ModifiserUnder\"; [n] children/*[1]; [t] \"Med\"; [n] children/*[2]"],["Action","underscore-brief","[t] \"ModUnder\"; [n] children/*[1]; [t] \"Med\"; [n] children/*[2]"],["Action","double-underscore","[t] \"ModifiserUnder Under\"; [n] children/*[1]; [t] \"Med\"; [n] children/*[2]"],["Action","double-underscore-brief","[t] \"ModUnder Under\"; [n] children/*[1]; [t] \"Med\"; [n] children/*[2]"],["Action","overbar","[n] children/*[1]; [t] \"overstrek\""],["Action","underbar","[n] children/*[1]; [t] \"understrek\""],["Action","overtilde","[n] children/*[1]; [t] \"overtilde\""],["Action","undertilde","[n] children/*[1]; [t] \"undertilde\""],["Action","matrix","[t] \"Start\"; [t] count(children/*); [t] \"Med\"; [t] count(children/*[1]/children/*); [t] \"Matrise\"; [m] children/* (ctxtFunc:CTFordinalCounter, context:\"Rad \"); [t] \"StoppMatrise\""],["Action","matrix-sbrief","[t] count(children/*); [t] \"Med\"; [t] count(children/*[1]/children/*); [t] \"Matrise\"; [m] children/* (ctxtFunc:CTFordinalCounter, context:\"Rad \"); [t] \"StoppMatrise\""],["Action","matrix-row","[m] children/* (ctxtFunc:CTFordinalCounter, context:\"Kolonne\", pause:200)"],["Action","row-with-label","[t] \"med etikett\"; [n] content/*[1]; [t] \"StoppEtikett\" (pause:200); [m] children/* (ctxtFunc:CTFordinalCounter, context:\"Kolonne\")"],["Action","row-with-label-brief","[t] \"Etikett\"; [n] content/*[1]; [m] children/* (ctxtFunc:CTFordinalCounter, context:\"Kolonne\")"],["Action","row-with-text-label","[t] \"Etikett\"; [t] CSFRemoveParens; [m] children/* (ctxtFunc:CTFordinalCounter, context:\"Kolonne\")"],["Action","empty-row","[t] \"Tom\""],["Action","empty-cell","[t] \"Tom\" (pause:300)"],["Action","determinant","[t] \"Start\"; [t] count(children/*); [t] \"Med\"; [t] count(children/*[1]/children/*); [t] \"Determinant\"; [m] children/* (ctxtFunc:CTFordinalCounter, context:\"Rad \"); [t] \"StoppDeterminant\""],["Action","determinant-sbrief","[t] count(children/*); [t] \"Med\"; [t] count(children/*[1]/children/*); [t] \"Determinant\"; [m] children/* (ctxtFunc:CTFordinalCounter, context:\"Rad \"); [t] \"StoppDeterminant\""],["Action","determinant-simple","[t] \"Start\"; [t] count(children/*); [t] \"Med\"; [t] count(children/*[1]/children/*); [t] \"Determinant\"; [m] children/* (ctxtFunc:CTFordinalCounter, context:\"Rad\", grammar:simpleDet); [t] \"StoppDeterminant\""],["Action","determinant-simple-sbrief","[t] count(children/*); [t] \"Med\"; [t] count(children/*[1]/children/*); [t] \"Determinant\"; [m] children/* (ctxtFunc:CTFordinalCounter, context:\"Rad\", grammar:simpleDet); [t] \"StoppDeterminant\""],["Action","layout","[t] \"StartOppsett\"; [m] children/* (ctxtFunc:CTFordinalCounter, context:\"Rad \"); [t] \"StoppOppsett\""],["Action","layout-sbrief","[t] \"Oppsett\"; [m] children/* (ctxtFunc:CTFordinalCounter, context:\"Rad \"); [t] \"StoppOppsett\""],["Action","binomial","[t] \"StartBinomialEllerMatrise\"; [n] children/*[1]/children/*[1]; [t] \"Velg\"; [n] children/*[2]/children/*[1]; [t] \"StoppBinomialEllerMatrise\""],["Action","binomial-sbrief","[t] \"BinomialEllerMatrise\"; [n] children/*[1]/children/*[1]; [t] \"Velg\"; [n] children/*[2]/children/*[1]; [t] \"StoppBinomialEllerMatrise\""],["Action","cases","[t] \"StartOppsett\"; [t] \"Forstørret\"; [n] content/*[1]; [m] children/* (ctxtFunc:CTFordinalCounter, context:\"Row \"); [t] \"StoppOppsett\""],["Action","cases-sbrief","[t] \"Oppsett\"; [t] \"Forstørret\"; [n] content/*[1]; [m] children/* (ctxtFunc:CTFordinalCounter, context:\"Rad \"); [t] \"StoppOppsett\""],["Action","line-with-label","[t] \"med Etikett\"; [n] content/*[1]; [t] \"StoppEtikett\" (pause:200); [m] children/*"],["Action","line-with-label-brief","[t] \"Etikett\"; [n] content/*[1] (pause:200); [m] children/*"],["Action","line-with-text-label","[t] \"Etikett\"; [t] CSFRemoveParens; [m] children/*"],["Action","empty-line","[t] \"Tom\""],["Action","empty-line-with-label","[t] \"med Etikett\"; [n] content/*[1]; [t] \"StoppEtikett\" (pause:200); [t] \"Tom\""],["Action","empty-line-with-label-brief","[t] \"Etikett\"; [n] content/*[1] (pause:200); [t] \"Tom\""],["Action","enclose","[t] \"StartOmslutting\"; [t] @role (grammar:localEnclose); [n] children/*[1]; [t] \"StoppOmslutting\""],["Action","leftbar","[t] \"vertikal strek\"; [n] children/*[1]"],["Action","rightbar","[n] children/*[1]; [t] \"vertikal strek\""],["Action","crossout","[t] \"Utkryssing\"; [n] children/*[1]; [t] \"StoppUtkryssing\""],["Action","cancel","[t] \"Utkryssing\"; [n] children/*[1]/children/*[1]; [t] \"Med\"; [n] children/*[2]; [t] \"StoppUtkryssing\""],["Action","cancel-reverse","[t] \"Utkryssing\"; [n] children/*[2]/children/*[1]; [t] \"Med\"; [n] children/*[1]; [t] \"StoppUtkryssing\""],["Action","multi-inference","[t] \"slutningsregel\"; [m] content/*; [t] \"med konklusjon\"; [n] children/*[1]; [t] \"og\"; [t] count(children/*[2]/children/*); [t] \"premisser\""],["Action","inference","[t] \"slutningsregel\"; [m] content/*; [t] \"med konklusjon\"; [n] children/*[1]; [t] \"og\"; [t] count(children/*[2]/children/*); [t] \"premiss\""],["Action","premise","[m] children/* (ctxtFunc:CTFordinalCounter, context:\"premiss \")"],["Action","conclusion","[n] children/*[1]"],["Action","label","[t] \"etikett\"; [n] children/*[1]"],["Action","axiom","[t] \"aksiom\"; [m] children/*[1]"],["Action","empty-axiom","[t] \"tomt aksiom\""]]}
+,
+"nb/rules/prefix_bokmal.min": 
+{"modality":"prefix","locale":"nb","inherits":"base","domain":"default","rules":[]}
+,
+"nb/rules/prefix_bokmal_actions.min": 
+{"modality":"prefix","locale":"nb","domain":"default","kind":"actions","rules":[["Action","numerator","[t] \"Teller\" (pause:200)"],["Action","denominator","[t] \"Nevner\" (pause:200)"],["Action","base","[t] \"Basis\" (pause:200)"],["Action","exponent","[t] \"Eksponent\" (pause:200)"],["Action","subscript","[t] \"Indeks\" (pause:200)"],["Action","overscript","[t] \"Øvre grenseverdi\" (pause:200)"],["Action","underscript","[t] \"Nedre grenseverdi\" (pause:200)"],["Action","radicand","[t] \"Radikand\" (pause:200)"],["Action","index","[t] \"Indeks\" (pause:200)"],["Action","leftsub","[t] \"Venstre subskript\" (pause:200)"],["Action","leftsub-counted","[t] CSFordinalPosition; [t] \"Venstre subskript\" (pause:200)"],["Action","leftsuper","[t] \"Venstre superskript\" (pause:200)"],["Action","leftsuper-counted","[t] CSFordinalPosition; [t] \"Venstre superskript\" (pause:200)"],["Action","rightsub","[t] \"Høyre subskript\" (pause:200)"],["Action","rightsub-counted","[t] CSFordinalPosition; [t] \"Høyre subskript\" (pause:200)"],["Action","rightsuper","[t] \"Høyre superskript\" (pause:200)"],["Action","rightsuper-counted","[t] CSFordinalPosition; [t] \"Høyre superskript\" (pause:200)"],["Action","choice","[t] \"Utvalg\" (pause:200)"],["Action","select","[t] \"Stikkprøvestørrelse\" (pause:200)"],["Action","row","[t] CSFordinalPosition; [t] \"Rad\" (pause:200)"],["Action","cell","[n] ../..; [t] CSFordinalPosition; [t] \"Kolonne\" (pause:200)"],["Action","cell-simple","[t] CSFordinalPosition; [t] \"Kolonne\" (pause:200)"]]}
+,
+"nb/rules/summary_bokmal.min": 
+{"modality":"summary","locale":"nb","inherits":"base","rules":[]}
+,
+"nb/rules/summary_bokmal_actions.min": 
+{"modality":"summary","locale":"nb","kind":"actions","rules":[["Action","abstr-identifier-long","[t] \"lang identifikator\""],["Action","abstr-identifier","[t] \"identifikator\""],["Action","abstr-number-long","[t] \"langt nummer\""],["Action","abstr-number","[t] \"nummer\""],["Action","abstr-mixed-number-long","[t] \"langt blandet nummer\""],["Action","abstr-mixed-number","[t] \"blandet nummer\""],["Action","abstr-text","[t] \"tekst\""],["Action","abstr-function","[t] \"funksjonsuttrykk\""],["Action","abstr-function-brief","[t] \"funksjon\""],["Action","abstr-lim","[t] \"grenseverdifunksjon\""],["Action","abstr-lim-brief","[t] \"grenseverdi\""],["Action","abstr-fraction","[t] \"brøk\""],["Action","abstr-fraction-brief","[t] \"brøk\""],["Action","abstr-continued-fraction","[t] \"kjedebrøk\""],["Action","abstr-continued-fraction-brief","[t] \"kjedebrøk\""],["Action","abstr-sqrt","[t] \"kvadratrot\""],["Action","abstr-sqrt-nested","[t] \"n-te rot\""],["Action","abstr-root-end","[t] \"rot med eksponent\"; [n] children/*[1] (engine:modality=speech); [t] \"slutteksponent\""],["Action","abstr-root","[t] \"rot med eksponent\"; [n] children/*[1] (engine:modality=speech)"],["Action","abstr-root-brief","[t] \"rot\""],["Action","abstr-root-nested-end","[t] \"nøstet rot med eksponent\"; [n] children/*[1] (engine:modality=speech); [t] \"slutteksponent\""],["Action","abstr-root-nested","[t] \"nøstet rot med eksponent\"; [n] children/*[1] (engine:modality=speech)"],["Action","abstr-root-nested-brief","[t] \"nøstet rot\""],["Action","abstr-superscript","[t] \"potens\""],["Action","abstr-subscript","[t] \"indeks\""],["Action","abstr-subsup","[t] \"potens med indeks\""],["Action","abstr-infixop","[t] @role (grammar:localRole); [t] \"med\"; [t] count(./children/*); [t] \"elementer\""],["Action","abstr-infixop-var","[t] @role (grammar:localRole); [t] \"med variabelt antall elementer\""],["Action","abstr-infixop-brief","[t] @role (grammar:localRole)"],["Action","abstr-addition","[t] \"sum med\"; [t] count(./children/*); [t] \"delsummer\""],["Action","abstr-addition-brief","[t] \"sum\""],["Action","abstr-addition-var","[t] \"sum med variabelt antall delsummer\""],["Action","abstr-multiplication","[t] \"produkt med\"; [t] count(./children/*); [t] \"faktorer\""],["Action","abstr-multiplication-brief","[t] \"produkt\""],["Action","abstr-multiplication-var","[t] \"produkt med variabelt antall faktorer\""],["Action","abstr-vector","[t] count(./children/*); [t] \"dimensjonal vektor\""],["Action","abstr-vector-brief","[t] \"vektor\""],["Action","abstr-vector-var","[t] \"n-dimensjonal vektor\""],["Action","abstr-binomial","[t] \"binomialkoeffisient\""],["Action","abstr-determinant","[t] count(./children/*); [t] \"dimensjonal determinant\""],["Action","abstr-determinant-brief","[t] \"determinant\""],["Action","abstr-determinant-var","[t] \"n-dimensjonal determinant\""],["Action","abstr-squarematrix","[t] count(./children/*); [t] \"dimensjonal kvadratmatrise\""],["Action","abstr-squarematrix-brief","[t] \"kvadratmatrise\""],["Action","abstr-rowvector","[t] count(./children/row/children/*); [t] \"dimensjonal radvektor\""],["Action","abstr-rowvector-brief","[t] \"radvektor\""],["Action","abstr-rowvector-var","[t] \"n-dimensjonal radvektor\""],["Action","abstr-matrix","[t] count(children/*); [t] \"ganger\"; [t] count(children/*[1]/children/*); [t] \"matrise\""],["Action","abstr-matrix-brief","[t] \"matrise\""],["Action","abstr-matrix-var","[t] \"n ganger m-dimensjonal matrise\""],["Action","abstr-cases","[t] \"case-setning\"; [t] \"med\"; [t] count(children/*); [t] \"caser\""],["Action","abstr-cases-brief","[t] \"case-setning\""],["Action","abstr-cases-var","[t] \"case-setning med variabelt antall alternativer\""],["Action","abstr-punctuated","[n] content/*[1]; [t] \"separert liste\"; [t] \"med lengde\"; [t] count(children/*) - count(content/*)"],["Action","abstr-punctuated-brief","[n] content/*[1]; [t] \"separatert liste\""],["Action","abstr-punctuated-var","[n] content/*[1]; [t] \"separert liste\"; [t] \"med variabel lengde\""],["Action","abstr-bigop","[n] content/*[1]"],["Action","abstr-integral","[t] \"integral\""],["Action","abstr-relation","[t] @role (grammar:localRole)"],["Action","abstr-relation-seq","[t] @role (grammar:localRole); [t] \"sekvens\"; [t] \"med\"; [t] count(./children/*); [t] \"elementer\""],["Action","abstr-relation-seq-brief","[t] @role (grammar:localRole); [t] \"sekvens\""],["Action","abstr-relation-var","[t] @role (grammar:localRole); [t] \"sekvens\"; [t] \"med variabelt antall elementer\""],["Action","abstr-multirel","[t] \"relasjonssekvens\"; [t] \"med\"; [t] count(./children/*); [t] \"elementer\""],["Action","abstr-multirel-brief","[t] \"relasjonssekvens\""],["Action","abstr-multirel-var","[t] \"relasjonssekvens med variabelt antall elementer\""],["Action","abstr-table","[t] \"tabell med\"; [t] count(children/*); [t] \"rader og\"; [t] count(children/*[1]/children/*); [t] \"kolonner\""],["Action","abstr-line","[t] \"i\"; [t] @role (grammar:localRole)"],["Action","abstr-row","[t] \"i\"; [t] @role (grammar:localRole); [t] count(preceding-sibling::..); [t] \"med\"; [t] count(children/*); [t] \"kolonner\""],["Action","abstr-cell","[t] \"i\"; [t] @role (grammar:localRole)"]]}
+}
+
diff --git a/devel/_static/mathjax/sre/mathmaps/nemeth.json b/devel/_static/mathjax/sre/mathmaps/nemeth.json
new file mode 100644
index 0000000000..99d2575807
--- /dev/null
+++ b/devel/_static/mathjax/sre/mathmaps/nemeth.json
@@ -0,0 +1,125 @@
+{
+"nemeth/messages/alphabets.min": 
+{"kind":"alphabets","locale":"nemeth","messages":{"latinSmall":["⠰⠁","⠰⠃","⠰⠉","⠰⠙","⠰⠑","⠰⠋","⠰⠛","⠰⠓","⠰⠊","⠰⠚","⠰⠅","⠰⠇","⠰⠍","⠰⠝","⠰⠕","⠰⠏","⠰⠟","⠰⠗","⠰⠎","⠰⠞","⠰⠥","⠰⠧","⠰⠺","⠰⠭","⠰⠽","⠰⠵"],"latinCap":["⠰⠠⠁","⠰⠠⠃","⠰⠠⠉","⠰⠠⠙","⠰⠠⠑","⠰⠠⠋","⠰⠠⠛","⠰⠠⠓","⠰⠠⠊","⠰⠠⠚","⠰⠠⠅","⠰⠠⠇","⠰⠠⠍","⠰⠠⠝","⠰⠠⠕","⠰⠠⠏","⠰⠠⠟","⠰⠠⠗","⠰⠠⠎","⠰⠠⠞","⠰⠠⠥","⠰⠠⠧","⠰⠠⠺","⠰⠠⠭","⠰⠠⠽","⠰⠠⠵"],"greekSmall":["⠨⠫","⠨⠁","⠨⠃","⠨⠛","⠨⠙","⠨⠑","⠨⠵","⠨⠱","⠨⠹","⠨⠊","⠨⠅","⠨⠇","⠨⠍","⠨⠝","⠨⠭","⠨⠕","⠨⠏","⠨⠗","⠨⠈⠎","⠨⠎","⠨⠞","⠨⠥","⠨⠈⠋","⠨⠯","⠨⠽","⠨⠺","⠈⠙","⠨⠑","⠨⠈⠹","⠨⠅","⠨⠋","⠨⠗","⠨⠏"],"greekCap":["⠨⠠⠁","⠨⠠⠃","⠨⠠⠛","⠨⠠⠙","⠨⠠⠑","⠨⠠⠵","⠨⠠⠱","⠨⠠⠹","⠨⠠⠊","⠨⠠⠅","⠨⠠⠇","⠨⠠⠍","⠨⠠⠝","⠨⠠⠭","⠨⠠⠕","⠨⠠⠏","⠨⠠⠗","⠨⠠⠹","⠨⠠⠎","⠨⠠⠞","⠨⠠⠥","⠨⠠⠋","⠨⠠⠯","⠨⠠⠽","⠨⠠⠺"],"capPrefix":{"default":""},"smallPrefix":{"default":""},"digitPrefix":{"default":"⠼"},"languagePrefix":{"greek":"⠨","english":"⠰","german":"⠸","hebrew":"⠠⠠","number":"⠼"}}}
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new file mode 100644
index 0000000000..dc0f40519e
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(grammar:EndRoot); [t] \"slutt rot\" (pause:short)"],["Action","negative","[t] \"minus\" (span:content/*[1]); [n] children/*[1]"],["Action","positive","[t] \"pluss\" (span:content/*[1]); [n] children/*[1]"],["Action","angle-measure","[t] \"vinkelmål\"; [n] content/*[1]; [n] children/*[2] (grammar:angle)"],["Action","set-prefix-operators","[n] . (grammar:!prefix:addArticle); [t] \"av\""],["Action","division","[n] children/*[1]; [t] \"delt på\" (span:content/*[1]); [n] children/*[2]"],["Action","operators-after-power","[m] children/* (rate:\"0.5\")"],["Action","natural-numbers","[t] \"dei naturlege tala\" (span:.)"],["Action","integers","[t] \"heiltala\" (span:.)"],["Action","rational-numbers","[t] \"dei rasjonale tala\" (span:.)"],["Action","real-numbers","[t] \"dei reelle tala\" (span:.)"],["Action","complex-numbers","[t] \"dei komplekse tala\" (span:.)"],["Action","natural-numbers-with-zero","[t] \"dei naturlege tala med null\" (span:.)"],["Action","positive-integers","[t] \"dei positive heiltala\" (span:.)"],["Action","negative-integers","[t] \"dei negative heltala\" (span:.)"],["Action","positive-rational-numbers","[t] \"de positive rasjonale tala\" (span:.)"],["Action","negative-rational-numbers","[t] \"de negative rasjonale tala\" (span:.)"],["Action","fences-neutral","[p] (pause:short); [t] \"den absolutte verdien av\" (span:.); [n] children/*[1] (pause:short)"],["Action","fences-neutral-absend","[p] (pause:short); [t] \"den absolutte verdien av\" (span:content/*[1]); [n] children/*[1] (pause:short); [t] \"slutt absolutt verdi\" (span:content/*[1], pause:short)"],["Action","fences-neutral-cardinality","[p] (pause:short); [t] \"kardinaliteten til\" (span:.); [n] children/*[1] (pause:short)"],["Action","fences-neutral-determinant","[p] (pause:short); [t] \"determinanten til\" (span:.); [n] children/*[1] (pause:short)"],["Action","fences-metric","[p] (pause:short); [t] \"berekninga av\" (span:.); [n] children/*[1] (pause:short)"],["Action","fences-metric-absend","[p] (pause:short); [t] \"berekninga av\" (span:content/*[1]); [n] children/*[1] (pause:short); [t] \"slutt berekning\" (span:content/*[1], pause:short)"],["Action","matrix","[t] count(children/*); [t] \"ganger\"; [t] count(children/*[1]/children/*); [t] \"matrisa\" (pause:long); [m] children/* (ctxtFunc:CTFnodeCounter, context:\"rad-:\", pause:long)"],["Action","matrix-simple","[t] count(children/*); [t] \"ganger\"; [t] count(children/*[1]/children/*); [t] \"matrisen\" (pause:long); [m] children/* (ctxtFunc:CTFnodeCounter, context:\"rad-:\", pause:long, grammar:simpleDet)"],["Action","matrix-trivial","[t] \"1 ganger 1 matrisa med element\"; [n] children/*[1] (pause:long)"],["Action","determinant","[t] \"determinanten til\"; [t] count(children/*); [t] \"ganger\"; [t] count(children/*[1]/children/*); [t] \"matrisa\" (pause:long); [m] children/* (ctxtFunc:CTFnodeCounter, context:\"rad-:\", pause:long, grammar:simpleDet)"],["Action","determinant-simple","[t] \"determinanten til\"; [t] count(children/*); [t] \"ganger\"; [t] count(children/*[1]/children/*); [t] \"matrisa\" (pause:long); [m] children/* (ctxtFunc:CTFnodeCounter, context:\"rad-:\", pause:long)"],["Action","matrix-vector","[t] count(children/*); [t] \"ganger\"; [t] count(children/*[1]/children/*); [t] \"kolonnematrise\" (pause:long); [m] children/* (ctxtFunc:CTFnodeCounter, context:\"rad-:\", pause:long, grammar:simpleDet)"],["Action","matrix-vector-simple","[t] count(children/*); [t] \"ganger\"; [t] count(children/*[1]/children/*); [t] \"kolonnematrise\" (pause:long); [m] children/* (sepFunc:CTFpauseSeparator, separator:\"short\", pause:long, grammar:simpleDet)"],["Action","matrix-vector-simple-silentcolnum","[t] count(children/*); [t] \"ganger\"; [t] count(children/*[1]/children/*); [t] \"kolonnematrise\" (pause:long); [m] children/* (sepFunc:CTFpauseSeparator, separator:\"short\", pause:long, grammar:simpleDet)"],["Action","matrix-row-vector","[t] count(children/*); [t] \"ganger\"; [t] count(children/*[1]/children/*); [t] \"radmatrise\" (pause:long); [m] children/*[1]/children/* (ctxtFunc:CTFnodeCounter, context:\"kolonne-:\", pause:long, grammar:simpleDet)"],["Action","matrix-row-vector-simple","[t] count(children/*); [t] \"ganger\"; [t] count(children/*[1]/children/*); [t] \"radmatrise\" (pause:long); [m] children/*[1]/children/* (sepFunc:CTFpauseSeparator, separator:\"short\", pause:long, grammar:simpleDet)"],["Action","matrix-row-vector-simple-silentcolnum","[t] count(children/*); [t] \"ganger\"; [t] count(children/*[1]/children/*); [t] \"radmatrise\" (pause:long); [m] children/*[1]/children/* (sepFunc:CTFpauseSeparator, separator:\"short\", pause:long, grammar:simpleDet)"],["Action","matrix-row","[m] children/* (ctxtFunc:CTFnodeCounter, context:\"kolonne-,- \", sepFunc:CTFpauseSeparator, separator:\"medium\", pause:long)"],["Action","matrix-end-matrix","[n] . (grammar:EndMatrix); [t] \"slutt matrise\""],["Action","matrix-end-vector","[n] . (grammar:EndMatrix); [t] \"slutt matrise\""],["Action","matrix-end-determinant","[n] . (grammar:EndMatrix); [t] \"slutt determinant\""],["Action","vector","[t] count(children/*); [t] \"ganger\"; [t] count(children/*[1]/children/*); [t] \"kolonnevektor\" (pause:long); [m] children/* (ctxtFunc:CTFnodeCounter, context:\"rad-:\", pause:long, grammar:simpleDet)"],["Action","vector-simple","[t] count(children/*); [t] \"ganger\"; [t] count(children/*[1]/children/*); [t] \"kolonnevektor\" (pause:long); [m] children/* (sepFunc:CTFpauseSeparator, separator:\"short\", pause:long, grammar:simpleDet)"],["Action","row-vector","[t] count(children/*); [t] \"ganger\"; [t] count(children/*[1]/children/*); [t] \"radvektor\" (pause:long); [m] children/*[1]/children/* (ctxtFunc:CTFnodeCounter, context:\"kolonne-:\", pause:long, grammar:simpleDet)"],["Action","row-vector-simple","[t] count(children/*); [t] \"ganger\"; [t] count(children/*[1]/children/*); [t] \"radvektor\" (pause:long); [m] children/*[1]/children/* (sepFunc:CTFpauseSeparator, separator:\"short\", pause:long, grammar:simpleDet)"],["Action","vector-end-matrix","[n] . (grammar:EndMatrix); [t] \"slutt matrise\""],["Action","vector-end-vector","[n] . (grammar:EndMatrix); [t] \"slutt vektor\""],["Action","vector-end-vector-endvector","[n] . (grammar:EndMatrix); [t] \"slutt vektor\""],["Action","vector-end-determinant","[n] . (grammar:EndMatrix); [t] \"slutt determinant\""],["Action","binomial","[n] children/*[1]/children/*[1]; [t] \"over\"; [n] children/*[2]/children/*[1]"],["Action","lines-summary","[p] (pause:short); [t] count(children/*); [t] \"rader\"; [n] . (grammar:layoutSummary)"],["Action","cases-summary","[p] (pause:short); [t] count(children/*); [t] \"tilfelle\"; [n] . (grammar:layoutSummary)"],["Action","lines","[p] (pause:short); [m] children/* (ctxtFunc:CTFnodeCounter, context:\"rad-:\", sepFunc:CTFpauseSeparator, separator:\"long\", pause:long)"],["Action","blank-cell","[t] \"tom\""],["Action","blank-line","[t] \"tom\""],["Action","blank-cell-empty","[t] \"tom\""],["Action","blank-line-empty","[t] \"tom\""],["Action","cases","[p] (pause:short); [m] children/* (ctxtFunc:CTFnodeCounter, context:\"tilfelle-:\", sepFunc:CTFpauseSeparator, separator:\"long\", pause:long)"],["Action","lines-cases-summary","[p] (pause:short); [t] count(children/*); [t] \"tilfelle\"; [n] . (grammar:layoutSummary)"],["Action","lines-cases","[p] (pause:short); [m] children/* (ctxtFunc:CTFnodeCounter, context:\"tilfelle-:\", sepFunc:CTFpauseSeparator, separator:\"long\", pause:long)"],["Action","lines-equations-summary","[p] (pause:short); [t] count(children/*); [t] \"likningar\"; [n] . (grammar:layoutSummary)"],["Action","lines-equations","[p] (pause:short); [m] children/* (ctxtFunc:CTFnodeCounter, context:\"ligning-:\", sepFunc:CTFpauseSeparator, separator:\"long\", pause:long)"],["Action","lines-steps-summary","[p] (pause:short); [t] count(children/*); [t] \"steg\"; [n] . (grammar:layoutSummary)"],["Action","lines-steps","[p] (pause:short); [m] children/* (ctxtFunc:CTFnodeCounter, context:\"steg-:\", sepFunc:CTFpauseSeparator, separator:\"long\", pause:long)"],["Action","lines-rows-summary","[p] (pause:short); [t] count(children/*); [t] \"rader\"; [n] . (grammar:layoutSummary)"],["Action","lines-rows","[p] (pause:short); [m] children/* (ctxtFunc:CTFnodeCounter, context:\"rad-:\", sepFunc:CTFpauseSeparator, separator:\"long\", pause:long)"],["Action","lines-constraints-summary","[p] (pause:short); [t] count(children/*); [t] \"føresetnadar\"; [n] . (grammar:layoutSummary)"],["Action","lines-constraints","[p] (pause:short); [m] children/* (ctxtFunc:CTFnodeCounter, context:\"betingelse-:\", sepFunc:CTFpauseSeparator, separator:\"long\", pause:long)"],["Action","bigop","[n] children/*[1] (grammar:addArticle); [t] \"over\"; [n] children/*[2] (pause:short)"],["Action","limboth","[n] children/*[1]; [t] \"frå\"; [n] children/*[2]; [t] \"til\"; [n] children/*[3]"],["Action","limlower","[n] children/*[1]; [t] \"over\"; [n] children/*[2] (pause:short)"],["Action","limupper","[n] children/*[1]; [t] \"under\"; [n] children/*[2] (pause:short)"],["Action","integral","[n] children/*[1] (grammar:addArticle); [t] \"over\"; [n] children/*[2] (pause:short); [n] children/*[3] (pause:short)"],["Action","integral-novar","[n] children/*[1] (grammar:addArticle); [t] \"over\"; [n] children/*[2] (pause:short)"],["Action","overscript","[n] children/*[1]; [t] \"under\"; [n] children/*[2] (pause:short)"],["Action","overscript-limits","[n] children/*[1]; [t] \"til\"; [n] children/*[2]"],["Action","underscript","[n] children/*[1]; [t] \"over\"; [n] children/*[2] (pause:short)"],["Action","underscript-limits","[n] children/*[1]; [t] \"frå\"; [n] children/*[2]"],["Action","mixed-number","[n] children/*[1]; [t] \"og\"; [n] children/*[2]"],["Action","number-with-chars","[t] \"tal\"; [m] CQFspaceoutNumber (grammar:protected)"],["Action","decimal-period","[t] \"desimalbrøk\"; [n] children/*[1] (grammar:spaceout); [t] \"komma etterfølgt av gjentatte siffer\"; [n] children/*[3]/children/*[1] (grammar:spaceout)"],["Action","decimal-period-float","[t] \"desimalbrøk\"; [n] children/*[1] (grammar:spaceout); [t] \"etterfølgt av gjentatte siffer\"; [n] children/*[2]/children/*[1] (grammar:spaceout)"],["Action","decimal-period-singular","[t] \"desimalbrøk\"; [n] children/*[1] (grammar:spaceout); [t] \"komma etterfølgt av gjentatt siffer\"; [n] children/*[3]/children/*[1] (grammar:spaceout)"],["Action","decimal-period-singular-float","[t] \"desimalbrøk\"; [n] children/*[1] (grammar:spaceout); [t] \"etterfølgt av gjentatt siffer\"; [n] children/*[2]/children/*[1] (grammar:spaceout)"],["Action","decimal-point","[t] \"komma\""],["Action","line-segment","[t] \"linjestykket\"; [n] children/*[1]/children/*[1]; [n] children/*[1]/children/*[2] (pause:short)"],["Action","conjugate","[t] \"komplekskonjugatet til\"; [n] children/*[1]"],["Action","defined-by","[t] \"er definert som\" (pause:short)"],["Action","adorned-sign","[n] children/*[1]; [t] \"teikn med\"; [n] children/*[2]; [t] \"over\""],["Action","factorial","[t] \"fakultet\""],["Action","left-super","[t] \"venstre øvre indeks\"; [n] text()"],["Action","left-super-list","[t] \"venstre øvre indeks\"; [m] children/*"],["Action","left-sub","[t] \"venstre nedre indeks\"; [n] text()"],["Action","left-sub-list","[t] \"venstre nedre indeks\"; [m] children/*"],["Action","right-super","[t] \"høgre øvre indeks\"; [n] text()"],["Action","right-super-list","[t] \"høgre øvre indeks\"; [m] children/*"],["Action","right-sub","[t] \"høgre nedre indeks\"; [n] text()"],["Action","right-sub-list","[t] \"høgre nedre indeks\"; [m] children/*"],["Action","choose","[n] children/*[2] (grammar:combinatorics); [t] \"over\"; [n] children/*[4] (grammar:combinatorics)"],["Action","permute","[n] children/*[2] (grammar:combinatorics); [t] \"permutasjon av\"; [n] children/*[4] (grammar:combinatorics)"],["Action","unit-square","[t] \"kvadrat\"; [n] children/*[1]"],["Action","unit-cubic","[t] \"kubikk\"; [n] children/*[1]"],["Action","unit-reciprocal","[t] \"invers\"; [n] children/*[1]"],["Action","unit-reciprocal-singular","[t] \"per\"; [n] children/*[1] (grammar:singular)"],["Action","unit-divide","[n] children/*[1]; [t] \"per\"; [n] children/*[2] (grammar:singular)"],["Action","enclose","[t] \"omslutta av\"; [t] @role (grammar:localEnclose); [n] children/*[1]"],["Action","enclose-end","[t] \"omslutta av\"; [t] @role (grammar:localEnclose); [n] children/*[1]; [t] \"slutt omslutta\""],["Action","overbar","[n] children/*[1]; [t] \"horisontal strek\""],["Action","underbar","[n] children/*[1]; [t] \"over horisontal strek\""],["Action","leftbar","[t] \"vertikal strek\"; [n] children/*[1]"],["Action","rightbar","[n] children/*[1]; [t] \"vertikal strek\""],["Action","crossout","[t] \"utkryssa\"; [n] children/*[1]"],["Action","crossout-end","[t] \"utkryssa\"; [n] children/*[1]; [t] \"slutt utkryssa\""],["Action","cancel-over","[n] children/*[1]/children/*[1]; [t] \"utkryssa med\"; [n] children/*[2]"],["Action","cancel-under","[n] children/*[2]/children/*[1]; [t] \"utkryssa med\"; [n] children/*[1]"],["Action","cancel-over-end","[n] children/*[1]/children/*[1]; [t] \"utkryssa med\"; [n] children/*[2]; [t] \"slutt utkryssa\""],["Action","cancel-under-end","[n] children/*[2]/children/*[1]; [t] \"utkrysset med\"; [n] children/*[1]; [t] \"slutt utkryssa\""]]}
+,
+"nn/rules/mathspeak_nynorsk.min": 
+{"inherits":"base","locale":"nn","domain":"mathspeak","modality":"speech","rules":[]}
+,
+"nn/rules/mathspeak_nynorsk_actions.min": 
+{"kind":"actions","locale":"nn","domain":"mathspeak","modality":"speech","rules":[["Action","collapsed","[t] \"kollapsa\"; [n] . (engine:modality=summary, grammar:collapsed)"],["Action","blank-cell-empty","[t] \"tom\""],["Action","blank-line-empty","[t] \"tom\""],["Action","font","[t] @font (grammar:localFont); [n] . (grammar:ignoreFont=@font)"],["Action","mixed-number","[n] children/*[1]; [t] \"og\"; [n] children/*[2]"],["Action","number-with-chars","[t] \"nummer\"; [m] CQFspaceoutNumber (grammar:protected)"],["Action","number-with-chars-brief","[t] \"num\"; [m] CQFspaceoutNumber (grammar:protected)"],["Action","number-as-upper-word","[t] \"ord stor\"; [t] CSFspaceoutText"],["Action","number-baseline","[t] \"grunnlinje\"; [n] . (grammar:baseline)"],["Action","number-baseline-brief","[t] \"basis\"; [n] . (grammar:baseline)"],["Action","number-baseline-font","[t] \"grunnlinje\"; [t] @font; [n] . (grammar:ignoreFont=@font)"],["Action","number-baseline-font-brief","[t] \"basis\"; [t] @font; [n] . (grammar:ignoreFont=@font)"],["Action","negative-number","[t] \"negativ\"; [n] children/*[1]"],["Action","negative","[t] \"minus\"; [n] children/*[1]"],["Action","division","[n] children/*[1]; [t] \"delt på\"; [n] children/*[2]"],["Action","subtraction","[m] children/* (separator:\"minus\")"],["Action","fences-neutral","[t] \"StartAbsoluttVerdi\"; [n] children/*[1]; [t] \"StoppAbsoluttVerdi\""],["Action","fences-neutral-sbrief","[t] \"AbsoluttVerdi\"; [n] children/*[1]; [t] \"StoppAbsoluttVerdi\""],["Action","fences-metric","[t] \"StartMetrisk\"; [n] children/*[1]; [t] \"StoppMetrisk\""],["Action","fences-metric-sbrief","[t] \"Metrisk\"; [n] children/*[1]; [t] \"StoppMetrisk\""],["Action","empty-set","[t] \"tomt sett\""],["Action","fences-set","[t] \"StartSett\"; [n] children/*[1]; [t] \"StoppSett\""],["Action","fences-set-sbrief","[t] \"Sett\"; [n] children/*[1]; [t] \"StoppSet\""],["Action","factorial","[t] \"faktorisk\""],["Action","minus","[t] \"minus\""],["Action","continued-fraction-outer","[t] \"KontinuerligBrøk\"; [n] children/*[1]; [t] \"Over\"; [n] children/*[2]"],["Action","continued-fraction-outer-brief","[t] \"KontinuerligBrøk\"; [n] children/*[1]; [t] \"Over\"; [n] children/*[2]"],["Action","continued-fraction-inner","[t] \"StartBrøk\"; [n] children/*[1]; [t] \"Over\"; [n] children/*[2]"],["Action","continued-fraction-inner-brief","[t] \"StartBrøk\"; [n] children/*[1]; [t] \"Over\"; [n] children/*[2]"],["Action","continued-fraction-inner-sbrief","[t] \"Brøk\"; [n] children/*[1]; [t] \"Over\"; [n] children/*[2]"],["Action","integral","[n] children/*[1]; [t] \"subskript\"; [n] children/*[2]; [t] \"superskript\"; [n] children/*[3]; [t] \"grunnlinje\""],["Action","integral-brief","[n] children/*[1]; [t] \"sub\"; [n] children/*[2]; [t] \"super\"; [n] children/*[3]; [t] \"grunn\""],["Action","square","[n] children/*[1]; [t] \"i andre potens\""],["Action","cube","[n] children/*[1]; [t] \"i tredje potens\""],["Action","prime","[n] children/*[1]; [n] children/*[2]"],["Action","counted-prime","[t] count(children/*) (grammar:numbers2alpha); 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diff --git a/devel/_static/mathjax/sre/mathmaps/sv.json b/devel/_static/mathjax/sre/mathmaps/sv.json
new file mode 100644
index 0000000000..2269fcc7e6
--- /dev/null
+++ b/devel/_static/mathjax/sre/mathmaps/sv.json
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(grammar:functions_none)"],["Action","superscript-ordinal-number","[n] children/*[1]; [t] \"upphöjt till\"; [n] children/*[2] (pause:short, grammar:ordinal:noArticle)"],["Action","superscript-ordinal-negative","[n] children/*[1]; [t] \"upphöjt till\"; [n] children/*[2] (pause:short, grammar:noArticle)"],["Action","superscript-ordinal-default","[n] children/*[1]; [t] \"upphöjt till exponenten\" (pause:short); [n] children/*[2] (pause:short); [t] \"slut exponent\" (pause:short)"],["Action","superscript-ordinal-power-number","[n] children/*[1]; [t] \"upphöjt till\"; [n] children/*[2] (pause:short, grammar:ordinal:noArticle)"],["Action","superscript-ordinal-power-negative","[n] children/*[1]; [t] \"upphöjt till\"; [n] children/*[2] (pause:short, grammar:noArticle)"],["Action","superscript-ordinal-power-identifier","[n] children/*[1]; [t] \"upphöjt till\"; [n] children/*[2] (pause:short, grammar:ordinal:noArticle)"],["Action","superscript-ordinal-power-default","[n] children/*[1]; [t] \"upphöjt till exponenten\" (pause:short); [n] children/*[2] (pause:short); [t] \"slut exponent\" (pause:short)"],["Action","superscript-power","[n] children/*[1]; [t] \"upphöjt till exponenten\"; [n] children/*[2] (pause:short, grammar:afterPower)"],["Action","superscript-power-default","[n] children/*[1]; [t] \"upphöjt till exponenten\" (pause:short); [n] children/*[2] (pause:short); [t] \"slut exponent\" (pause:short)"],["Action","exponent","[n] text()"],["Action","exponent-number","[t] CSFordinalExponent"],["Action","exponent-ordinal","[t] CSFwordOrdinal"],["Action","exponent-ordinal-zero","[t] \"noll\""],["Action","exponent-ordinalpower","[t] CSFwordOrdinal"],["Action","exponent-ordinalpower-zero","[t] \"noll\""],["Action","square","[n] children/*[1]; [t] \"kvadrat\" (span:children/*[2])"],["Action","cube","[n] children/*[1]; [t] \"kubik\" (span:children/*[2])"],["Action","fences-points","[t] \"Punkten med koordinaterna\"; [n] children/*[1]"],["Action","fences-interval","[t] \"intervallet från\"; [n] children/*[1]/children/*[1]; [t] \"till\"; [n] children/*[1]/children/*[3] (pause:short); [n] . (grammar:interval)"],["Action","interval-open","[t] \"utan\"; [n] children/*[1]/children/*[1]; [t] \"eller\"; [n] children/*[1]/children/*[3]"],["Action","interval-closed-open","[t] \"inkluderat\"; [n] children/*[1]/children/*[1] (pause:short); [t] \"men utan\"; [n] children/*[1]/children/*[3]"],["Action","interval-open-closed","[t] \"utan\"; [n] children/*[1]/children/*[1] (pause:short); [t] \"men inkluderat\"; [n] children/*[1]/children/*[3]"],["Action","interval-closed","[t] \"inkluderat\"; [n] children/*[1]/children/*[1]; [t] \"och\"; [n] children/*[1]/children/*[3]"],["Action","interval-open-inf-r","[t] \"utan\"; [n] children/*[1]/children/*[1]"],["Action","interval-open-inf-l","[t] \"utan\"; [n] children/*[1]/children/*[3]"],["Action","interval-closed-open-inf","[t] \"inkluderat\"; [n] children/*[1]/children/*[1]"],["Action","interval-open-closed-inf","[t] \"inkluderat\"; [n] children/*[1]/children/*[3]"],["Action","set-empty","[t] \"den tomma mängden\""],["Action","set-extended","[t] \"hela mängden\"; [n] children/*[1]/children/*[1]; [t] \"med\"; [n] children/*[1]/children/*[3]"],["Action","set-collection","[t] \"mängden\"; [n] children/*[1]"],["Action","set-extended-woall","[t] \"mängden av\"; [n] children/*[1]/children/*[1]; [t] \"med\"; [n] children/*[1]/children/*[3]"],["Action","subscript","[p] (pause:short); [n] children/*[1]; [t] \"index\"; [n] children/*[2] (pause:short)"],["Action","logarithm-base","[n] children/*[1]; [t] \"bas\"; [n] children/*[2]"],["Action","subscript-index","[n] children/*[1]; [t] \"index\"; [n] children/*[2]"],["Action","fraction","[p] (pause:short); [t] \"bråket med täljare\"; [n] children/*[1] (pause:short); [t] \"och nämnare\"; [n] children/*[2] (pause:short)"],["Action","fraction-none","[p] (pause:short); [t] \"bråket med täljare\"; [n] children/*[1] (pause:short); [t] \"och nämnare\"; [n] children/*[2] (pause:short)"],["Action","simple-fraction","[p] (pause:short); [n] children/*[1]; [t] \"delat med\"; [n] children/*[2] (pause:short)"],["Action","simple-vulgar-fraction","[p] (pause:short); [n] children/*[1]; [t] \"delat med\"; [n] children/*[2] (pause:short)"],["Action","simple-text-fraction","[p] (pause:short); [n] children/*[1]; [t] \"delat med\"; [n] children/*[2] (pause:short)"],["Action","vulgar-fraction","[t] CSFvulgarFraction (grammar:correctOne)"],["Action","fraction-over","[p] (pause:short); [n] children/*[1]; [t] \"delat med\"; [n] children/*[2] (pause:short)"],["Action","fraction-overendfrac","[p] (pause:short); [n] children/*[1]; [t] \"delat med\"; [n] children/*[2] (pause:short); [t] \"slut bråk\" (pause:short)"],["Action","fraction-fracover","[p] (pause:short); [t] \"bråket\"; [n] children/*[1]; [t] \"delat med\"; [n] children/*[2] (pause:short)"],["Action","fraction-per","[p] (pause:short); [n] children/*[1]; [t] \"per\"; [n] children/*[2] (pause:short)"],["Action","fraction-generalendfrac","[p] (pause:short); [t] \"bråket med täljare\"; [n] children/*[1] (pause:short); [t] \"och nämnare\"; [n] children/*[2] (pause:short); [t] \"slut bråk\" (pause:short)"],["Action","fraction-general","[p] (pause:short); [t] \"bråket med täljare\"; [n] children/*[1] (pause:short); [t] \"och nämnare\"; [n] children/*[2] (pause:short)"],["Action","simple-vulgar-fraction-ordinal","[t] CSFvulgarFraction"],["Action","fraction-endfrac","[p] (pause:short); [n] . (grammar:endfrac); [t] \"slut bråk\" (pause:short)"],["Action","vulgar-fraction-endfrac","[p] (pause:short); [n] children/*[1]; [t] \"delat med\"; [n] children/*[2] (pause:short)"],["Action","simple-vulgar-fraction-endfrac","[t] CSFvulgarFraction"],["Action","sqrt","[t] \"kvadratroten av\"; [n] children/*[1] (pause:short, grammar:!EndRoot)"],["Action","sqrt-nested","[p] (pause:\"short\"); [t] \"kvadratroten av\"; [n] children/*[1] (pause:short, grammar:!EndRoot)"],["Action","negative-sqrt","[t] \"den negativa kvadratroten av\"; [n] children/*[1]/children/*[1] (pause:short, grammar:!EndRoot)"],["Action","negative-sqrt-default","[p] (pause:\"short\"); [t] \"den negativa kvadratroten av\"; [n] children/*[1]/children/*[1] (pause:short, grammar:!EndRoot)"],["Action","sqrt-plus-minus","[t] \"den positiva kvadratroten av\"; [n] children/*[1] (pause:short, grammar:!EndRoot)"],["Action","sqrt-nested-plus-minus","[p] (pause:\"short\"); [t] \"den positiva kvadratroten av\"; [n] children/*[1] (pause:short, grammar:!EndRoot)"],["Action","sqrt-plus-minus-posnegsqrootend","[t] \"den positiva kvadratroten av\"; [n] children/*[1] (pause:short, grammar:!EndRoot)"],["Action","sqrt-nested-plus-minus-posnegsqrootend","[p] (pause:\"short\"); [t] \"den positiva kvadratroten av\"; [n] children/*[1] (pause:short, grammar:!EndRoot)"],["Action","sqrt-endroot","[n] . (grammar:EndRoot); [t] \"slut rot\" (pause:short)"],["Action","negative-sqrt-endroot","[n] . (grammar:EndRoot); [t] \"slut rot\" (pause:short)"],["Action","sqrt-posnegsqrootend","[n] . (grammar:EndRoot); [t] \"slut rot\" (pause:short)"],["Action","negative-sqrt-posnegsqrootend","[n] . (grammar:EndRoot); [t] \"slut rot\" (pause:short)"],["Action","cubic","[t] \"kubikroten av\"; [n] children/*[2] (pause:short, grammar:!EndRoot)"],["Action","cubic-nested","[p] (pause:short); [t] \"kubikroten av\"; [n] children/*[2] (pause:short, grammar:!EndRoot)"],["Action","root","[n] children/*[1] (grammar:ordinal); [t] \"roten av\"; [n] children/*[2] (pause:short, grammar:!EndRoot)"],["Action","root-nested","[p] (pause:short); [n] children/*[1] (grammar:ordinal); [t] \"roten av\"; [n] children/*[2] (pause:short, grammar:!EndRoot)"],["Action","root-endroot","[n] . (grammar:EndRoot); [t] \"slut rot\" (pause:short)"],["Action","root-posnegsqrootend","[n] . (grammar:EndRoot); [t] \"slut rot\" (pause:short)"],["Action","negative","[t] \"minus\" (span:content/*[1]); [n] children/*[1]"],["Action","positive","[t] \"plus\" (span:content/*[1]); [n] children/*[1]"],["Action","angle-measure","[t] \"vinkelmåttet av\"; [n] content/*[1]; [n] children/*[2] (grammar:angle)"],["Action","set-prefix-operators","[n] . (grammar:!prefix); [t] \"av\""],["Action","division","[n] children/*[1]; [t] \"delat med\" (span:content/*[1]); [n] children/*[2]"],["Action","operators-after-power","[m] children/* (rate:\"0.5\")"],["Action","natural-numbers","[t] \"de naturliga talen\" (span:.)"],["Action","integers","[t] \"heltalen\" (span:.)"],["Action","rational-numbers","[t] \"de rationella talen\" (span:.)"],["Action","real-numbers","[t] \"de reella talen\" (span:.)"],["Action","complex-numbers","[t] \"de komplexa talen\" (span:.)"],["Action","natural-numbers-with-zero","[t] \"de naturliga talen med noll\" (span:.)"],["Action","positive-integers","[t] \"de positiva heltalen\" (span:.)"],["Action","negative-integers","[t] \"de negativa heltalen\" (span:.)"],["Action","positive-rational-numbers","[t] \"de positiva rationella talen\" (span:.)"],["Action","negative-rational-numbers","[t] \"de negativa rationella talen\" (span:.)"],["Action","fences-neutral","[p] (pause:short); [t] \"absolutvärdet av\" (span:.); [n] children/*[1] (pause:short)"],["Action","fences-neutral-absend","[p] (pause:short); [t] \"absolutvärdet av\" (span:content/*[1]); [n] children/*[1] (pause:short); [t] \"slut absolutvärde\" (span:content/*[1], pause:short)"],["Action","fences-neutral-cardinality","[p] (pause:short); [t] \"kardinaliteten av\" (span:.); [n] children/*[1] (pause:short)"],["Action","fences-neutral-determinant","[p] (pause:short); [t] \"determinanten av\" (span:.); [n] children/*[1] (pause:short)"],["Action","fences-metric","[p] (pause:short); [t] \"beloppet av\" (span:.); [n] children/*[1] (pause:short)"],["Action","fences-metric-absend","[p] (pause:short); [t] \"beloppet av\" (span:content/*[1]); [n] children/*[1] (pause:short); [t] \"slutbelopp\" (span:content/*[1], pause:short)"],["Action","matrix","[t] count(children/*); [t] \"gånger\"; [t] count(children/*[1]/children/*); [t] \"matrisen\" (pause:long); [m] children/* (ctxtFunc:CTFnodeCounter, context:\"rad-:\", pause:long)"],["Action","matrix-simple","[t] count(children/*); [t] \"gånger\"; [t] count(children/*[1]/children/*); [t] \"matrisen\" (pause:long); [m] children/* (ctxtFunc:CTFnodeCounter, context:\"rad-:\", pause:long, grammar:simpleDet)"],["Action","matrix-trivial","[t] \"en gånger en matrisen med element\"; [n] children/*[1] (pause:long)"],["Action","determinant","[t] \"determinanten av\"; [t] count(children/*); [t] \"gånger\"; [t] count(children/*[1]/children/*); [t] \"matrisen\" (pause:long); [m] children/* (ctxtFunc:CTFnodeCounter, context:\"rad-:\", pause:long, grammar:simpleDet)"],["Action","determinant-simple","[t] \"determinanten av\"; [t] count(children/*); [t] \"gånger\"; [t] count(children/*[1]/children/*); [t] \"matrisen\" (pause:long); [m] children/* (ctxtFunc:CTFnodeCounter, context:\"rad-:\", pause:long)"],["Action","matrix-vector","[t] count(children/*); [t] \"gånger\"; [t] count(children/*[1]/children/*); [t] \"kolumnmatrisen\" (pause:long); [m] children/* (ctxtFunc:CTFnodeCounter, context:\"rad-:\", pause:long, grammar:simpleDet)"],["Action","matrix-vector-simple","[t] count(children/*); [t] \"gånger\"; [t] count(children/*[1]/children/*); [t] \"kolumnmatrisen\" (pause:long); [m] children/* (sepFunc:CTFpauseSeparator, separator:\"short\", pause:long, grammar:simpleDet)"],["Action","matrix-vector-simple-silentcolnum","[t] count(children/*); [t] \"gånger\"; [t] count(children/*[1]/children/*); [t] \"kolumnmatrisen\" (pause:long); [m] children/* (sepFunc:CTFpauseSeparator, separator:\"short\", pause:long, grammar:simpleDet)"],["Action","matrix-row-vector","[t] count(children/*); [t] \"gånger\"; [t] count(children/*[1]/children/*); [t] \"radmatrisen\" (pause:long); [m] children/*[1]/children/* (ctxtFunc:CTFnodeCounter, context:\"kolumn-:\", pause:long, grammar:simpleDet)"],["Action","matrix-row-vector-simple","[t] count(children/*); [t] \"gånger\"; [t] count(children/*[1]/children/*); [t] \"radmatrisen\" (pause:long); [m] children/*[1]/children/* (sepFunc:CTFpauseSeparator, separator:\"short\", pause:long, grammar:simpleDet)"],["Action","matrix-row-vector-simple-silentcolnum","[t] count(children/*); [t] \"gånger\"; [t] count(children/*[1]/children/*); [t] \"radmatrisen\" (pause:long); [m] children/*[1]/children/* (sepFunc:CTFpauseSeparator, separator:\"short\", pause:long, grammar:simpleDet)"],["Action","matrix-row","[m] children/* (ctxtFunc:CTFnodeCounter, context:\"kolumn-,- \", sepFunc:CTFpauseSeparator, separator:\"medium\", pause:long)"],["Action","matrix-end-matrix","[n] . (grammar:EndMatrix); [t] \"slut matris\""],["Action","matrix-end-vector","[n] . (grammar:EndMatrix); [t] \"slut matris\""],["Action","matrix-end-determinant","[n] . (grammar:EndMatrix); [t] \"slut determinant\""],["Action","vector","[t] count(children/*); [t] \"gånger\"; [t] count(children/*[1]/children/*); [t] \"kolumnvektorn\" (pause:long); [m] children/* (ctxtFunc:CTFnodeCounter, context:\"rad-:\", pause:long, grammar:simpleDet)"],["Action","vector-simple","[t] count(children/*); [t] \"gånger\"; [t] count(children/*[1]/children/*); [t] \"kolumnvektorn\" (pause:long); [m] children/* (sepFunc:CTFpauseSeparator, separator:\"short\", pause:long, grammar:simpleDet)"],["Action","row-vector","[t] count(children/*); [t] \"gånger\"; [t] count(children/*[1]/children/*); [t] \"radvektorn\" (pause:long); [m] children/*[1]/children/* (ctxtFunc:CTFnodeCounter, context:\"kolumn-:\", pause:long, grammar:simpleDet)"],["Action","row-vector-simple","[t] count(children/*); [t] \"gånger\"; [t] count(children/*[1]/children/*); [t] \"radvektorn\" (pause:long); [m] children/*[1]/children/* (sepFunc:CTFpauseSeparator, separator:\"short\", pause:long, grammar:simpleDet)"],["Action","vector-end-matrix","[n] . (grammar:EndMatrix); [t] \"slut matris\""],["Action","vector-end-vector","[n] . (grammar:EndMatrix); [t] \"slut vektor\""],["Action","vector-end-vector-endvector","[n] . (grammar:EndMatrix); [t] \"slut vektor\""],["Action","vector-end-determinant","[n] . (grammar:EndMatrix); [t] \"slut determinant\""],["Action","binomial","[n] children/*[1]/children/*[1]; [t] \"över\"; [n] children/*[2]/children/*[1]"],["Action","lines-summary","[p] (pause:short); [t] count(children/*); [t] \"rader\"; [n] . (grammar:layoutSummary)"],["Action","cases-summary","[p] (pause:short); [t] count(children/*); [t] \"fall\"; [n] . 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(grammar:layoutSummary)"],["Action","lines-equations","[p] (pause:short); [m] children/* (ctxtFunc:CTFnodeCounter, context:\"Ekvation-:\", sepFunc:CTFpauseSeparator, separator:\"long\", pause:long)"],["Action","lines-steps-summary","[p] (pause:short); [t] count(children/*); [t] \"steg\"; [n] . (grammar:layoutSummary)"],["Action","lines-steps","[p] (pause:short); [m] children/* (ctxtFunc:CTFnodeCounter, context:\"steg-:\", sepFunc:CTFpauseSeparator, separator:\"long\", pause:long)"],["Action","lines-rows-summary","[p] (pause:short); [t] count(children/*); [t] \"rader\"; [n] . (grammar:layoutSummary)"],["Action","lines-rows","[p] (pause:short); [m] children/* (ctxtFunc:CTFnodeCounter, context:\"rad-:\", sepFunc:CTFpauseSeparator, separator:\"long\", pause:long)"],["Action","lines-constraints-summary","[p] (pause:short); [t] count(children/*); [t] \"begränsning\"; [n] . 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+,
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+{"domain":"mathspeak","locale":"sv","modality":"speech","inherits":"base","rules":[["Ignore","vulgar-fraction"],["Precondition","font-double-struck","default","self::*[@font=\"double-struck\"]","string-length(text())=1","not(contains(@grammar, \"ignoreFont\"))"],["Precondition","german-font","default","self::*[@font=\"fraktur\"]","not(contains(@grammar, \"ignoreFont\"))"],["Precondition","german-font-bold","default","self::*[@font=\"bold-fraktur\"]","not(contains(@grammar, \"ignoreFont\"))"]]}
+,
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+{"domain":"mathspeak","locale":"sv","modality":"speech","kind":"actions","rules":[["Action","collapsed","[t] \"kollapsad\"; [n] . (engine:modality=summary, grammar:collapsed)"],["Action","blank-cell-empty","[t] \"Tom\""],["Action","blank-line-empty","[t] \"Tom\""],["Action","font","[t] @font (grammar:localFont); [n] . (grammar:ignoreFont=@font)"],["Action","font-double-struck","[n] . (grammar:ignoreFont=@font); [t] @font (grammar:localFont)"],["Action","mixed-number","[n] children/*[1]; [t] \"och\"; [n] children/*[2]"],["Action","number-with-chars","[t] \"Nummer\"; [m] CQFspaceoutNumber (grammar:protected)"],["Action","number-with-chars-brief","[t] \"Num\"; [m] CQFspaceoutNumber (grammar:protected)"],["Action","number-as-upper-word","[t] \"StortOrd\"; [t] CSFspaceoutText"],["Action","number-baseline","[t] \"Basnivå\"; [n] . (grammar:baseline)"],["Action","number-baseline-brief","[t] \"Bas\"; [n] . 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+,
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+{"modality":"prefix","domain":"default","locale":"sv","inherits":"base","rules":[]}
+,
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+{"modality":"prefix","domain":"default","locale":"sv","kind":"actions","rules":[["Action","numerator","[t] \"Täljare\" (pause:200)"],["Action","denominator","[t] \"Nämnare\" (pause:200)"],["Action","base","[t] \"Bas\" (pause:200)"],["Action","exponent","[t] \"Exponent\" (pause:200)"],["Action","subscript","[t] \"Index\" (pause:200)"],["Action","overscript","[t] \"Överskrift\" (pause:200)"],["Action","underscript","[t] \"Underskrift\" (pause:200)"],["Action","radicand","[t] \"Radikand\" (pause:200)"],["Action","index","[t] \"Index\" (pause:200)"],["Action","leftsub","[t] \"Vänster Index\" (pause:200)"],["Action","leftsub-counted","[t] CSFordinalPosition; [t] \"Vänstra Indexet\" (pause:200)"],["Action","leftsuper","[t] \"Vänster Exponent\" (pause:200)"],["Action","leftsuper-counted","[t] CSFordinalPosition; [t] \"Vänstra Exponenten\" (pause:200)"],["Action","rightsub","[t] \"Höger Index\" (pause:200)"],["Action","rightsub-counted","[t] CSFordinalPosition; [t] \"Högra Indexet\" (pause:200)"],["Action","rightsuper","[t] \"Höger Exponent\" (pause:200)"],["Action","rightsuper-counted","[t] CSFordinalPosition; [t] \"Högra Exponenten\" (pause:200)"],["Action","choice","[t] \"Valmängd\" (pause:200)"],["Action","select","[t] \"Urvalsmängd\" (pause:200)"],["Action","row","[t] CSFordinalPosition; [t] \"Raden\" (pause:200)"],["Action","cell","[n] ../..; [t] CSFordinalPosition; [t] \"Kolumnen\" (pause:200)"],["Action","cell-simple","[t] CSFordinalPosition; [t] \"Kolumn\" (pause:200)"]]}
+,
+"sv/rules/summary_swedish.min": 
+{"locale":"sv","modality":"summary","inherits":"base","rules":[]}
+,
+"sv/rules/summary_swedish_actions.min": 
+{"locale":"sv","modality":"summary","kind":"actions","rules":[["Action","abstr-identifier-long","[t] \"lång identifierare\""],["Action","abstr-identifier","[t] \"identifierare\""],["Action","abstr-number-long","[t] \"långt nummer\""],["Action","abstr-number","[t] \"nummer\""],["Action","abstr-mixed-number-long","[t] \"långt blandat nummer\""],["Action","abstr-mixed-number","[t] \"blandat nummer\""],["Action","abstr-text","[t] \"text\""],["Action","abstr-function","[t] \"funktionsuttryck\""],["Action","abstr-function-brief","[t] \"funktion\""],["Action","abstr-lim","[t] \"gränsfunktion\""],["Action","abstr-lim-brief","[t] \"gränsfunktion\""],["Action","abstr-fraction","[t] \"bråk\""],["Action","abstr-fraction-brief","[t] \"bråk\""],["Action","abstr-continued-fraction","[t] \"förlängt bråk\""],["Action","abstr-continued-fraction-brief","[t] \"förlängt bråk\""],["Action","abstr-sqrt","[t] \"kvadratrot\""],["Action","abstr-sqrt-nested","[t] \"nästlad kvadratrot\""],["Action","abstr-root-end","[t] \"rot med index\"; [n] children/*[1] (engine:modality=speech); [t] \"slutindex\""],["Action","abstr-root","[t] \"rot med index\"; [n] children/*[1] (engine:modality=speech)"],["Action","abstr-root-brief","[t] \"rot\""],["Action","abstr-root-nested-end","[t] \"nästlad rot med index\"; [n] children/*[1] (engine:modality=speech); [t] \"slutindex\""],["Action","abstr-root-nested","[t] \"nästlad rot med index\"; [n] children/*[1] (engine:modality=speech)"],["Action","abstr-root-nested-brief","[t] \"nästlat rot\""],["Action","abstr-superscript","[t] \"potens\""],["Action","abstr-subscript","[t] \"uttryck med index\""],["Action","abstr-subsup","[t] \"potens med index\""],["Action","abstr-infixop","[t] @role (grammar:localRole); [t] \"med\"; [t] count(./children/*); [t] \"element\""],["Action","abstr-infixop-var","[t] @role (grammar:localRole); [t] \"med varierande antal element\""],["Action","abstr-infixop-brief","[t] @role (grammar:localRole)"],["Action","abstr-addition","[t] \"Summa med\"; [t] count(./children/*); [t] \"summander\""],["Action","abstr-addition-brief","[t] \"summa\""],["Action","abstr-addition-var","[t] \"summa med varierande antal summander\""],["Action","abstr-multiplication","[t] \"produkt med\"; [t] count(./children/*); [t] \"faktorer\""],["Action","abstr-multiplication-brief","[t] \"produkt\""],["Action","abstr-multiplication-var","[t] \"produkt med varierande antal faktorer\""],["Action","abstr-vector","[t] count(./children/*) (join:\"-\"); [t] \"dimensionell vektor\""],["Action","abstr-vector-brief","[t] \"vektor\""],["Action","abstr-vector-var","[t] \"n-dimensionell vektor\""],["Action","abstr-binomial","[t] \"binom\""],["Action","abstr-determinant","[t] count(./children/*) (join:\"-\"); [t] \"dimensionell determinant\""],["Action","abstr-determinant-brief","[t] \"determinant\""],["Action","abstr-determinant-var","[t] \"n-dimensionell determinant\""],["Action","abstr-squarematrix","[t] count(./children/*) (join:\"-\"); [t] \"dimensionell kvadratisk matris\""],["Action","abstr-squarematrix-brief","[t] \"kvadratisk matris\""],["Action","abstr-rowvector","[t] count(./children/row/children/*) (join:\"-\"); [t] \"dimensionell radvektor\""],["Action","abstr-rowvector-brief","[t] \"radvektor\""],["Action","abstr-rowvector-var","[t] \"n-dimensionell radvektor\""],["Action","abstr-matrix","[t] count(children/*); [t] \"gånger\"; [t] count(children/*[1]/children/*); [t] \"matris\""],["Action","abstr-matrix-brief","[t] \"matris\""],["Action","abstr-matrix-var","[t] \"n gånger m-dimensionell matris\""],["Action","abstr-cases","[t] \"case-sats\"; [t] \"med\"; [t] count(children/*); [t] \"case\""],["Action","abstr-cases-brief","[t] \"case-sats\""],["Action","abstr-cases-var","[t] \"case-sats med varierande antal case\""],["Action","abstr-punctuated","[n] content/*[1] (join:\"\"); [t] \"separerad lista\"; [t] \"med längden\"; [t] count(children/*) - count(content/*)"],["Action","abstr-punctuated-brief","[n] content/*[1] (join:\"\"); [t] \"separerad lista\""],["Action","abstr-punctuated-var","[n] content/*[1] (join:\"\"); [t] \"separerad lista\"; [t] \"med varierande längd\""],["Action","abstr-bigop","[n] content/*[1]"],["Action","abstr-integral","[t] \"integral\""],["Action","abstr-relation","[t] @role (grammar:localRole)"],["Action","abstr-relation-seq","[t] @role (join:\"\", grammar:localRole); [t] \"sekvens\"; [t] \"med\"; [t] count(./children/*); [t] \"element\""],["Action","abstr-relation-seq-brief","[t] @role (join:\"\", grammar:localRole); [t] \"sekvens\""],["Action","abstr-relation-var","[t] @role (join:\"\", grammar:localRole); [t] \"sekvens\"; [t] \"med varierande antal element\""],["Action","abstr-multirel","[t] \"sambandssekvens\"; [t] \"med\"; [t] count(./children/*); [t] \"element\""],["Action","abstr-multirel-brief","[t] \"sambandssekvens\""],["Action","abstr-multirel-var","[t] \"sambandssekvens med varierande antal element\""],["Action","abstr-table","[t] \"tabell med\"; [t] count(children/*); [t] \"rader och\"; [t] count(children/*[1]/children/*); [t] \"kolumner\""],["Action","abstr-line","[t] \"i\"; [t] @role (grammar:localRole)"],["Action","abstr-row","[t] \"i\"; [t] @role (grammar:localRole); [t] count(preceding-sibling::..); [t] \"med\"; [t] count(children/*); [t] \"kolumner\""],["Action","abstr-cell","[t] \"i\"; [t] @role (grammar:localRole)"]]}
+}
+
diff --git a/devel/_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css b/devel/_static/sphinx_immaterial_theme.af531f03affe68837.min.css
similarity index 99%
rename from devel/_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css
rename to devel/_static/sphinx_immaterial_theme.af531f03affe68837.min.css
index 1d0a46e53c..abac09765b 100644
--- a/devel/_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css
+++ b/devel/_static/sphinx_immaterial_theme.af531f03affe68837.min.css
@@ -1,4 +1,4 @@
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diff --git a/devel/_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js b/devel/_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js
similarity index 72%
rename from devel/_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js
rename to devel/_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js
index ec77f5d06f..8334adf70b 100644
--- a/devel/_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js
+++ b/devel/_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js
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ki({alert$:e}){cn.default.isSupported()&&new U(t=>{new cn.default("[data-clipboard-target], [data-clipboard-text]",{text:r=>r.getAttribute("data-clipboard-text")||Vs(B(r.getAttribute("data-clipboard-target")))}).on("success",r=>t.next(r))}).pipe(M(t=>{t.trigger.focus()}),d(()=>ae("clipboard.copied"))).subscribe(e)}function Ns(e){if(e.length<2)return[""];let[t,r]=[...e].sort((o,i)=>o.length-i.length).map(o=>o.replace(/[^/]+$/,"")),n=0;if(t===r)n=t.length;else for(;t.charCodeAt(n)===r.charCodeAt(n);)n++;return e.map(o=>o.replace(t.slice(0,n),""))}function hr(e){let t=__md_get("__sitemap",sessionStorage,e);if(t)return C(t);{let r=ce();return zo(new URL("sitemap.xml",e||r.base)).pipe(d(n=>Ns(D("loc",n).map(o=>o.textContent))),le(()=>k),De([]),M(n=>__md_set("__sitemap",n,sessionStorage,e)))}}function Pi({document$:e,location$:t,viewport$:r}){let n=ce();if(location.protocol==="file:")return;"scrollRestoration"in 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Set,u=new Set;for(let l of D("script",document))l.src?f.add(new URL(l.src,document.baseURI).toString()):u.add(l.outerHTML);e.pipe(Pe(1),mt(l=>ze(this,null,function*(){var p;for(let m of["title","link[rel=canonical]","meta[name=author]","meta[name=description]","[data-md-component=announce]","[data-md-component=container]","[data-md-component=header-topic]","[data-md-component=outdated]","[data-md-component=logo]","[data-md-component=skip]",...Z("navigation.tabs.sticky")?["[data-md-component=tabs]"]:[]]){let h=ie(m),b=ie(m,l);typeof h!="undefined"&&typeof b!="undefined"&&h.replaceWith(b)}((p=window.MathJax)==null?void 0:p.typesetPromise)!==void 0&&(yield window.MathJax.typesetPromise());for(let m of D("script",l))if(m.src){let h=new URL(m.src,document.baseURI).toString();if(!f.has(h)){let b=document.createElement("script");for(let q of m.getAttributeNames())b.setAttribute(q,m.getAttribute(q));let w;b.src=h,b.async||(w=new 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$i=Je(rn());function Ii(e,t){let r=new RegExp(e.separator,"img"),n=(o,i,a)=>`${i}<mark data-md-highlight>${a}</mark>`;return o=>{o=o.replace(/[\s*+\-:~^]+/g," ").trim();let i=new RegExp(`(^|${e.separator})(${o.replace(/[|\\{}()[\]^$+*?.-]/g,"\\$&").replace(r,"|")})`,"img");return a=>(t?(0,$i.default)(a):a).replace(i,n).replace(/<\/mark>(\s+)<mark[^>]*>/img,"$1")}}function ji({document$:e}){var a;let t=ce(),r=t.version.staticVersions?C(t.version.staticVersions):Ge(new URL((a=t.version.versionPath)!=null?a:"../versions.json",t.base)),n=new URL("..",t.base),o=c=>new URL(c,n).toString().replace(/\/*$/,""),i=r.pipe(d(c=>{let s=t.base.toString().replace(/\/*$/,"");return c.find(({version:f,aliases:u})=>o(f)===s||u.find(l=>o(l)===s))||c[0]}));r.pipe(d(c=>new Map(c.map(s=>[`${new URL(`../${s.version}/`,t.base)}`,s]))),_(c=>x(document.body,"click").pipe(A(s=>!s.metaKey&&!s.ctrlKey),me(i),_(([s,f])=>{if(s.target instanceof Element){let u=s.target.closest("a");if(u&&!u.target&&c.has(u.href)){let l=u.href;return!s.target.closest(".md-version")&&c.get(l)===f?k:(s.preventDefault(),C(l))}}return k}),_(s=>{let{version:f}=c.get(s);return hr(new URL(s)).pipe(d(u=>{let p=Oe().href.replace(t.base,"");return u.includes(p.split("#")[0])?new URL(`../${f}/${p}`,t.base):new URL(s)}))})))).subscribe(c=>lr(c)),G([r,i]).subscribe(([c,s])=>{B(".md-header__topic").appendChild(oi(c,s))}),e.pipe(_(()=>i)).subscribe(c=>{var f;let s=__md_get("__outdated",sessionStorage);if(s===null){let u=((f=t.version)==null?void 0:f.default)||"latest";s=!c.aliases.includes(u),__md_set("__outdated",s,sessionStorage)}if(s)for(let u of ee("outdated"))u.hidden=!1})}function Fi(e,{location$:t}){let r={lang:[],separator:"\\s+"};return G([t.pipe(z(Oe()),A(n=>!!n.searchParams.get("h")))]).pipe(d(([n])=>Ii(r,!0)(n.searchParams.get("h"))),d(n=>{var a;let o=new Map,i=document.createNodeIterator(e,NodeFilter.SHOW_TEXT);for(let c=i.nextNode();c;c=i.nextNode())if((a=c.parentElement)!=null&&a.offsetHeight){let s=c.textContent,f=n(s);f.length>s.length&&o.set(c,f)}for(let[c,s]of o){let{childNodes:f}=O("span",null,s);c.replaceWith(...Array.from(f))}return{ref:e,nodes:o}}))}function Ds(e,{viewport$:t,main$:r}){let n=e.parentElement,o=n.offsetTop-n.parentElement.offsetTop;return G([r,t]).pipe(d(([{offset:i,height:a},{offset:{y:c}}])=>(a=a+Math.min(o,Math.max(0,c-i))-o,{height:a,locked:c>=i+o})),Q((i,a)=>i.height===a.height&&i.locked===a.locked))}function fn(e,n){var o=n,{header$:t}=o,r=hn(o,["header$"]);let i=B(".md-sidebar__scrollwrap",e),{y:a}=Be(i);return I(()=>{let c=new L;return c.pipe(Re(0,fe),me(t)).subscribe({next([{height:s},{height:f}]){i.style.height=`${s-2*a}px`,e.style.top=`${f}px`},complete(){i.style.height="",e.style.top=""}}),c.pipe(Ce(fe),ve(1)).subscribe(()=>{for(let s of D(".md-nav__link--active[href]",e)){let f=ko(s);if(typeof f!="undefined"){let 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zs(e){return Ws||(Ws=I(()=>{let t=__md_get("__source",sessionStorage);if(t)return C(t);if(ee("consent").length){let n=__md_get("__consent");if(!(n&&n.github))return k}return Ni(e.href).pipe(M(n=>__md_set("__source",n,sessionStorage)))}).pipe(le(()=>k),A(t=>Object.keys(t).length>0),d(t=>({facts:t})),X(1)))}function Di(e){let t=B(":scope > :last-child",e);return I(()=>{let r=new L;return r.subscribe(({facts:n})=>{t.appendChild(ri(n)),t.classList.add("md-source__repository--active")}),zs(e).pipe(M(n=>r.next(n)),R(()=>r.complete()),d(n=>V({ref:e},n)))})}function qs(e,{viewport$:t,header$:r}){return de(document.body).pipe(_(()=>mr(e,{header$:r,viewport$:t})),d(({offset:{y:n}})=>({hidden:n>=10})),J("hidden"))}function Wi(e,t){return I(()=>{let r=new L;return r.subscribe({next({hidden:n}){e.hidden=n},complete(){e.hidden=!1}}),(Z("navigation.tabs.sticky")?C({hidden:!1}):qs(e,t)).pipe(M(n=>r.next(n)),R(()=>r.complete()),d(n=>V({ref:e},n)))})}function 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s=!o||Z("toc.integrate");i.pipe(He(1)).subscribe(({prev:f})=>{var m;let u;if(f.length===0&&s&&(u=(m=e.querySelector("a[href='#']"))!=null?m:e),s=!1,f.length!==0&&(u=f[f.length-1][0]),u===void 0||!u.offsetHeight)return;let l=u.parentElement,p=5;for(;l!==null&&l.scrollHeight-p<=l.clientHeight;)l=l.parentElement;if(l!==null&&l!==document.body&&l!==document.documentElement){let h=u.getBoundingClientRect(),b=l.getBoundingClientRect();l.scrollTo({top:l.scrollTop+(h.y-b.height/2-b.y)})}})}o&&Z("navigation.tracking")&&t.pipe(te(i.pipe(ue(1))),J("offset"),He(250),Pe(1),te(n.pipe(Pe(1))),Ot({delay:250}),me(i)).subscribe(([,{prev:s}])=>{let f=Oe(),u=s[s.length-1];if(u&&u.length){let[l]=u,{hash:p}=new URL(l.href);f.hash!==p&&(f.hash=p,history.replaceState({},"",`${f}`))}else f.hash="",history.replaceState({},"",`${f}`)}),Z("toc.sticky")&&de(document.body).pipe(J("width"),He(0)).subscribe(()=>{let s=new Map,f="--md-nav__header-height";for(let u of D(".md-nav__link",e)){let 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0&&(f=u.score),s.push({location:n+u.anchor,score:u.score===f?e.score:0,terms:u.terms,title:r(u.title||o),text:r(u.snippet)});return s})}function Fs(e){return new DOMParser().parseFromString(e,"text/html").body.textContent||""}function Mi(e){return ze(this,null,function*(){yield Cs();let t=new Stemmer,r=[],n=[],o=[],i=[];for(let u of ks(e)){let l=!1;u[0]==="-"&&(l=!0,u=u.substr(1));let p=u.toLowerCase();if(p.length===0)continue;i.push(p);let m=!1;for(let h of p.matchAll(/\w+/g)){let b=h[0];if(stopwords.indexOf(b)!==-1)continue;let v=t.stemWord(b);v.length<3&&b.length>=3&&(v=b);let V;l?V=n:(V=r,m=!0),V.indexOf(v)===-1&&V.push(v)}!l&&m&&o.push(p)}let a=[];for(let u=0;u<i.length;u++){let l=[...i.slice(0,u),...i.slice(u+1,i.length)];a.push(...Hs(i[u],l))}a.push(...Ps(r,n)),a.sort((u,l)=>{let p=u.score,m=l.score;if(p!==m)return m-p;let h=u.title.toLowerCase(),b=l.title.toLowerCase();return h>b?1:h<b?-1:0}),Rs&&console.log(a);let c=new RegExp(`\\b(?:${o.map(sn).join("|")})`,"img"),s=u=>`<mark 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1:r.textContent=ae("search.result.one");break;default:r.textContent=ae("search.result.other",ur(l.count))}u(l)}else r.textContent=ae("search.result.placeholder")}),C()}function Us(e,{query$:t}){return t.pipe(d(({value:r})=>{let n=Oe();return n.hash="",n.searchParams.delete("h"),n.searchParams.set("q",r),{url:n}}))}function Ci(e,t){let r=new L;return r.subscribe(({url:n})=>{e.setAttribute("data-clipboard-text",e.href),e.href=`${n}`}),w(e,"click").subscribe(n=>n.preventDefault()),Us(e,t).pipe(M(n=>r.next(n)),R(()=>r.complete()),d(n=>N({ref:e},n)))}function Ri(e,{keyboard$:t}){let r=_e("search-query");return t.pipe(A(({mode:n})=>n==="search")).subscribe(n=>{switch(n.type){case"ArrowRight":e.innerText.length&&r.selectionStart===r.value.length&&(r.value=e.innerText);break}}),C()}function Hi(e,{keyboard$:t}){try{let r=_e("search-query",e),n=_e("search-result",e);t.pipe(A(({mode:a})=>a==="search")).subscribe(a=>{let c=Fe();switch(a.type){case"Enter":if(c===r){let s=new Map;for(let f of W(":first-child [href]",n)){let u=f.firstElementChild;s.set(f,parseFloat(u.getAttribute("data-md-score")))}if(s.size){let[[f]]=[...s].sort(([,u],[,l])=>l-u);f.click()}a.claim()}break;case"Escape":case"Tab":Ye("search",!1),r.blur();break;case"ArrowUp":case"ArrowDown":if(typeof c=="undefined")r.focus();else{let s=[r,...W(":not(details) > [href], summary, details[open] [href]",n)],f=Math.max(0,(Math.max(0,s.indexOf(c))+s.length+(a.type==="ArrowUp"?-1:1))%s.length);s[f].focus()}a.claim();break;default:r!==Fe()&&r.focus()}}),t.pipe(A(({mode:a})=>a==="global")).subscribe(a=>{switch(a.type){case"f":case"s":case"/":r.focus(),r.select(),a.claim();break}});let o=_i(r),i=Ai(n,{query$:o});return P(o,i).pipe(tt(...ee("search-share",e).map(a=>Ci(a,{query$:o})),...ee("search-suggest",e).map(a=>Ri(a,{keyboard$:t}))))}catch(r){return e.hidden=!0,je}}var cn=Je(Qr());function Vs(e){e.setAttribute("data-md-copying","");let t=e.innerText;return e.removeAttribute("data-md-copying"),t}function ki({alert$:e}){cn.default.isSupported()&&new U(t=>{new cn.default("[data-clipboard-target], [data-clipboard-text]",{text:r=>r.getAttribute("data-clipboard-text")||Vs(B(r.getAttribute("data-clipboard-target")))}).on("success",r=>t.next(r))}).pipe(M(t=>{t.trigger.focus()}),d(()=>ae("clipboard.copied"))).subscribe(e)}function Ns(e){if(e.length<2)return[""];let[t,r]=[...e].sort((o,i)=>o.length-i.length).map(o=>o.replace(/[^/]+$/,"")),n=0;if(t===r)n=t.length;else for(;t.charCodeAt(n)===r.charCodeAt(n);)n++;return e.map(o=>o.replace(t.slice(0,n),""))}function hr(e){let t=__md_get("__sitemap",sessionStorage,e);if(t)return C(t);{let r=ce();return zo(new URL("sitemap.xml",e||r.base)).pipe(d(n=>Ns(W("loc",n).map(o=>o.textContent))),le(()=>k),De([]),M(n=>__md_set("__sitemap",n,sessionStorage,e)))}}function Pi({document$:e,location$:t,viewport$:r}){let n=ce();if(location.protocol==="file:")return;"scrollRestoration"in history&&(history.scrollRestoration="manual",w(window,"beforeunload").subscribe(()=>{history.scrollRestoration="auto"}));let o=ie("link[rel=icon]");typeof o!="undefined"&&(o.href=o.href);let i=hr().pipe(d(l=>l.map(p=>`${new URL(p,n.base)}`)),_(l=>w(document.body,"click").pipe(A(p=>!p.metaKey&&!p.ctrlKey),_(p=>{if(p.target instanceof Element){let m=p.target.closest("a");if(m&&!m.target){let h=new URL(m.href);if(h.search="",h.hash="",h.pathname!==location.pathname&&l.includes(h.toString()))return p.preventDefault(),C({url:new URL(m.href)})}}return je}))),pe()),a=w(window,"popstate").pipe(A(l=>l.state!==null),d(l=>({url:new URL(location.href),offset:l.state})),pe());P(i,a).pipe(Q((l,p)=>l.url.href===p.url.href),d(({url:l})=>l)).subscribe(t);let c=t.pipe(J("pathname"),_(l=>pr(l.href).pipe(le(()=>(lr(l),je)))),pe());i.pipe(rr(c)).subscribe(({url:l})=>{history.pushState({},"",`${l}`)});let s=new DOMParser;c.pipe(_(l=>l.text()),d(l=>s.parseFromString(l,"text/html"))).subscribe(e);let f=new Set,u=new Set;for(let l of W("script",document))l.src?f.add(new URL(l.src,document.baseURI).toString()):u.add(l.outerHTML);e.pipe(Pe(1),mt(l=>ze(this,null,function*(){var p;for(let m of["title","link[rel=canonical]","meta[name=author]","meta[name=description]","[data-md-component=announce]","[data-md-component=container]","[data-md-component=header-topic]","[data-md-component=outdated]","[data-md-component=logo]","[data-md-component=skip]",...Z("navigation.tabs.sticky")?["[data-md-component=tabs]"]:[]]){let h=ie(m),b=ie(m,l);typeof h!="undefined"&&typeof b!="undefined"&&h.replaceWith(b)}((p=window.MathJax)==null?void 0:p.typesetPromise)!==void 0&&(yield window.MathJax.typesetPromise());for(let m of W("script",l))if(m.src){let h=new URL(m.src,document.baseURI).toString();if(!f.has(h)){let b=document.createElement("script");for(let V of m.getAttributeNames())b.setAttribute(V,m.getAttribute(V));let v;b.src=h,b.async||(v=new 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$i=Je(rn());function Ii(e,t){let r=new RegExp(e.separator,"img"),n=(o,i,a)=>`${i}<mark data-md-highlight>${a}</mark>`;return o=>{o=o.replace(/[\s*+\-:~^]+/g," ").trim();let i=new RegExp(`(^|${e.separator})(${o.replace(/[|\\{}()[\]^$+*?.-]/g,"\\$&").replace(r,"|")})`,"img");return a=>(t?(0,$i.default)(a):a).replace(i,n).replace(/<\/mark>(\s+)<mark[^>]*>/img,"$1")}}function ji({document$:e}){var a;let t=ce(),r=t.version.staticVersions?C(t.version.staticVersions):Ge(new URL((a=t.version.versionPath)!=null?a:"../versions.json",t.base)),n=new URL("..",t.base),o=c=>new URL(c,n).toString().replace(/\/*$/,""),i=r.pipe(d(c=>{let s=t.base.toString().replace(/\/*$/,"");return c.find(({version:f,aliases:u})=>o(f)===s||u.find(l=>o(l)===s))||c[0]}));r.pipe(d(c=>new Map(c.map(s=>[`${new URL(`../${s.version}/`,t.base)}`,s]))),_(c=>w(document.body,"click").pipe(A(s=>!s.metaKey&&!s.ctrlKey),me(i),_(([s,f])=>{if(s.target instanceof Element){let u=s.target.closest("a");if(u&&!u.target&&c.has(u.href)){let l=u.href;return!s.target.closest(".md-version")&&c.get(l)===f?k:(s.preventDefault(),C(l))}}return k}),_(s=>{let{version:f}=c.get(s);return hr(new URL(s)).pipe(d(u=>{let p=Oe().href.replace(t.base,"");return u.includes(p.split("#")[0])?new URL(`../${f}/${p}`,t.base):new URL(s)}))})))).subscribe(c=>lr(c)),G([r,i]).subscribe(([c,s])=>{B(".md-header__topic").appendChild(oi(c,s))}),e.pipe(_(()=>i)).subscribe(c=>{var f;let s=__md_get("__outdated",sessionStorage);if(s===null){let u=((f=t.version)==null?void 0:f.default)||"latest";s=!c.aliases.includes(u),__md_set("__outdated",s,sessionStorage)}if(s)for(let u of ee("outdated"))u.hidden=!1})}function Fi(e,{location$:t}){let r={lang:[],separator:"\\s+"};return G([t.pipe(q(Oe()),A(n=>!!n.searchParams.get("h")))]).pipe(d(([n])=>Ii(r,!0)(n.searchParams.get("h"))),d(n=>{var a;let o=new Map,i=document.createNodeIterator(e,NodeFilter.SHOW_TEXT);for(let c=i.nextNode();c;c=i.nextNode())if((a=c.parentElement)!=null&&a.offsetHeight){let s=c.textContent,f=n(s);f.length>s.length&&o.set(c,f)}for(let[c,s]of o){let{childNodes:f}=O("span",null,s);c.replaceWith(...Array.from(f))}return{ref:e,nodes:o}}))}function Ds(e,{viewport$:t,main$:r}){let n=e.parentElement,o=n.offsetTop-n.parentElement.offsetTop;return G([r,t]).pipe(d(([{offset:i,height:a},{offset:{y:c}}])=>(a=a+Math.min(o,Math.max(0,c-i))-o,{height:a,locked:c>=i+o})),Q((i,a)=>i.height===a.height&&i.locked===a.locked))}function fn(e,n){var o=n,{header$:t}=o,r=hn(o,["header$"]);let i=B(".md-sidebar__scrollwrap",e),{y:a}=Be(i);return I(()=>{let c=new L;return c.pipe(Re(0,fe),me(t)).subscribe({next([{height:s},{height:f}]){i.style.height=`${s-2*a}px`,e.style.top=`${f}px`},complete(){i.style.height="",e.style.top=""}}),c.pipe(Ce(fe),ve(1)).subscribe(()=>{for(let s of W(".md-nav__link--active[href]",e)){let f=ko(s);if(typeof f!="undefined"){let u=s.offsetTop-f.offsetTop,{height:l}=Se(f);f.scrollTo({top:u-l/2})}}}),Ds(e,r).pipe(M(s=>c.next(s)),R(()=>c.complete()),d(s=>N({ref:e},s)))})}function Ui(e,t){if(typeof t!="undefined"){let r=`https://api.github.com/repos/${e}/${t}`;return Ir(Ge(`${r}/releases/latest`).pipe(le(()=>k),d(n=>({version:n.tag_name})),De({})),Ge(r).pipe(le(()=>k),d(n=>({stars:n.stargazers_count,forks:n.forks_count})),De({}))).pipe(d(([n,o])=>N(N({},n),o)))}else{let r=`https://api.github.com/users/${e}`;return Ge(r).pipe(d(n=>({repositories:n.public_repos})),De({}))}}function Vi(e,t){let r=`https://${e}/api/v4/projects/${encodeURIComponent(t)}`;return Ge(r).pipe(le(()=>k),d(({star_count:n,forks_count:o})=>({stars:n,forks:o})),De({}))}function Ni(e){let[t]=e.match(/(git(?:hub|lab))/i)||[];switch(t.toLowerCase()){case"github":let[,r,n]=e.match(/^.+github\.com\/([^/]+)\/?([^/]+)?/i);return Ui(r,n);case"gitlab":let[,o,i]=e.match(/^.+?([^/]*gitlab[^/]+)\/(.+?)\/?$/i);return Vi(o,i);default:return k}}var Ws;function zs(e){return Ws||(Ws=I(()=>{let t=__md_get("__source",sessionStorage);if(t)return C(t);if(ee("consent").length){let n=__md_get("__consent");if(!(n&&n.github))return k}return Ni(e.href).pipe(M(n=>__md_set("__source",n,sessionStorage)))}).pipe(le(()=>k),A(t=>Object.keys(t).length>0),d(t=>({facts:t})),X(1)))}function Di(e){let t=B(":scope > :last-child",e);return I(()=>{let r=new L;return r.subscribe(({facts:n})=>{t.appendChild(ri(n)),t.classList.add("md-source__repository--active")}),zs(e).pipe(M(n=>r.next(n)),R(()=>r.complete()),d(n=>N({ref:e},n)))})}function qs(e,{viewport$:t,header$:r}){return de(document.body).pipe(_(()=>mr(e,{header$:r,viewport$:t})),d(({offset:{y:n}})=>({hidden:n>=10})),J("hidden"))}function Wi(e,t){return I(()=>{let r=new L;return r.subscribe({next({hidden:n}){e.hidden=n},complete(){e.hidden=!1}}),(Z("navigation.tabs.sticky")?C({hidden:!1}):qs(e,t)).pipe(M(n=>r.next(n)),R(()=>r.complete()),d(n=>N({ref:e},n)))})}function Ks(e,{viewport$:t,header$:r,excludedLinks:n}){let o=new Map,i=W("a[href]",e);for(let s of i){if(n!=null&&n.has(s))continue;let f=s.getAttribute("href"),u;if(f.startsWith("#")){let l=decodeURIComponent(s.hash.substring(1));u=ie(`[id="${l}"]`)}else u=ie(`a.pseudo-toc-entry[href=${CSS.escape(f)}]`);if(typeof u!="undefined"){let l=s.closest(".md-nav__link");l!==null&&o.set(l,u)}}let a=r.pipe(J("height"),d(({height:s})=>{let f=_e("main"),u=B(":scope > :first-child",f);return s+.8*(u.offsetTop-f.offsetTop)}),pe());return de(document.body).pipe(J("height"),_(s=>I(()=>{let f=[];return C([...o].reduce((u,[l,p])=>{for(;f.length&&o.get(f[f.length-1]).tagName>=p.tagName;)f.pop();let m=p.offsetTop;for(;!m&&p.parentElement;)p=p.parentElement,m=p.offsetTop;return u.set([...f=[...f,l]].reverse(),m)},new Map))}).pipe(d(f=>new Map([...f].sort(([,u],[,l])=>u-l))),St(a),_(([f,u])=>t.pipe(Nr(([l,p],{offset:{y:m},size:h})=>{let b=m+h.height>=Math.floor(s.height);for(;p.length;){let[,v]=p[0];if(v-u<m||b)l=[...l,p.shift()];else break}for(;l.length;){let[,v]=l[l.length-1];if(v-u>=m&&!b)p=[l.pop(),...p];else break}return[l,p]},[[],[...f]]),Q((l,p)=>l[0]===p[0]&&l[1]===p[1])))))).pipe(d(([s,f])=>({prev:s.map(([u])=>u),next:f.map(([u])=>u)})),q({prev:[],next:[]}),Ne(2,1),d(([s,f])=>s.prev.length<f.prev.length?{prev:f.prev.slice(Math.max(0,s.prev.length-1),f.prev.length),next:[]}:{prev:f.prev.slice(-1),next:f.next.slice(0,f.next.length-s.next.length)}))}function ln(e,{viewport$:t,header$:r,target$:n,localToc:o}){return I(()=>{let i=new L,a=o?"md-nav__link--active":"md-nav__link--in-viewport";if(i.subscribe(({prev:s,next:f})=>{for(let[u]of f)u.classList.remove("md-nav__link--passed"),u.classList.remove(a);for(let[u,[l]]of s.entries())l.classList.add("md-nav__link--passed"),l.classList.toggle(a,u===s.length-1)}),Z("toc.follow")&&(o||!Z("toc.integrate"))){let s=!o||Z("toc.integrate");i.pipe(He(1)).subscribe(({prev:f})=>{var m;let u;if(f.length===0&&s&&(u=(m=e.querySelector("a[href='#']"))!=null?m:e),s=!1,f.length!==0&&(u=f[f.length-1][0]),u===void 0||!u.offsetHeight)return;let l=u.parentElement,p=5;for(;l!==null&&l.scrollHeight-p<=l.clientHeight;)l=l.parentElement;if(l!==null&&l!==document.body&&l!==document.documentElement){let h=u.getBoundingClientRect(),b=l.getBoundingClientRect();l.scrollTo({top:l.scrollTop+(h.y-b.height/2-b.y)})}})}o&&Z("navigation.tracking")&&t.pipe(te(i.pipe(ue(1))),J("offset"),He(250),Pe(1),te(n.pipe(Pe(1))),Ot({delay:250}),me(i)).subscribe(([,{prev:s}])=>{let f=Oe(),u=s[s.length-1];if(u&&u.length){let[l]=u,{hash:p}=new URL(l.href);f.hash!==p&&(f.hash=p,history.replaceState({},"",`${f}`))}else f.hash="",history.replaceState({},"",`${f}`)}),Z("toc.sticky")&&de(document.body).pipe(J("width"),He(0)).subscribe(()=>{let s=new Map,f="--md-nav__header-height";for(let u of W(".md-nav__link",e)){let l=u.nextElementSibling;if(!(l instanceof HTMLElement)||l.tagName!=="NAV")continue;let p="",m=NaN,h=l.parentElement.closest("nav");if(h!==null){let b=s.get(h);b!==void 0&&(p=`${b.height} + `,m=b.zindex-1)}isNaN(m)&&(m=100),p+=`${u.offsetHeight}px + 0.625em`,u.classList.add("md-nav__sticky"),u.style.setProperty("--md-nav__sticky-zindex",m.toString()),l.style.setProperty(f,`calc(${p})`),s.set(l,{height:p,zindex:m})}});let c=o?void 0:new Set(W("[data-md-component='toc'] a[href]",e));return Ks(e,{viewport$:t,header$:r,excludedLinks:c}).pipe(M(s=>i.next(s)),R(()=>i.complete()),d(s=>N({ref:e},s)))})}function Bs(e,{viewport$:t,main$:r,target$:n}){let o=t.pipe(d(({offset:{y:a}})=>a),Ne(2,1),d(([a,c])=>a>c&&c>0),Q()),i=r.pipe(d(({active:a})=>a));return G([i,o]).pipe(d(([a,c])=>!(a&&c)),Q(),te(n.pipe(Pe(1))),Vr(!0),Ot({delay:250}),d(a=>({hidden:a})))}function zi(e,{viewport$:t,header$:r,main$:n,target$:o}){let i=new L,a=i.pipe(ue(1));return i.subscribe({next({hidden:c}){e.hidden=c,c?(e.setAttribute("tabindex","-1"),e.blur()):e.removeAttribute("tabindex")},complete(){e.style.top="",e.hidden=!0,e.removeAttribute("tabindex")}}),r.pipe(te(a),J("height")).subscribe(({height:c})=>{e.style.top=`${c+16}px`}),Bs(e,{viewport$:t,main$:n,target$:o}).pipe(M(c=>i.next(c)),R(()=>i.complete()),d(c=>N({ref:e},c)))}function qi({document$:e,tablet$:t}){e.pipe(_(()=>W(".md-toggle--indeterminate, [data-md-state=indeterminate]")),M(r=>{r.indeterminate=!0,r.checked=!1}),oe(r=>w(r,"change").pipe(zr(()=>r.classList.contains("md-toggle--indeterminate")),d(()=>r))),me(t)).subscribe(([r,n])=>{r.classList.remove("md-toggle--indeterminate"),n&&(r.checked=!1)})}function Ys(){return/(iPad|iPhone|iPod)/.test(navigator.userAgent)}function Ki({document$:e}){e.pipe(_(()=>W("[data-md-scrollfix]")),M(t=>t.removeAttribute("data-md-scrollfix")),A(Ys),oe(t=>w(t,"touchstart").pipe(d(()=>t)))).subscribe(t=>{let r=t.scrollTop;r===0?t.scrollTop=1:r+t.offsetHeight===t.scrollHeight&&(t.scrollTop=r-1)})}function Bi({viewport$:e,tablet$:t}){G([ht("search"),t]).pipe(d(([r,n])=>r&&!n),_(r=>C(r).pipe(ke(r?400:100))),me(e)).subscribe(([r,{offset:{y:n}}])=>{if(r)document.body.setAttribute("data-md-scrolllock",""),document.body.style.top=`-${n}px`;else{let o=-1*parseInt(document.body.style.top,10);document.body.removeAttribute("data-md-scrolllock"),document.body.style.top="",o&&window.scrollTo(0,o)}})}Object.entries||(Object.entries=function(e){let t=[];for(let r of Object.keys(e))t.push([r,e[r]]);return t});Object.values||(Object.values=function(e){let t=[];for(let r of Object.keys(e))t.push(e[r]);return t});typeof Element!="undefined"&&(Element.prototype.scrollTo||(Element.prototype.scrollTo=function(e,t){typeof e=="object"?(this.scrollLeft=e.left,this.scrollTop=e.top):(this.scrollLeft=e,this.scrollTop=t)}),Element.prototype.replaceWith||(Element.prototype.replaceWith=function(...e){let t=this.parentNode;if(t){e.length===0&&t.removeChild(this);for(let r=e.length-1;r>=0;r--){let n=e[r];typeof n!="object"?n=document.createTextNode(n):n.parentNode&&n.parentNode.removeChild(n),r?t.insertBefore(this.previousSibling,n):t.replaceChild(n,this)}}}));document.documentElement.classList.remove("no-js");document.documentElement.classList.add("js");var nt=So(),vr=jo(),rt=Do(),un=Io(),ge=Jo(),gr=Yr("(min-width: 960px)"),Gi=Yr("(min-width: 1220px)"),Ji=Wo(),Gs=ce(),pn=new L;ki({alert$:pn});Z("navigation.instant")&&Pi({document$:nt,location$:vr,viewport$:ge});var Yi;((Yi=Gs.version)==null?void 0:Yi.provider)==="mike"&&ji({document$:nt});P(vr,rt).pipe(ke(125)).subscribe(()=>{Ye("drawer",!1),Ye("search",!1)});un.pipe(A(({mode:e})=>e==="global")).subscribe(e=>{switch(e.type){case"p":case",":let t=ie("[href][rel=prev]");typeof t!="undefined"&&t.click();break;case"n":case".":let r=ie("[href][rel=next]");typeof r!="undefined"&&r.click();break}});qi({document$:nt,tablet$:gr});Ki({document$:nt});Bi({viewport$:ge,tablet$:gr});var We=xi(_e("header"),{viewport$:ge}),br=nt.pipe(d(()=>_e("main")),_(e=>Si(e,{viewport$:ge,header$:We})),X(1)),Js=P(...ee("consent").map(e=>Xo(e,{target$:rt})),...ee("dialog").map(e=>yi(e,{alert$:pn})),...ee("header").map(e=>wi(e,{viewport$:ge,header$:We,main$:br})),...ee("palette").map(e=>Oi(e)),...ee("search").map(e=>Hi(e,{keyboard$:un})),...ee("source").map(e=>Di(e))),Qs=I(()=>P(...ee("announce").map(e=>Qo(e)),...ee("content").map(e=>gi(e,{viewport$:ge,target$:rt,print$:Ji})),...ee("content").map(e=>Z("search.highlight")?Fi(e,{location$:vr}):k),...ee("header-title").map(e=>Ei(e,{viewport$:ge,header$:We})),...ee("sidebar").map(e=>e.getAttribute("data-md-type")==="navigation"?Gr(Gi,()=>fn(e,{viewport$:ge,header$:We,main$:br})):Gr(gr,()=>fn(e,{viewport$:ge,header$:We,main$:br}))),...ee("tabs").map(e=>Wi(e,{viewport$:ge,header$:We})),...ee("toc").map(e=>ln(e,{viewport$:ge,header$:We,target$:rt,localToc:!0})),...ee("sidebar").filter(e=>e.getAttribute("data-md-type")==="navigation").map(e=>ln(e,{viewport$:ge,header$:We,target$:rt,localToc:!1})),...ee("top").map(e=>zi(e,{viewport$:ge,header$:We,main$:br,target$:rt})))),Qi=nt.pipe(_(()=>Qs),tt(Js),X(1));Qi.subscribe();window.document$=nt;window.location$=vr;window.target$=rt;window.keyboard$=un;window.viewport$=ge;window.tablet$=gr;window.screen$=Gi;window.print$=Ji;window.alert$=pn;window.component$=Qi;})();
diff --git a/devel/api.html b/devel/api.html
index f3a370f57b..d9d1132d2e 100644
--- a/devel/api.html
+++ b/devel/api.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>API Reference - arch v6.2.1 (+21)</title>
+        <title>API Reference - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL(".",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -918,7 +918,7 @@ <h2 id="long-run-covariance-hac-estimation">Long-run Covariance (HAC) Estimation
     <script id="__config" type="application/json">{"base": ".", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/devel/bootstrap/background.html b/devel/bootstrap/background.html
index 84d04a9c7c..052115f1a2 100644
--- a/devel/bootstrap/background.html
+++ b/devel/bootstrap/background.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>References - arch v6.2.1 (+21)</title>
+        <title>References - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
@@ -87,7 +87,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -97,7 +97,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -184,10 +184,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -710,7 +710,7 @@ <h1 id="references">References<a class="headerlink" href="#references" title="Li
     <script id="__config" type="application/json">{"base": "..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/devel/bootstrap/bootstrap.html b/devel/bootstrap/bootstrap.html
index 4efd21e7e0..ba403b132c 100644
--- a/devel/bootstrap/bootstrap.html
+++ b/devel/bootstrap/bootstrap.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>Bootstrapping - arch v6.2.1 (+21)</title>
+        <title>Bootstrapping - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
@@ -87,7 +87,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -97,7 +97,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -184,10 +184,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -779,7 +779,7 @@ <h1 id="module-arch.bootstrap"><span id="bootstrapping"></span>Bootstrapping<a c
     <script id="__config" type="application/json">{"base": "..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/devel/bootstrap/bootstrap_examples.html b/devel/bootstrap/bootstrap_examples.html
index 4843bf8d75..2a27754dba 100644
--- a/devel/bootstrap/bootstrap_examples.html
+++ b/devel/bootstrap/bootstrap_examples.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>Bootstrap Examples - arch v6.2.1 (+21)</title>
+        <title>Bootstrap Examples - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../_static/nbsphinx-code-cells.css?v=2aa19091" />
         <link rel="stylesheet" type="text/css" href="../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
@@ -93,7 +93,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -103,7 +103,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -190,10 +190,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -1321,7 +1321,7 @@ <h2 id="Bootstrapping-Uneven-Length-Samples">Bootstrapping Uneven Length Samples
     <script id="__config" type="application/json">{"base": "..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
         <script>window.MathJax = {"tex": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true}, "options": {"ignoreHtmlClass": "tex2jax_ignore|mathjax_ignore|document", "processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
         <script id="MathJax-script" src="../_static/mathjax/tex-mml-chtml.js?v=cadf963e"></script>
diff --git a/devel/bootstrap/confidence-intervals.html b/devel/bootstrap/confidence-intervals.html
index f00b73cbb9..693de8e46a 100644
--- a/devel/bootstrap/confidence-intervals.html
+++ b/devel/bootstrap/confidence-intervals.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>Confidence Intervals - arch v6.2.1 (+21)</title>
+        <title>Confidence Intervals - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -1140,7 +1140,7 @@ <h3 id="bias-corrected-and-accelerated-bca"><a class="toc-backref" href="#id12"
     <script id="__config" type="application/json">{"base": "..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
         <script>window.MathJax = {"tex": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true}, "options": {"ignoreHtmlClass": "tex2jax_ignore|mathjax_ignore|document", "processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
         <script id="MathJax-script" src="../_static/mathjax/tex-mml-chtml.js?v=cadf963e"></script>
diff --git a/devel/bootstrap/generated/arch.bootstrap.CircularBlockBootstrap.html b/devel/bootstrap/generated/arch.bootstrap.CircularBlockBootstrap.html
index 53f04ae689..3ac49f8bf1 100644
--- a/devel/bootstrap/generated/arch.bootstrap.CircularBlockBootstrap.html
+++ b/devel/bootstrap/generated/arch.bootstrap.CircularBlockBootstrap.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.CircularBlockBootstrap - arch v6.2.1 (+21)</title>
+        <title>arch.bootstrap.CircularBlockBootstrap - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -1588,7 +1588,7 @@ <h1 id="arch-bootstrap-circularblockbootstrap">arch.bootstrap.CircularBlockBoots
     <script id="__config" type="application/json">{"base": "../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/devel/bootstrap/generated/arch.bootstrap.IIDBootstrap.html b/devel/bootstrap/generated/arch.bootstrap.IIDBootstrap.html
index 76b4a9dbd4..05f1273415 100644
--- a/devel/bootstrap/generated/arch.bootstrap.IIDBootstrap.html
+++ b/devel/bootstrap/generated/arch.bootstrap.IIDBootstrap.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.IIDBootstrap - arch v6.2.1 (+21)</title>
+        <title>arch.bootstrap.IIDBootstrap - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -1527,7 +1527,7 @@ <h1 id="arch-bootstrap-iidbootstrap">arch.bootstrap.IIDBootstrap<a class="header
     <script id="__config" type="application/json">{"base": "../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/devel/bootstrap/generated/arch.bootstrap.IndependentSamplesBootstrap.html b/devel/bootstrap/generated/arch.bootstrap.IndependentSamplesBootstrap.html
index d92d7c36ad..0ef443a8b5 100644
--- a/devel/bootstrap/generated/arch.bootstrap.IndependentSamplesBootstrap.html
+++ b/devel/bootstrap/generated/arch.bootstrap.IndependentSamplesBootstrap.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.IndependentSamplesBootstrap - arch v6.2.1 (+21)</title>
+        <title>arch.bootstrap.IndependentSamplesBootstrap - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -1489,7 +1489,7 @@ <h1 id="arch-bootstrap-independentsamplesbootstrap">arch.bootstrap.IndependentSa
     <script id="__config" type="application/json">{"base": "../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/devel/bootstrap/generated/arch.bootstrap.MovingBlockBootstrap.html b/devel/bootstrap/generated/arch.bootstrap.MovingBlockBootstrap.html
index 3311122c9f..2b90abf100 100644
--- a/devel/bootstrap/generated/arch.bootstrap.MovingBlockBootstrap.html
+++ b/devel/bootstrap/generated/arch.bootstrap.MovingBlockBootstrap.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.MovingBlockBootstrap - arch v6.2.1 (+21)</title>
+        <title>arch.bootstrap.MovingBlockBootstrap - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -1590,7 +1590,7 @@ <h1 id="arch-bootstrap-movingblockbootstrap">arch.bootstrap.MovingBlockBootstrap
     <script id="__config" type="application/json">{"base": "../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/devel/bootstrap/generated/arch.bootstrap.StationaryBootstrap.html b/devel/bootstrap/generated/arch.bootstrap.StationaryBootstrap.html
index ce28648abc..7a29c5e36e 100644
--- a/devel/bootstrap/generated/arch.bootstrap.StationaryBootstrap.html
+++ b/devel/bootstrap/generated/arch.bootstrap.StationaryBootstrap.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.StationaryBootstrap - arch v6.2.1 (+21)</title>
+        <title>arch.bootstrap.StationaryBootstrap - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -1588,7 +1588,7 @@ <h1 id="arch-bootstrap-stationarybootstrap">arch.bootstrap.StationaryBootstrap<a
     <script id="__config" type="application/json">{"base": "../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/devel/bootstrap/generated/arch.bootstrap.optimal_block_length.html b/devel/bootstrap/generated/arch.bootstrap.optimal_block_length.html
index dbc1233762..d64f0cb9ac 100644
--- a/devel/bootstrap/generated/arch.bootstrap.optimal_block_length.html
+++ b/devel/bootstrap/generated/arch.bootstrap.optimal_block_length.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.optimal_block_length - arch v6.2.1 (+21)</title>
+        <title>arch.bootstrap.optimal_block_length - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -782,7 +782,7 @@ <h1 id="arch-bootstrap-optimal-block-length">arch.bootstrap.optimal_block_length
 size for the circular bootstrap.</p>
 </dd>
 <dt class="field-odd" id="arch.bootstrap.optimal_block_length-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.optimal_block_length-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><code class="xref py py-class docutils literal notranslate"><span class="pre">pandas.DataFrame</span></code></a></p>
+<dd class="field-odd"><p><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><code class="xref py py-class docutils literal notranslate"><span class="pre">pandas.DataFrame</span></code></a></p>
 </dd>
 </dl>
 <div class="seealso admonition">
@@ -950,7 +950,7 @@ <h1 id="arch-bootstrap-optimal-block-length">arch.bootstrap.optimal_block_length
     <script id="__config" type="application/json">{"base": "../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
         <script>window.MathJax = {"tex": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true}, "options": {"ignoreHtmlClass": "tex2jax_ignore|mathjax_ignore|document", "processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
         <script id="MathJax-script" src="../../_static/mathjax/tex-mml-chtml.js?v=cadf963e"></script>
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.apply.html b/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.apply.html
index d305f73c65..f415157d66 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.apply.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.apply.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.CircularBlockBootstrap.apply - arch v6.2.1 (+21)</title>
+        <title>arch.bootstrap.CircularBlockBootstrap.apply - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -1084,12 +1084,12 @@
 <h1 id="arch-bootstrap-circularblockbootstrap-apply">arch.bootstrap.CircularBlockBootstrap.apply<a class="headerlink" href="#arch-bootstrap-circularblockbootstrap-apply" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.bootstrap.CircularBlockBootstrap.apply">
-<span class="sig-prename descclassname"><span class="pre">CircularBlockBootstrap.</span></span><span class="sig-name descname"><span class="pre">apply</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.CircularBlockBootstrap.apply.func" title="arch.bootstrap.CircularBlockBootstrap.apply.func (Python parameter) — Function the computes parameter values.  See Notes for requirements"><span class="n"><span class="pre">func</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.12)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="p"><span class="pre">]</span></span></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.CircularBlockBootstrap.apply.reps" title="arch.bootstrap.CircularBlockBootstrap.apply.reps (Python parameter) — Number of bootstrap replications"><span class="n"><span class="pre">reps</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.CircularBlockBootstrap.apply.extra_kwargs" title="arch.bootstrap.CircularBlockBootstrap.apply.extra_kwargs (Python parameter) — Extra keyword arguments to use when calling func.  Must not conflict with keyword arguments used to initialize bootstrap"><span class="n"><span class="pre">extra_kwargs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.12)"><span class="pre">Any</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="headerlink" href="#arch.bootstrap.CircularBlockBootstrap.apply" title="Link to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">CircularBlockBootstrap.</span></span><span class="sig-name descname"><span class="pre">apply</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.CircularBlockBootstrap.apply.func" title="arch.bootstrap.CircularBlockBootstrap.apply.func (Python parameter) — Function the computes parameter values.  See Notes for requirements"><span class="n"><span class="pre">func</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.12)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a><span class="p"><span class="pre">]</span></span></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.CircularBlockBootstrap.apply.reps" title="arch.bootstrap.CircularBlockBootstrap.apply.reps (Python parameter) — Number of bootstrap replications"><span class="n"><span class="pre">reps</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.CircularBlockBootstrap.apply.extra_kwargs" title="arch.bootstrap.CircularBlockBootstrap.apply.extra_kwargs (Python parameter) — Extra keyword arguments to use when calling func.  Must not conflict with keyword arguments used to initialize bootstrap"><span class="n"><span class="pre">extra_kwargs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.12)"><span class="pre">Any</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="headerlink" href="#arch.bootstrap.CircularBlockBootstrap.apply" title="Link to this definition">¶</a></dt>
 <dd><p>Applies a function to bootstrap replicated data</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.bootstrap.CircularBlockBootstrap.apply-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.CircularBlockBootstrap.apply-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.bootstrap.CircularBlockBootstrap.apply.func"><span class="n sig-name">func</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.12)">Callable</a><span class="p">[</span><span class="p">[</span><span class="p">...</span><span class="p">]</span><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a><span class="p">]</span></span></span><a class="headerlink" href="#arch.bootstrap.CircularBlockBootstrap.apply.func" title="Permalink to this definition">¶</a></dt><dd><p>Function the computes parameter values.  See Notes for requirements</p>
+<dt id="arch.bootstrap.CircularBlockBootstrap.apply.func"><span class="n sig-name">func</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.12)">Callable</a><span class="p">[</span><span class="p">[</span><span class="p">...</span><span class="p">]</span><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a><span class="p">]</span></span></span><a class="headerlink" href="#arch.bootstrap.CircularBlockBootstrap.apply.func" title="Permalink to this definition">¶</a></dt><dd><p>Function the computes parameter values.  See Notes for requirements</p>
 </dd>
 <dt id="arch.bootstrap.CircularBlockBootstrap.apply.reps"><span class="n sig-name">reps</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code><a class="headerlink" href="#arch.bootstrap.CircularBlockBootstrap.apply.reps" title="Permalink to this definition">¶</a></dt><dd><p>Number of bootstrap replications</p>
 </dd>
@@ -1240,7 +1240,7 @@ <h1 id="arch-bootstrap-circularblockbootstrap-apply">arch.bootstrap.CircularBloc
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.bootstrap.html b/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.bootstrap.html
index 5397ac62d1..08729b630c 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.bootstrap.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.bootstrap.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.CircularBlockBootstrap.bootstrap - arch v6.2.1 (+21)</title>
+        <title>arch.bootstrap.CircularBlockBootstrap.bootstrap - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -1056,7 +1056,7 @@
 <h1 id="arch-bootstrap-circularblockbootstrap-bootstrap">arch.bootstrap.CircularBlockBootstrap.bootstrap<a class="headerlink" href="#arch-bootstrap-circularblockbootstrap-bootstrap" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.bootstrap.CircularBlockBootstrap.bootstrap">
-<span class="sig-prename descclassname"><span class="pre">CircularBlockBootstrap.</span></span><span class="sig-name descname"><span class="pre">bootstrap</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.CircularBlockBootstrap.bootstrap.reps" title="arch.bootstrap.CircularBlockBootstrap.bootstrap.reps (Python parameter) — Number of bootstrap replications"><span class="n"><span class="pre">reps</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Generator" title="(in Python v3.12)"><span class="pre">Generator</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="p"><span class="pre">]</span></span></span></span></span><a class="headerlink" href="#arch.bootstrap.CircularBlockBootstrap.bootstrap" title="Link to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">CircularBlockBootstrap.</span></span><span class="sig-name descname"><span class="pre">bootstrap</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.CircularBlockBootstrap.bootstrap.reps" title="arch.bootstrap.CircularBlockBootstrap.bootstrap.reps (Python parameter) — Number of bootstrap replications"><span class="n"><span class="pre">reps</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Generator" title="(in Python v3.12)"><span class="pre">Generator</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="p"><span class="pre">]</span></span></span></span></span><a class="headerlink" href="#arch.bootstrap.CircularBlockBootstrap.bootstrap" title="Link to this definition">¶</a></dt>
 <dd><p>Iterator for use when bootstrapping</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.bootstrap.CircularBlockBootstrap.bootstrap-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.CircularBlockBootstrap.bootstrap-parameters" title="Permalink to this headline">¶</a></dt>
@@ -1208,7 +1208,7 @@ <h1 id="arch-bootstrap-circularblockbootstrap-bootstrap">arch.bootstrap.Circular
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.clone.html b/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.clone.html
index fd2b4c9702..31b4aaa71f 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.clone.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.clone.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.CircularBlockBootstrap.clone - arch v6.2.1 (+21)</title>
+        <title>arch.bootstrap.CircularBlockBootstrap.clone - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -1220,7 +1220,7 @@ <h1 id="arch-bootstrap-circularblockbootstrap-clone">arch.bootstrap.CircularBloc
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.conf_int.html b/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.conf_int.html
index 6cee4747b7..ee0aa7557c 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.conf_int.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.conf_int.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.CircularBlockBootstrap.conf_int - arch v6.2.1 (+21)</title>
+        <title>arch.bootstrap.CircularBlockBootstrap.conf_int - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -1182,7 +1182,7 @@
 <h1 id="arch-bootstrap-circularblockbootstrap-conf-int">arch.bootstrap.CircularBlockBootstrap.conf_int<a class="headerlink" href="#arch-bootstrap-circularblockbootstrap-conf-int" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.bootstrap.CircularBlockBootstrap.conf_int">
-<span class="sig-prename descclassname"><span class="pre">CircularBlockBootstrap.</span></span><span class="sig-name descname"><span class="pre">conf_int</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.CircularBlockBootstrap.conf_int.func" title="arch.bootstrap.CircularBlockBootstrap.conf_int.func (Python parameter) — Function the computes parameter values.  See Notes for requirements"><span class="n"><span class="pre">func</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.12)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.CircularBlockBootstrap.conf_int.reps" title="arch.bootstrap.CircularBlockBootstrap.conf_int.reps (Python parameter) — Number of bootstrap replications"><span class="n"><span class="pre">reps</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.CircularBlockBootstrap.conf_int.method" title="arch.bootstrap.CircularBlockBootstrap.conf_int.method (Python parameter) — One of 'basic', 'percentile', 'studentized', 'norm' (identical to 'var', 'cov'), 'bc' (identical to 'debiased', 'bias-corrected'), or 'bca'"><span class="n"><span class="pre">method</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'basic'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'percentile'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'studentized'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'norm'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'bc'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'bca'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;basic&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.CircularBlockBootstrap.conf_int.size" title="arch.bootstrap.CircularBlockBootstrap.conf_int.size (Python parameter) — Coverage of confidence interval"><span class="n"><span class="pre">size</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mf">0.95</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.CircularBlockBootstrap.conf_int.tail" title="arch.bootstrap.CircularBlockBootstrap.conf_int.tail (Python parameter) — One of 'two', 'upper' or 'lower'."><span class="n"><span class="pre">tail</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'two'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'upper'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'lower'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;two&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.CircularBlockBootstrap.conf_int.extra_kwargs" title="arch.bootstrap.CircularBlockBootstrap.conf_int.extra_kwargs (Python parameter) — Extra keyword arguments to use when calling func and std_err_func, when appropriate"><span class="n"><span class="pre">extra_kwargs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.12)"><span class="pre">Any</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.CircularBlockBootstrap.conf_int.reuse" title="arch.bootstrap.CircularBlockBootstrap.conf_int.reuse (Python parameter) — Flag indicating whether to reuse previously computed bootstrap results.  This allows alternative methods to be compared without rerunning the bootstrap simulation.  Reuse is ignored if reps is not the same across multiple runs, func changes across calls, or method is 'studentized'."><span class="n"><span class="pre">reuse</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.CircularBlockBootstrap.conf_int.sampling" title="arch.bootstrap.CircularBlockBootstrap.conf_int.sampling (Python parameter) — Type of sampling to use: 'nonparametric', 'semi-parametric' (or 'semi') or 'parametric'.  The default is 'nonparametric'.  See notes about the changes to func required when using 'semi' or 'parametric'."><span class="n"><span class="pre">sampling</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'nonparametric'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'semi-parametric'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'semi'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'parametric'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'semiparametric'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;nonparametric&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.CircularBlockBootstrap.conf_int.std_err_func" title="arch.bootstrap.CircularBlockBootstrap.conf_int.std_err_func (Python parameter) — Function to use when standardizing estimated parameters when using the studentized bootstrap.  Providing an analytical function eliminates the need for a nested bootstrap"><span class="n"><span class="pre">std_err_func</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.12)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.CircularBlockBootstrap.conf_int.studentize_reps" title="arch.bootstrap.CircularBlockBootstrap.conf_int.studentize_reps (Python parameter) — Number of bootstraps to use in the inner bootstrap when using the studentized bootstrap.  Ignored when std_err_func is provided"><span class="n"><span class="pre">studentize_reps</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="headerlink" href="#arch.bootstrap.CircularBlockBootstrap.conf_int" title="Link to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">CircularBlockBootstrap.</span></span><span class="sig-name descname"><span class="pre">conf_int</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.CircularBlockBootstrap.conf_int.func" title="arch.bootstrap.CircularBlockBootstrap.conf_int.func (Python parameter) — Function the computes parameter values.  See Notes for requirements"><span class="n"><span class="pre">func</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.12)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.CircularBlockBootstrap.conf_int.reps" title="arch.bootstrap.CircularBlockBootstrap.conf_int.reps (Python parameter) — Number of bootstrap replications"><span class="n"><span class="pre">reps</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.CircularBlockBootstrap.conf_int.method" title="arch.bootstrap.CircularBlockBootstrap.conf_int.method (Python parameter) — One of 'basic', 'percentile', 'studentized', 'norm' (identical to 'var', 'cov'), 'bc' (identical to 'debiased', 'bias-corrected'), or 'bca'"><span class="n"><span class="pre">method</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'basic'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'percentile'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'studentized'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'norm'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'bc'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'bca'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;basic&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.CircularBlockBootstrap.conf_int.size" title="arch.bootstrap.CircularBlockBootstrap.conf_int.size (Python parameter) — Coverage of confidence interval"><span class="n"><span class="pre">size</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mf">0.95</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.CircularBlockBootstrap.conf_int.tail" title="arch.bootstrap.CircularBlockBootstrap.conf_int.tail (Python parameter) — One of 'two', 'upper' or 'lower'."><span class="n"><span class="pre">tail</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'two'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'upper'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'lower'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;two&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.CircularBlockBootstrap.conf_int.extra_kwargs" title="arch.bootstrap.CircularBlockBootstrap.conf_int.extra_kwargs (Python parameter) — Extra keyword arguments to use when calling func and std_err_func, when appropriate"><span class="n"><span class="pre">extra_kwargs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.12)"><span class="pre">Any</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.CircularBlockBootstrap.conf_int.reuse" title="arch.bootstrap.CircularBlockBootstrap.conf_int.reuse (Python parameter) — Flag indicating whether to reuse previously computed bootstrap results.  This allows alternative methods to be compared without rerunning the bootstrap simulation.  Reuse is ignored if reps is not the same across multiple runs, func changes across calls, or method is 'studentized'."><span class="n"><span class="pre">reuse</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.CircularBlockBootstrap.conf_int.sampling" title="arch.bootstrap.CircularBlockBootstrap.conf_int.sampling (Python parameter) — Type of sampling to use: 'nonparametric', 'semi-parametric' (or 'semi') or 'parametric'.  The default is 'nonparametric'.  See notes about the changes to func required when using 'semi' or 'parametric'."><span class="n"><span class="pre">sampling</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'nonparametric'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'semi-parametric'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'semi'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'parametric'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'semiparametric'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;nonparametric&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.CircularBlockBootstrap.conf_int.std_err_func" title="arch.bootstrap.CircularBlockBootstrap.conf_int.std_err_func (Python parameter) — Function to use when standardizing estimated parameters when using the studentized bootstrap.  Providing an analytical function eliminates the need for a nested bootstrap"><span class="n"><span class="pre">std_err_func</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.12)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.CircularBlockBootstrap.conf_int.studentize_reps" title="arch.bootstrap.CircularBlockBootstrap.conf_int.studentize_reps (Python parameter) — Number of bootstraps to use in the inner bootstrap when using the studentized bootstrap.  Ignored when std_err_func is provided"><span class="n"><span class="pre">studentize_reps</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="headerlink" href="#arch.bootstrap.CircularBlockBootstrap.conf_int" title="Link to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 <dt class="field-odd" id="arch.bootstrap.CircularBlockBootstrap.conf_int-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.CircularBlockBootstrap.conf_int-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
@@ -1212,7 +1212,7 @@ <h1 id="arch-bootstrap-circularblockbootstrap-conf-int">arch.bootstrap.CircularB
 <dt id="arch.bootstrap.CircularBlockBootstrap.conf_int.extra_kwargs"><span class="n sig-name">extra_kwargs</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)">dict</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)">str</a><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.12)">Any</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.bootstrap.CircularBlockBootstrap.conf_int.extra_kwargs" title="Permalink to this definition">¶</a></dt><dd><p>Extra keyword arguments to use when calling func and std_err_func,
 when appropriate</p>
 </dd>
-<dt id="arch.bootstrap.CircularBlockBootstrap.conf_int.std_err_func"><span class="n sig-name">std_err_func</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.12)">Callable</a><span class="p">[</span><span class="p">[</span><span class="p">...</span><span class="p">]</span><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.bootstrap.CircularBlockBootstrap.conf_int.std_err_func" title="Permalink to this definition">¶</a></dt><dd><p>Function to use when standardizing estimated parameters when using
+<dt id="arch.bootstrap.CircularBlockBootstrap.conf_int.std_err_func"><span class="n sig-name">std_err_func</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.12)">Callable</a><span class="p">[</span><span class="p">[</span><span class="p">...</span><span class="p">]</span><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.bootstrap.CircularBlockBootstrap.conf_int.std_err_func" title="Permalink to this definition">¶</a></dt><dd><p>Function to use when standardizing estimated parameters when using
 the studentized bootstrap.  Providing an analytical function
 eliminates the need for a nested bootstrap</p>
 </dd>
@@ -1387,7 +1387,7 @@ <h1 id="arch-bootstrap-circularblockbootstrap-conf-int">arch.bootstrap.CircularB
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
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diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.cov.html b/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.cov.html
index 09d03ba61e..d88db6f488 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.cov.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.cov.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.CircularBlockBootstrap.cov - arch v6.2.1 (+21)</title>
+        <title>arch.bootstrap.CircularBlockBootstrap.cov - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -1098,12 +1098,12 @@
 <h1 id="arch-bootstrap-circularblockbootstrap-cov">arch.bootstrap.CircularBlockBootstrap.cov<a class="headerlink" href="#arch-bootstrap-circularblockbootstrap-cov" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.bootstrap.CircularBlockBootstrap.cov">
-<span class="sig-prename descclassname"><span class="pre">CircularBlockBootstrap.</span></span><span class="sig-name descname"><span class="pre">cov</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.CircularBlockBootstrap.cov.func" title="arch.bootstrap.CircularBlockBootstrap.cov.func (Python parameter) — Callable function that returns the statistic of interest as a 1-d array"><span class="n"><span class="pre">func</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.12)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="p"><span class="pre">]</span></span></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.CircularBlockBootstrap.cov.reps" title="arch.bootstrap.CircularBlockBootstrap.cov.reps (Python parameter) — Number of bootstrap replications"><span class="n"><span class="pre">reps</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.CircularBlockBootstrap.cov.recenter" title="arch.bootstrap.CircularBlockBootstrap.cov.recenter (Python parameter) — Whether to center the bootstrap variance estimator on the average of the bootstrap samples (True) or to center on the original sample estimate (False).  Default is True."><span class="n"><span class="pre">recenter</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.CircularBlockBootstrap.cov.extra_kwargs" title="arch.bootstrap.CircularBlockBootstrap.cov.extra_kwargs (Python parameter) — Dictionary of extra keyword arguments to pass to func"><span class="n"><span class="pre">extra_kwargs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.12)"><span class="pre">Any</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="headerlink" href="#arch.bootstrap.CircularBlockBootstrap.cov" title="Link to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">CircularBlockBootstrap.</span></span><span class="sig-name descname"><span class="pre">cov</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.CircularBlockBootstrap.cov.func" title="arch.bootstrap.CircularBlockBootstrap.cov.func (Python parameter) — Callable function that returns the statistic of interest as a 1-d array"><span class="n"><span class="pre">func</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.12)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a><span class="p"><span class="pre">]</span></span></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.CircularBlockBootstrap.cov.reps" title="arch.bootstrap.CircularBlockBootstrap.cov.reps (Python parameter) — Number of bootstrap replications"><span class="n"><span class="pre">reps</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.CircularBlockBootstrap.cov.recenter" title="arch.bootstrap.CircularBlockBootstrap.cov.recenter (Python parameter) — Whether to center the bootstrap variance estimator on the average of the bootstrap samples (True) or to center on the original sample estimate (False).  Default is True."><span class="n"><span class="pre">recenter</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.CircularBlockBootstrap.cov.extra_kwargs" title="arch.bootstrap.CircularBlockBootstrap.cov.extra_kwargs (Python parameter) — Dictionary of extra keyword arguments to pass to func"><span class="n"><span class="pre">extra_kwargs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.12)"><span class="pre">Any</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="headerlink" href="#arch.bootstrap.CircularBlockBootstrap.cov" title="Link to this definition">¶</a></dt>
 <dd><p>Compute parameter covariance using bootstrap</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.bootstrap.CircularBlockBootstrap.cov-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.CircularBlockBootstrap.cov-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.bootstrap.CircularBlockBootstrap.cov.func"><span class="n sig-name">func</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.12)">Callable</a><span class="p">[</span><span class="p">[</span><span class="p">...</span><span class="p">]</span><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a><span class="p">]</span></span></span><a class="headerlink" href="#arch.bootstrap.CircularBlockBootstrap.cov.func" title="Permalink to this definition">¶</a></dt><dd><p>Callable function that returns the statistic of interest as a
+<dt id="arch.bootstrap.CircularBlockBootstrap.cov.func"><span class="n sig-name">func</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.12)">Callable</a><span class="p">[</span><span class="p">[</span><span class="p">...</span><span class="p">]</span><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a><span class="p">]</span></span></span><a class="headerlink" href="#arch.bootstrap.CircularBlockBootstrap.cov.func" title="Permalink to this definition">¶</a></dt><dd><p>Callable function that returns the statistic of interest as a
 1-d array</p>
 </dd>
 <dt id="arch.bootstrap.CircularBlockBootstrap.cov.reps"><span class="n sig-name">reps</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code><a class="headerlink" href="#arch.bootstrap.CircularBlockBootstrap.cov.reps" title="Permalink to this definition">¶</a></dt><dd><p>Number of bootstrap replications</p>
@@ -1274,7 +1274,7 @@ <h1 id="arch-bootstrap-circularblockbootstrap-cov">arch.bootstrap.CircularBlockB
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.generator.html b/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.generator.html
index 16fe728512..4684568e4a 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.generator.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.generator.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.CircularBlockBootstrap.generator - arch v6.2.1 (+21)</title>
+        <title>arch.bootstrap.CircularBlockBootstrap.generator - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -1164,7 +1164,7 @@ <h1 id="arch-bootstrap-circularblockbootstrap-generator">arch.bootstrap.Circular
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.get_state.html b/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.get_state.html
index 15d77af27e..2341243f7e 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.get_state.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.get_state.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.CircularBlockBootstrap.get_state - arch v6.2.1 (+21)</title>
+        <title>arch.bootstrap.CircularBlockBootstrap.get_state - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -1142,7 +1142,7 @@ <h1 id="arch-bootstrap-circularblockbootstrap-get-state">arch.bootstrap.Circular
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.index.html b/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.index.html
index e8cdcecd6e..1d2ce1dcf1 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.index.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.index.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.CircularBlockBootstrap.index - arch v6.2.1 (+21)</title>
+        <title>arch.bootstrap.CircularBlockBootstrap.index - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -1094,7 +1094,7 @@ <h1 id="arch-bootstrap-circularblockbootstrap-index">arch.bootstrap.CircularBloc
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.random_state.html b/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.random_state.html
index 4f96859ad2..8607323cce 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.random_state.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.random_state.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.CircularBlockBootstrap.random_state - arch v6.2.1 (+21)</title>
+        <title>arch.bootstrap.CircularBlockBootstrap.random_state - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -1162,7 +1162,7 @@ <h1 id="arch-bootstrap-circularblockbootstrap-random-state">arch.bootstrap.Circu
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.reset.html b/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.reset.html
index 634df5e077..c9e25c6a0a 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.reset.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.reset.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.CircularBlockBootstrap.reset - arch v6.2.1 (+21)</title>
+        <title>arch.bootstrap.CircularBlockBootstrap.reset - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -1156,7 +1156,7 @@ <h1 id="arch-bootstrap-circularblockbootstrap-reset">arch.bootstrap.CircularBloc
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.seed.html b/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.seed.html
index 2f6be75901..40dc9258b9 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.seed.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.seed.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.CircularBlockBootstrap.seed - arch v6.2.1 (+21)</title>
+        <title>arch.bootstrap.CircularBlockBootstrap.seed - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -1154,7 +1154,7 @@ <h1 id="arch-bootstrap-circularblockbootstrap-seed">arch.bootstrap.CircularBlock
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.set_state.html b/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.set_state.html
index d0756b564e..bea9b287fe 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.set_state.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.set_state.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.CircularBlockBootstrap.set_state - arch v6.2.1 (+21)</title>
+        <title>arch.bootstrap.CircularBlockBootstrap.set_state - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -1154,7 +1154,7 @@ <h1 id="arch-bootstrap-circularblockbootstrap-set-state">arch.bootstrap.Circular
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.state.html b/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.state.html
index df841355e7..cf549b5f1d 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.state.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.state.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.CircularBlockBootstrap.state - arch v6.2.1 (+21)</title>
+        <title>arch.bootstrap.CircularBlockBootstrap.state - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -1144,7 +1144,7 @@ <h1 id="arch-bootstrap-circularblockbootstrap-state">arch.bootstrap.CircularBloc
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.update_indices.html b/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.update_indices.html
index 989a93254d..24e9d868f9 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.update_indices.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.update_indices.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.CircularBlockBootstrap.update_indices - arch v6.2.1 (+21)</title>
+        <title>arch.bootstrap.CircularBlockBootstrap.update_indices - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -1095,7 +1095,7 @@ <h1 id="arch-bootstrap-circularblockbootstrap-update-indices">arch.bootstrap.Cir
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.var.html b/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.var.html
index 706b4d6863..b17e006743 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.var.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.CircularBlockBootstrap.var.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.CircularBlockBootstrap.var - arch v6.2.1 (+21)</title>
+        <title>arch.bootstrap.CircularBlockBootstrap.var - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -1098,12 +1098,12 @@
 <h1 id="arch-bootstrap-circularblockbootstrap-var">arch.bootstrap.CircularBlockBootstrap.var<a class="headerlink" href="#arch-bootstrap-circularblockbootstrap-var" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.bootstrap.CircularBlockBootstrap.var">
-<span class="sig-prename descclassname"><span class="pre">CircularBlockBootstrap.</span></span><span class="sig-name descname"><span class="pre">var</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.CircularBlockBootstrap.var.func" title="arch.bootstrap.CircularBlockBootstrap.var.func (Python parameter) — Callable function that returns the statistic of interest as a 1-d array"><span class="n"><span class="pre">func</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.12)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="p"><span class="pre">]</span></span></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.CircularBlockBootstrap.var.reps" title="arch.bootstrap.CircularBlockBootstrap.var.reps (Python parameter) — Number of bootstrap replications"><span class="n"><span class="pre">reps</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.CircularBlockBootstrap.var.recenter" title="arch.bootstrap.CircularBlockBootstrap.var.recenter (Python parameter) — Whether to center the bootstrap variance estimator on the average of the bootstrap samples (True) or to center on the original sample estimate (False).  Default is True."><span class="n"><span class="pre">recenter</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.CircularBlockBootstrap.var.extra_kwargs" title="arch.bootstrap.CircularBlockBootstrap.var.extra_kwargs (Python parameter) — Dictionary of extra keyword arguments to pass to func"><span class="n"><span class="pre">extra_kwargs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.12)"><span class="pre">Any</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="headerlink" href="#arch.bootstrap.CircularBlockBootstrap.var" title="Link to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">CircularBlockBootstrap.</span></span><span class="sig-name descname"><span class="pre">var</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.CircularBlockBootstrap.var.func" title="arch.bootstrap.CircularBlockBootstrap.var.func (Python parameter) — Callable function that returns the statistic of interest as a 1-d array"><span class="n"><span class="pre">func</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.12)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a><span class="p"><span class="pre">]</span></span></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.CircularBlockBootstrap.var.reps" title="arch.bootstrap.CircularBlockBootstrap.var.reps (Python parameter) — Number of bootstrap replications"><span class="n"><span class="pre">reps</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.CircularBlockBootstrap.var.recenter" title="arch.bootstrap.CircularBlockBootstrap.var.recenter (Python parameter) — Whether to center the bootstrap variance estimator on the average of the bootstrap samples (True) or to center on the original sample estimate (False).  Default is True."><span class="n"><span class="pre">recenter</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.CircularBlockBootstrap.var.extra_kwargs" title="arch.bootstrap.CircularBlockBootstrap.var.extra_kwargs (Python parameter) — Dictionary of extra keyword arguments to pass to func"><span class="n"><span class="pre">extra_kwargs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.12)"><span class="pre">Any</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="headerlink" href="#arch.bootstrap.CircularBlockBootstrap.var" title="Link to this definition">¶</a></dt>
 <dd><p>Compute parameter variance using bootstrap</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.bootstrap.CircularBlockBootstrap.var-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.CircularBlockBootstrap.var-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.bootstrap.CircularBlockBootstrap.var.func"><span class="n sig-name">func</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.12)">Callable</a><span class="p">[</span><span class="p">[</span><span class="p">...</span><span class="p">]</span><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a><span class="p">]</span></span></span><a class="headerlink" href="#arch.bootstrap.CircularBlockBootstrap.var.func" title="Permalink to this definition">¶</a></dt><dd><p>Callable function that returns the statistic of interest as a
+<dt id="arch.bootstrap.CircularBlockBootstrap.var.func"><span class="n sig-name">func</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.12)">Callable</a><span class="p">[</span><span class="p">[</span><span class="p">...</span><span class="p">]</span><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a><span class="p">]</span></span></span><a class="headerlink" href="#arch.bootstrap.CircularBlockBootstrap.var.func" title="Permalink to this definition">¶</a></dt><dd><p>Callable function that returns the statistic of interest as a
 1-d array</p>
 </dd>
 <dt id="arch.bootstrap.CircularBlockBootstrap.var.reps"><span class="n sig-name">reps</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code><a class="headerlink" href="#arch.bootstrap.CircularBlockBootstrap.var.reps" title="Permalink to this definition">¶</a></dt><dd><p>Number of bootstrap replications</p>
@@ -1274,7 +1274,7 @@ <h1 id="arch-bootstrap-circularblockbootstrap-var">arch.bootstrap.CircularBlockB
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.apply.html b/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.apply.html
index 769a63fc37..acd85a35e6 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.apply.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.apply.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.IIDBootstrap.apply - arch v6.2.1 (+21)</title>
+        <title>arch.bootstrap.IIDBootstrap.apply - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -1039,12 +1039,12 @@
 <h1 id="arch-bootstrap-iidbootstrap-apply">arch.bootstrap.IIDBootstrap.apply<a class="headerlink" href="#arch-bootstrap-iidbootstrap-apply" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.bootstrap.IIDBootstrap.apply">
-<span class="sig-prename descclassname"><span class="pre">IIDBootstrap.</span></span><span class="sig-name descname"><span class="pre">apply</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IIDBootstrap.apply.func" title="arch.bootstrap.IIDBootstrap.apply.func (Python parameter) — Function the computes parameter values.  See Notes for requirements"><span class="n"><span class="pre">func</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.12)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="p"><span class="pre">]</span></span></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IIDBootstrap.apply.reps" title="arch.bootstrap.IIDBootstrap.apply.reps (Python parameter) — Number of bootstrap replications"><span class="n"><span class="pre">reps</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IIDBootstrap.apply.extra_kwargs" title="arch.bootstrap.IIDBootstrap.apply.extra_kwargs (Python parameter) — Extra keyword arguments to use when calling func.  Must not conflict with keyword arguments used to initialize bootstrap"><span class="n"><span class="pre">extra_kwargs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.12)"><span class="pre">Any</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/bootstrap/base.html#IIDBootstrap.apply"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.bootstrap.IIDBootstrap.apply" title="Link to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">IIDBootstrap.</span></span><span class="sig-name descname"><span class="pre">apply</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IIDBootstrap.apply.func" title="arch.bootstrap.IIDBootstrap.apply.func (Python parameter) — Function the computes parameter values.  See Notes for requirements"><span class="n"><span class="pre">func</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.12)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a><span class="p"><span class="pre">]</span></span></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IIDBootstrap.apply.reps" title="arch.bootstrap.IIDBootstrap.apply.reps (Python parameter) — Number of bootstrap replications"><span class="n"><span class="pre">reps</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IIDBootstrap.apply.extra_kwargs" title="arch.bootstrap.IIDBootstrap.apply.extra_kwargs (Python parameter) — Extra keyword arguments to use when calling func.  Must not conflict with keyword arguments used to initialize bootstrap"><span class="n"><span class="pre">extra_kwargs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.12)"><span class="pre">Any</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/bootstrap/base.html#IIDBootstrap.apply"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.bootstrap.IIDBootstrap.apply" title="Link to this definition">¶</a></dt>
 <dd><p>Applies a function to bootstrap replicated data</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.bootstrap.IIDBootstrap.apply-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.IIDBootstrap.apply-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.bootstrap.IIDBootstrap.apply.func"><span class="n sig-name">func</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.12)">Callable</a><span class="p">[</span><span class="p">[</span><span class="p">...</span><span class="p">]</span><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a><span class="p">]</span></span></span><a class="headerlink" href="#arch.bootstrap.IIDBootstrap.apply.func" title="Permalink to this definition">¶</a></dt><dd><p>Function the computes parameter values.  See Notes for requirements</p>
+<dt id="arch.bootstrap.IIDBootstrap.apply.func"><span class="n sig-name">func</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.12)">Callable</a><span class="p">[</span><span class="p">[</span><span class="p">...</span><span class="p">]</span><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a><span class="p">]</span></span></span><a class="headerlink" href="#arch.bootstrap.IIDBootstrap.apply.func" title="Permalink to this definition">¶</a></dt><dd><p>Function the computes parameter values.  See Notes for requirements</p>
 </dd>
 <dt id="arch.bootstrap.IIDBootstrap.apply.reps"><span class="n sig-name">reps</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code><a class="headerlink" href="#arch.bootstrap.IIDBootstrap.apply.reps" title="Permalink to this definition">¶</a></dt><dd><p>Number of bootstrap replications</p>
 </dd>
@@ -1195,7 +1195,7 @@ <h1 id="arch-bootstrap-iidbootstrap-apply">arch.bootstrap.IIDBootstrap.apply<a c
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.bootstrap.html b/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.bootstrap.html
index cc6010efa9..70bd0d62b4 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.bootstrap.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.bootstrap.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.IIDBootstrap.bootstrap - arch v6.2.1 (+21)</title>
+        <title>arch.bootstrap.IIDBootstrap.bootstrap - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -1011,7 +1011,7 @@
 <h1 id="arch-bootstrap-iidbootstrap-bootstrap">arch.bootstrap.IIDBootstrap.bootstrap<a class="headerlink" href="#arch-bootstrap-iidbootstrap-bootstrap" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.bootstrap.IIDBootstrap.bootstrap">
-<span class="sig-prename descclassname"><span class="pre">IIDBootstrap.</span></span><span class="sig-name descname"><span class="pre">bootstrap</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IIDBootstrap.bootstrap.reps" title="arch.bootstrap.IIDBootstrap.bootstrap.reps (Python parameter) — Number of bootstrap replications"><span class="n"><span class="pre">reps</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Generator" title="(in Python v3.12)"><span class="pre">Generator</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="p"><span class="pre">]</span></span></span></span></span><a class="reference internal" href="../../../_modules/arch/bootstrap/base.html#IIDBootstrap.bootstrap"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.bootstrap.IIDBootstrap.bootstrap" title="Link to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">IIDBootstrap.</span></span><span class="sig-name descname"><span class="pre">bootstrap</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IIDBootstrap.bootstrap.reps" title="arch.bootstrap.IIDBootstrap.bootstrap.reps (Python parameter) — Number of bootstrap replications"><span class="n"><span class="pre">reps</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Generator" title="(in Python v3.12)"><span class="pre">Generator</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="p"><span class="pre">]</span></span></span></span></span><a class="reference internal" href="../../../_modules/arch/bootstrap/base.html#IIDBootstrap.bootstrap"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.bootstrap.IIDBootstrap.bootstrap" title="Link to this definition">¶</a></dt>
 <dd><p>Iterator for use when bootstrapping</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.bootstrap.IIDBootstrap.bootstrap-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.IIDBootstrap.bootstrap-parameters" title="Permalink to this headline">¶</a></dt>
@@ -1163,7 +1163,7 @@ <h1 id="arch-bootstrap-iidbootstrap-bootstrap">arch.bootstrap.IIDBootstrap.boots
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.clone.html b/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.clone.html
index 56649d91c6..8257ee5125 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.clone.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.clone.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.IIDBootstrap.clone - arch v6.2.1 (+21)</title>
+        <title>arch.bootstrap.IIDBootstrap.clone - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -1175,7 +1175,7 @@ <h1 id="arch-bootstrap-iidbootstrap-clone">arch.bootstrap.IIDBootstrap.clone<a c
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.conf_int.html b/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.conf_int.html
index bc0a574cc1..5b149cb812 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.conf_int.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.conf_int.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.IIDBootstrap.conf_int - arch v6.2.1 (+21)</title>
+        <title>arch.bootstrap.IIDBootstrap.conf_int - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -1137,7 +1137,7 @@
 <h1 id="arch-bootstrap-iidbootstrap-conf-int">arch.bootstrap.IIDBootstrap.conf_int<a class="headerlink" href="#arch-bootstrap-iidbootstrap-conf-int" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.bootstrap.IIDBootstrap.conf_int">
-<span class="sig-prename descclassname"><span class="pre">IIDBootstrap.</span></span><span class="sig-name descname"><span class="pre">conf_int</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IIDBootstrap.conf_int.func" title="arch.bootstrap.IIDBootstrap.conf_int.func (Python parameter) — Function the computes parameter values.  See Notes for requirements"><span class="n"><span class="pre">func</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.12)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IIDBootstrap.conf_int.reps" title="arch.bootstrap.IIDBootstrap.conf_int.reps (Python parameter) — Number of bootstrap replications"><span class="n"><span class="pre">reps</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IIDBootstrap.conf_int.method" title="arch.bootstrap.IIDBootstrap.conf_int.method (Python parameter) — One of 'basic', 'percentile', 'studentized', 'norm' (identical to 'var', 'cov'), 'bc' (identical to 'debiased', 'bias-corrected'), or 'bca'"><span class="n"><span class="pre">method</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'basic'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'percentile'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'studentized'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'norm'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'bc'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'bca'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;basic&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IIDBootstrap.conf_int.size" title="arch.bootstrap.IIDBootstrap.conf_int.size (Python parameter) — Coverage of confidence interval"><span class="n"><span class="pre">size</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mf">0.95</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IIDBootstrap.conf_int.tail" title="arch.bootstrap.IIDBootstrap.conf_int.tail (Python parameter) — One of 'two', 'upper' or 'lower'."><span class="n"><span class="pre">tail</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'two'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'upper'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'lower'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;two&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IIDBootstrap.conf_int.extra_kwargs" title="arch.bootstrap.IIDBootstrap.conf_int.extra_kwargs (Python parameter) — Extra keyword arguments to use when calling func and std_err_func, when appropriate"><span class="n"><span class="pre">extra_kwargs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.12)"><span class="pre">Any</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IIDBootstrap.conf_int.reuse" title="arch.bootstrap.IIDBootstrap.conf_int.reuse (Python parameter) — Flag indicating whether to reuse previously computed bootstrap results.  This allows alternative methods to be compared without rerunning the bootstrap simulation.  Reuse is ignored if reps is not the same across multiple runs, func changes across calls, or method is 'studentized'."><span class="n"><span class="pre">reuse</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IIDBootstrap.conf_int.sampling" title="arch.bootstrap.IIDBootstrap.conf_int.sampling (Python parameter) — Type of sampling to use: 'nonparametric', 'semi-parametric' (or 'semi') or 'parametric'.  The default is 'nonparametric'.  See notes about the changes to func required when using 'semi' or 'parametric'."><span class="n"><span class="pre">sampling</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'nonparametric'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'semi-parametric'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'semi'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'parametric'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'semiparametric'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;nonparametric&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IIDBootstrap.conf_int.std_err_func" title="arch.bootstrap.IIDBootstrap.conf_int.std_err_func (Python parameter) — Function to use when standardizing estimated parameters when using the studentized bootstrap.  Providing an analytical function eliminates the need for a nested bootstrap"><span class="n"><span class="pre">std_err_func</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.12)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IIDBootstrap.conf_int.studentize_reps" title="arch.bootstrap.IIDBootstrap.conf_int.studentize_reps (Python parameter) — Number of bootstraps to use in the inner bootstrap when using the studentized bootstrap.  Ignored when std_err_func is provided"><span class="n"><span class="pre">studentize_reps</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/bootstrap/base.html#IIDBootstrap.conf_int"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.bootstrap.IIDBootstrap.conf_int" title="Link to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">IIDBootstrap.</span></span><span class="sig-name descname"><span class="pre">conf_int</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IIDBootstrap.conf_int.func" title="arch.bootstrap.IIDBootstrap.conf_int.func (Python parameter) — Function the computes parameter values.  See Notes for requirements"><span class="n"><span class="pre">func</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.12)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IIDBootstrap.conf_int.reps" title="arch.bootstrap.IIDBootstrap.conf_int.reps (Python parameter) — Number of bootstrap replications"><span class="n"><span class="pre">reps</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IIDBootstrap.conf_int.method" title="arch.bootstrap.IIDBootstrap.conf_int.method (Python parameter) — One of 'basic', 'percentile', 'studentized', 'norm' (identical to 'var', 'cov'), 'bc' (identical to 'debiased', 'bias-corrected'), or 'bca'"><span class="n"><span class="pre">method</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'basic'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'percentile'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'studentized'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'norm'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'bc'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'bca'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;basic&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IIDBootstrap.conf_int.size" title="arch.bootstrap.IIDBootstrap.conf_int.size (Python parameter) — Coverage of confidence interval"><span class="n"><span class="pre">size</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mf">0.95</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IIDBootstrap.conf_int.tail" title="arch.bootstrap.IIDBootstrap.conf_int.tail (Python parameter) — One of 'two', 'upper' or 'lower'."><span class="n"><span class="pre">tail</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'two'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'upper'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'lower'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;two&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IIDBootstrap.conf_int.extra_kwargs" title="arch.bootstrap.IIDBootstrap.conf_int.extra_kwargs (Python parameter) — Extra keyword arguments to use when calling func and std_err_func, when appropriate"><span class="n"><span class="pre">extra_kwargs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.12)"><span class="pre">Any</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IIDBootstrap.conf_int.reuse" title="arch.bootstrap.IIDBootstrap.conf_int.reuse (Python parameter) — Flag indicating whether to reuse previously computed bootstrap results.  This allows alternative methods to be compared without rerunning the bootstrap simulation.  Reuse is ignored if reps is not the same across multiple runs, func changes across calls, or method is 'studentized'."><span class="n"><span class="pre">reuse</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IIDBootstrap.conf_int.sampling" title="arch.bootstrap.IIDBootstrap.conf_int.sampling (Python parameter) — Type of sampling to use: 'nonparametric', 'semi-parametric' (or 'semi') or 'parametric'.  The default is 'nonparametric'.  See notes about the changes to func required when using 'semi' or 'parametric'."><span class="n"><span class="pre">sampling</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'nonparametric'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'semi-parametric'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'semi'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'parametric'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'semiparametric'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;nonparametric&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IIDBootstrap.conf_int.std_err_func" title="arch.bootstrap.IIDBootstrap.conf_int.std_err_func (Python parameter) — Function to use when standardizing estimated parameters when using the studentized bootstrap.  Providing an analytical function eliminates the need for a nested bootstrap"><span class="n"><span class="pre">std_err_func</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.12)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IIDBootstrap.conf_int.studentize_reps" title="arch.bootstrap.IIDBootstrap.conf_int.studentize_reps (Python parameter) — Number of bootstraps to use in the inner bootstrap when using the studentized bootstrap.  Ignored when std_err_func is provided"><span class="n"><span class="pre">studentize_reps</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/bootstrap/base.html#IIDBootstrap.conf_int"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.bootstrap.IIDBootstrap.conf_int" title="Link to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 <dt class="field-odd" id="arch.bootstrap.IIDBootstrap.conf_int-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.IIDBootstrap.conf_int-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
@@ -1167,7 +1167,7 @@ <h1 id="arch-bootstrap-iidbootstrap-conf-int">arch.bootstrap.IIDBootstrap.conf_i
 <dt id="arch.bootstrap.IIDBootstrap.conf_int.extra_kwargs"><span class="n sig-name">extra_kwargs</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)">dict</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)">str</a><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.12)">Any</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.bootstrap.IIDBootstrap.conf_int.extra_kwargs" title="Permalink to this definition">¶</a></dt><dd><p>Extra keyword arguments to use when calling func and std_err_func,
 when appropriate</p>
 </dd>
-<dt id="arch.bootstrap.IIDBootstrap.conf_int.std_err_func"><span class="n sig-name">std_err_func</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.12)">Callable</a><span class="p">[</span><span class="p">[</span><span class="p">...</span><span class="p">]</span><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.bootstrap.IIDBootstrap.conf_int.std_err_func" title="Permalink to this definition">¶</a></dt><dd><p>Function to use when standardizing estimated parameters when using
+<dt id="arch.bootstrap.IIDBootstrap.conf_int.std_err_func"><span class="n sig-name">std_err_func</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.12)">Callable</a><span class="p">[</span><span class="p">[</span><span class="p">...</span><span class="p">]</span><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.bootstrap.IIDBootstrap.conf_int.std_err_func" title="Permalink to this definition">¶</a></dt><dd><p>Function to use when standardizing estimated parameters when using
 the studentized bootstrap.  Providing an analytical function
 eliminates the need for a nested bootstrap</p>
 </dd>
@@ -1342,7 +1342,7 @@ <h1 id="arch-bootstrap-iidbootstrap-conf-int">arch.bootstrap.IIDBootstrap.conf_i
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diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.cov.html b/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.cov.html
index 40a3317424..03f79e2203 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.cov.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.cov.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.IIDBootstrap.cov - arch v6.2.1 (+21)</title>
+        <title>arch.bootstrap.IIDBootstrap.cov - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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     <div class="md-nav__source">
@@ -1053,12 +1053,12 @@
 <h1 id="arch-bootstrap-iidbootstrap-cov">arch.bootstrap.IIDBootstrap.cov<a class="headerlink" href="#arch-bootstrap-iidbootstrap-cov" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.bootstrap.IIDBootstrap.cov">
-<span class="sig-prename descclassname"><span class="pre">IIDBootstrap.</span></span><span class="sig-name descname"><span class="pre">cov</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IIDBootstrap.cov.func" title="arch.bootstrap.IIDBootstrap.cov.func (Python parameter) — Callable function that returns the statistic of interest as a 1-d array"><span class="n"><span class="pre">func</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.12)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="p"><span class="pre">]</span></span></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IIDBootstrap.cov.reps" title="arch.bootstrap.IIDBootstrap.cov.reps (Python parameter) — Number of bootstrap replications"><span class="n"><span class="pre">reps</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IIDBootstrap.cov.recenter" title="arch.bootstrap.IIDBootstrap.cov.recenter (Python parameter) — Whether to center the bootstrap variance estimator on the average of the bootstrap samples (True) or to center on the original sample estimate (False).  Default is True."><span class="n"><span class="pre">recenter</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IIDBootstrap.cov.extra_kwargs" title="arch.bootstrap.IIDBootstrap.cov.extra_kwargs (Python parameter) — Dictionary of extra keyword arguments to pass to func"><span class="n"><span class="pre">extra_kwargs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.12)"><span class="pre">Any</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/bootstrap/base.html#IIDBootstrap.cov"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.bootstrap.IIDBootstrap.cov" title="Link to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">IIDBootstrap.</span></span><span class="sig-name descname"><span class="pre">cov</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IIDBootstrap.cov.func" title="arch.bootstrap.IIDBootstrap.cov.func (Python parameter) — Callable function that returns the statistic of interest as a 1-d array"><span class="n"><span class="pre">func</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.12)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a><span class="p"><span class="pre">]</span></span></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IIDBootstrap.cov.reps" title="arch.bootstrap.IIDBootstrap.cov.reps (Python parameter) — Number of bootstrap replications"><span class="n"><span class="pre">reps</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IIDBootstrap.cov.recenter" title="arch.bootstrap.IIDBootstrap.cov.recenter (Python parameter) — Whether to center the bootstrap variance estimator on the average of the bootstrap samples (True) or to center on the original sample estimate (False).  Default is True."><span class="n"><span class="pre">recenter</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IIDBootstrap.cov.extra_kwargs" title="arch.bootstrap.IIDBootstrap.cov.extra_kwargs (Python parameter) — Dictionary of extra keyword arguments to pass to func"><span class="n"><span class="pre">extra_kwargs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.12)"><span class="pre">Any</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/bootstrap/base.html#IIDBootstrap.cov"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.bootstrap.IIDBootstrap.cov" title="Link to this definition">¶</a></dt>
 <dd><p>Compute parameter covariance using bootstrap</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.bootstrap.IIDBootstrap.cov-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.IIDBootstrap.cov-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.bootstrap.IIDBootstrap.cov.func"><span class="n sig-name">func</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.12)">Callable</a><span class="p">[</span><span class="p">[</span><span class="p">...</span><span class="p">]</span><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a><span class="p">]</span></span></span><a class="headerlink" href="#arch.bootstrap.IIDBootstrap.cov.func" title="Permalink to this definition">¶</a></dt><dd><p>Callable function that returns the statistic of interest as a
+<dt id="arch.bootstrap.IIDBootstrap.cov.func"><span class="n sig-name">func</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.12)">Callable</a><span class="p">[</span><span class="p">[</span><span class="p">...</span><span class="p">]</span><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a><span class="p">]</span></span></span><a class="headerlink" href="#arch.bootstrap.IIDBootstrap.cov.func" title="Permalink to this definition">¶</a></dt><dd><p>Callable function that returns the statistic of interest as a
 1-d array</p>
 </dd>
 <dt id="arch.bootstrap.IIDBootstrap.cov.reps"><span class="n sig-name">reps</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code><a class="headerlink" href="#arch.bootstrap.IIDBootstrap.cov.reps" title="Permalink to this definition">¶</a></dt><dd><p>Number of bootstrap replications</p>
@@ -1229,7 +1229,7 @@ <h1 id="arch-bootstrap-iidbootstrap-cov">arch.bootstrap.IIDBootstrap.cov<a class
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.generator.html b/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.generator.html
index 55ca6635da..3c0c5ea634 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.generator.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.generator.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.IIDBootstrap.generator - arch v6.2.1 (+21)</title>
+        <title>arch.bootstrap.IIDBootstrap.generator - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -1119,7 +1119,7 @@ <h1 id="arch-bootstrap-iidbootstrap-generator">arch.bootstrap.IIDBootstrap.gener
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.get_state.html b/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.get_state.html
index cc2e9c411a..8700813a8f 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.get_state.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.get_state.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.IIDBootstrap.get_state - arch v6.2.1 (+21)</title>
+        <title>arch.bootstrap.IIDBootstrap.get_state - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -1097,7 +1097,7 @@ <h1 id="arch-bootstrap-iidbootstrap-get-state">arch.bootstrap.IIDBootstrap.get_s
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.index.html b/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.index.html
index e7c294c43f..3e8132b416 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.index.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.index.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.IIDBootstrap.index - arch v6.2.1 (+21)</title>
+        <title>arch.bootstrap.IIDBootstrap.index - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -1049,7 +1049,7 @@ <h1 id="arch-bootstrap-iidbootstrap-index">arch.bootstrap.IIDBootstrap.index<a c
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.random_state.html b/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.random_state.html
index 6c20435dea..1da8cd999b 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.random_state.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.random_state.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.IIDBootstrap.random_state - arch v6.2.1 (+21)</title>
+        <title>arch.bootstrap.IIDBootstrap.random_state - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -1117,7 +1117,7 @@ <h1 id="arch-bootstrap-iidbootstrap-random-state">arch.bootstrap.IIDBootstrap.ra
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.reset.html b/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.reset.html
index 1b7edcd4d1..e913b5f824 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.reset.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.reset.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.IIDBootstrap.reset - arch v6.2.1 (+21)</title>
+        <title>arch.bootstrap.IIDBootstrap.reset - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -1111,7 +1111,7 @@ <h1 id="arch-bootstrap-iidbootstrap-reset">arch.bootstrap.IIDBootstrap.reset<a c
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.seed.html b/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.seed.html
index 379a8b145b..11fa19e1ea 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.seed.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.seed.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.IIDBootstrap.seed - arch v6.2.1 (+21)</title>
+        <title>arch.bootstrap.IIDBootstrap.seed - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -1109,7 +1109,7 @@ <h1 id="arch-bootstrap-iidbootstrap-seed">arch.bootstrap.IIDBootstrap.seed<a cla
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.set_state.html b/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.set_state.html
index 0bceae5a15..e8834de8aa 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.set_state.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.set_state.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.IIDBootstrap.set_state - arch v6.2.1 (+21)</title>
+        <title>arch.bootstrap.IIDBootstrap.set_state - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -1109,7 +1109,7 @@ <h1 id="arch-bootstrap-iidbootstrap-set-state">arch.bootstrap.IIDBootstrap.set_s
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.state.html b/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.state.html
index d031dfc8ef..1ba8538bb6 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.state.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.state.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.IIDBootstrap.state - arch v6.2.1 (+21)</title>
+        <title>arch.bootstrap.IIDBootstrap.state - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -1099,7 +1099,7 @@ <h1 id="arch-bootstrap-iidbootstrap-state">arch.bootstrap.IIDBootstrap.state<a c
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.update_indices.html b/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.update_indices.html
index a1763548c0..f5ed96a634 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.update_indices.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.update_indices.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.IIDBootstrap.update_indices - arch v6.2.1 (+21)</title>
+        <title>arch.bootstrap.IIDBootstrap.update_indices - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -1050,7 +1050,7 @@ <h1 id="arch-bootstrap-iidbootstrap-update-indices">arch.bootstrap.IIDBootstrap.
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.var.html b/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.var.html
index c3f8b56702..0a9396838c 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.var.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.IIDBootstrap.var.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.IIDBootstrap.var - arch v6.2.1 (+21)</title>
+        <title>arch.bootstrap.IIDBootstrap.var - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -1053,12 +1053,12 @@
 <h1 id="arch-bootstrap-iidbootstrap-var">arch.bootstrap.IIDBootstrap.var<a class="headerlink" href="#arch-bootstrap-iidbootstrap-var" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.bootstrap.IIDBootstrap.var">
-<span class="sig-prename descclassname"><span class="pre">IIDBootstrap.</span></span><span class="sig-name descname"><span class="pre">var</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IIDBootstrap.var.func" title="arch.bootstrap.IIDBootstrap.var.func (Python parameter) — Callable function that returns the statistic of interest as a 1-d array"><span class="n"><span class="pre">func</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.12)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="p"><span class="pre">]</span></span></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IIDBootstrap.var.reps" title="arch.bootstrap.IIDBootstrap.var.reps (Python parameter) — Number of bootstrap replications"><span class="n"><span class="pre">reps</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IIDBootstrap.var.recenter" title="arch.bootstrap.IIDBootstrap.var.recenter (Python parameter) — Whether to center the bootstrap variance estimator on the average of the bootstrap samples (True) or to center on the original sample estimate (False).  Default is True."><span class="n"><span class="pre">recenter</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IIDBootstrap.var.extra_kwargs" title="arch.bootstrap.IIDBootstrap.var.extra_kwargs (Python parameter) — Dictionary of extra keyword arguments to pass to func"><span class="n"><span class="pre">extra_kwargs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.12)"><span class="pre">Any</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/bootstrap/base.html#IIDBootstrap.var"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.bootstrap.IIDBootstrap.var" title="Link to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">IIDBootstrap.</span></span><span class="sig-name descname"><span class="pre">var</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IIDBootstrap.var.func" title="arch.bootstrap.IIDBootstrap.var.func (Python parameter) — Callable function that returns the statistic of interest as a 1-d array"><span class="n"><span class="pre">func</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.12)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a><span class="p"><span class="pre">]</span></span></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IIDBootstrap.var.reps" title="arch.bootstrap.IIDBootstrap.var.reps (Python parameter) — Number of bootstrap replications"><span class="n"><span class="pre">reps</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IIDBootstrap.var.recenter" title="arch.bootstrap.IIDBootstrap.var.recenter (Python parameter) — Whether to center the bootstrap variance estimator on the average of the bootstrap samples (True) or to center on the original sample estimate (False).  Default is True."><span class="n"><span class="pre">recenter</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IIDBootstrap.var.extra_kwargs" title="arch.bootstrap.IIDBootstrap.var.extra_kwargs (Python parameter) — Dictionary of extra keyword arguments to pass to func"><span class="n"><span class="pre">extra_kwargs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.12)"><span class="pre">Any</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/bootstrap/base.html#IIDBootstrap.var"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.bootstrap.IIDBootstrap.var" title="Link to this definition">¶</a></dt>
 <dd><p>Compute parameter variance using bootstrap</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.bootstrap.IIDBootstrap.var-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.IIDBootstrap.var-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.bootstrap.IIDBootstrap.var.func"><span class="n sig-name">func</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.12)">Callable</a><span class="p">[</span><span class="p">[</span><span class="p">...</span><span class="p">]</span><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a><span class="p">]</span></span></span><a class="headerlink" href="#arch.bootstrap.IIDBootstrap.var.func" title="Permalink to this definition">¶</a></dt><dd><p>Callable function that returns the statistic of interest as a
+<dt id="arch.bootstrap.IIDBootstrap.var.func"><span class="n sig-name">func</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.12)">Callable</a><span class="p">[</span><span class="p">[</span><span class="p">...</span><span class="p">]</span><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a><span class="p">]</span></span></span><a class="headerlink" href="#arch.bootstrap.IIDBootstrap.var.func" title="Permalink to this definition">¶</a></dt><dd><p>Callable function that returns the statistic of interest as a
 1-d array</p>
 </dd>
 <dt id="arch.bootstrap.IIDBootstrap.var.reps"><span class="n sig-name">reps</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code><a class="headerlink" href="#arch.bootstrap.IIDBootstrap.var.reps" title="Permalink to this definition">¶</a></dt><dd><p>Number of bootstrap replications</p>
@@ -1229,7 +1229,7 @@ <h1 id="arch-bootstrap-iidbootstrap-var">arch.bootstrap.IIDBootstrap.var<a class
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.apply.html b/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.apply.html
index 18aa0b33f9..5ab57f01f5 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.apply.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.apply.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.IndependentSamplesBootstrap.apply - arch v6.2.1 (+21)</title>
+        <title>arch.bootstrap.IndependentSamplesBootstrap.apply - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
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+    arch v6.2.1 (+23)
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@@ -1039,12 +1039,12 @@
 <h1 id="arch-bootstrap-independentsamplesbootstrap-apply">arch.bootstrap.IndependentSamplesBootstrap.apply<a class="headerlink" href="#arch-bootstrap-independentsamplesbootstrap-apply" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.bootstrap.IndependentSamplesBootstrap.apply">
-<span class="sig-prename descclassname"><span class="pre">IndependentSamplesBootstrap.</span></span><span class="sig-name descname"><span class="pre">apply</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IndependentSamplesBootstrap.apply.func" title="arch.bootstrap.IndependentSamplesBootstrap.apply.func (Python parameter) — Function the computes parameter values.  See Notes for requirements"><span class="n"><span class="pre">func</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.12)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="p"><span class="pre">]</span></span></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IndependentSamplesBootstrap.apply.reps" title="arch.bootstrap.IndependentSamplesBootstrap.apply.reps (Python parameter) — Number of bootstrap replications"><span class="n"><span class="pre">reps</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IndependentSamplesBootstrap.apply.extra_kwargs" title="arch.bootstrap.IndependentSamplesBootstrap.apply.extra_kwargs (Python parameter) — Extra keyword arguments to use when calling func.  Must not conflict with keyword arguments used to initialize bootstrap"><span class="n"><span class="pre">extra_kwargs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.12)"><span class="pre">Any</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="headerlink" href="#arch.bootstrap.IndependentSamplesBootstrap.apply" title="Link to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">IndependentSamplesBootstrap.</span></span><span class="sig-name descname"><span class="pre">apply</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IndependentSamplesBootstrap.apply.func" title="arch.bootstrap.IndependentSamplesBootstrap.apply.func (Python parameter) — Function the computes parameter values.  See Notes for requirements"><span class="n"><span class="pre">func</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.12)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a><span class="p"><span class="pre">]</span></span></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IndependentSamplesBootstrap.apply.reps" title="arch.bootstrap.IndependentSamplesBootstrap.apply.reps (Python parameter) — Number of bootstrap replications"><span class="n"><span class="pre">reps</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IndependentSamplesBootstrap.apply.extra_kwargs" title="arch.bootstrap.IndependentSamplesBootstrap.apply.extra_kwargs (Python parameter) — Extra keyword arguments to use when calling func.  Must not conflict with keyword arguments used to initialize bootstrap"><span class="n"><span class="pre">extra_kwargs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.12)"><span class="pre">Any</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="headerlink" href="#arch.bootstrap.IndependentSamplesBootstrap.apply" title="Link to this definition">¶</a></dt>
 <dd><p>Applies a function to bootstrap replicated data</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.bootstrap.IndependentSamplesBootstrap.apply-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.IndependentSamplesBootstrap.apply-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.bootstrap.IndependentSamplesBootstrap.apply.func"><span class="n sig-name">func</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.12)">Callable</a><span class="p">[</span><span class="p">[</span><span class="p">...</span><span class="p">]</span><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a><span class="p">]</span></span></span><a class="headerlink" href="#arch.bootstrap.IndependentSamplesBootstrap.apply.func" title="Permalink to this definition">¶</a></dt><dd><p>Function the computes parameter values.  See Notes for requirements</p>
+<dt id="arch.bootstrap.IndependentSamplesBootstrap.apply.func"><span class="n sig-name">func</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.12)">Callable</a><span class="p">[</span><span class="p">[</span><span class="p">...</span><span class="p">]</span><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a><span class="p">]</span></span></span><a class="headerlink" href="#arch.bootstrap.IndependentSamplesBootstrap.apply.func" title="Permalink to this definition">¶</a></dt><dd><p>Function the computes parameter values.  See Notes for requirements</p>
 </dd>
 <dt id="arch.bootstrap.IndependentSamplesBootstrap.apply.reps"><span class="n sig-name">reps</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code><a class="headerlink" href="#arch.bootstrap.IndependentSamplesBootstrap.apply.reps" title="Permalink to this definition">¶</a></dt><dd><p>Number of bootstrap replications</p>
 </dd>
@@ -1195,7 +1195,7 @@ <h1 id="arch-bootstrap-independentsamplesbootstrap-apply">arch.bootstrap.Indepen
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
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   </body>
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.bootstrap.html b/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.bootstrap.html
index d58cdc25de..ff0e9bbdb4 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.bootstrap.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.bootstrap.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.IndependentSamplesBootstrap.bootstrap - arch v6.2.1 (+21)</title>
+        <title>arch.bootstrap.IndependentSamplesBootstrap.bootstrap - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -1011,7 +1011,7 @@
 <h1 id="arch-bootstrap-independentsamplesbootstrap-bootstrap">arch.bootstrap.IndependentSamplesBootstrap.bootstrap<a class="headerlink" href="#arch-bootstrap-independentsamplesbootstrap-bootstrap" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.bootstrap.IndependentSamplesBootstrap.bootstrap">
-<span class="sig-prename descclassname"><span class="pre">IndependentSamplesBootstrap.</span></span><span class="sig-name descname"><span class="pre">bootstrap</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IndependentSamplesBootstrap.bootstrap.reps" title="arch.bootstrap.IndependentSamplesBootstrap.bootstrap.reps (Python parameter) — Number of bootstrap replications"><span class="n"><span class="pre">reps</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Generator" title="(in Python v3.12)"><span class="pre">Generator</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="p"><span class="pre">]</span></span></span></span></span><a class="headerlink" href="#arch.bootstrap.IndependentSamplesBootstrap.bootstrap" title="Link to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">IndependentSamplesBootstrap.</span></span><span class="sig-name descname"><span class="pre">bootstrap</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IndependentSamplesBootstrap.bootstrap.reps" title="arch.bootstrap.IndependentSamplesBootstrap.bootstrap.reps (Python parameter) — Number of bootstrap replications"><span class="n"><span class="pre">reps</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Generator" title="(in Python v3.12)"><span class="pre">Generator</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="p"><span class="pre">]</span></span></span></span></span><a class="headerlink" href="#arch.bootstrap.IndependentSamplesBootstrap.bootstrap" title="Link to this definition">¶</a></dt>
 <dd><p>Iterator for use when bootstrapping</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.bootstrap.IndependentSamplesBootstrap.bootstrap-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.IndependentSamplesBootstrap.bootstrap-parameters" title="Permalink to this headline">¶</a></dt>
@@ -1163,7 +1163,7 @@ <h1 id="arch-bootstrap-independentsamplesbootstrap-bootstrap">arch.bootstrap.Ind
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.clone.html b/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.clone.html
index 2ac7b8c4e5..e789f526a9 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.clone.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.clone.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.IndependentSamplesBootstrap.clone - arch v6.2.1 (+21)</title>
+        <title>arch.bootstrap.IndependentSamplesBootstrap.clone - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -1175,7 +1175,7 @@ <h1 id="arch-bootstrap-independentsamplesbootstrap-clone">arch.bootstrap.Indepen
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.conf_int.html b/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.conf_int.html
index 86c25f3d81..cadf9d15c8 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.conf_int.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.conf_int.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.IndependentSamplesBootstrap.conf_int - arch v6.2.1 (+21)</title>
+        <title>arch.bootstrap.IndependentSamplesBootstrap.conf_int - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -1137,7 +1137,7 @@
 <h1 id="arch-bootstrap-independentsamplesbootstrap-conf-int">arch.bootstrap.IndependentSamplesBootstrap.conf_int<a class="headerlink" href="#arch-bootstrap-independentsamplesbootstrap-conf-int" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.bootstrap.IndependentSamplesBootstrap.conf_int">
-<span class="sig-prename descclassname"><span class="pre">IndependentSamplesBootstrap.</span></span><span class="sig-name descname"><span class="pre">conf_int</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IndependentSamplesBootstrap.conf_int.func" title="arch.bootstrap.IndependentSamplesBootstrap.conf_int.func (Python parameter) — Function the computes parameter values.  See Notes for requirements"><span class="n"><span class="pre">func</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.12)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IndependentSamplesBootstrap.conf_int.reps" title="arch.bootstrap.IndependentSamplesBootstrap.conf_int.reps (Python parameter) — Number of bootstrap replications"><span class="n"><span class="pre">reps</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IndependentSamplesBootstrap.conf_int.method" title="arch.bootstrap.IndependentSamplesBootstrap.conf_int.method (Python parameter) — One of 'basic', 'percentile', 'studentized', 'norm' (identical to 'var', 'cov'), 'bc' (identical to 'debiased', 'bias-corrected'), or 'bca'"><span class="n"><span class="pre">method</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'basic'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'percentile'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'studentized'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'norm'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'bc'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'bca'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;basic&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IndependentSamplesBootstrap.conf_int.size" title="arch.bootstrap.IndependentSamplesBootstrap.conf_int.size (Python parameter) — Coverage of confidence interval"><span class="n"><span class="pre">size</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mf">0.95</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IndependentSamplesBootstrap.conf_int.tail" title="arch.bootstrap.IndependentSamplesBootstrap.conf_int.tail (Python parameter) — One of 'two', 'upper' or 'lower'."><span class="n"><span class="pre">tail</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'two'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'upper'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'lower'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;two&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IndependentSamplesBootstrap.conf_int.extra_kwargs" title="arch.bootstrap.IndependentSamplesBootstrap.conf_int.extra_kwargs (Python parameter) — Extra keyword arguments to use when calling func and std_err_func, when appropriate"><span class="n"><span class="pre">extra_kwargs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.12)"><span class="pre">Any</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IndependentSamplesBootstrap.conf_int.reuse" title="arch.bootstrap.IndependentSamplesBootstrap.conf_int.reuse (Python parameter) — Flag indicating whether to reuse previously computed bootstrap results.  This allows alternative methods to be compared without rerunning the bootstrap simulation.  Reuse is ignored if reps is not the same across multiple runs, func changes across calls, or method is 'studentized'."><span class="n"><span class="pre">reuse</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IndependentSamplesBootstrap.conf_int.sampling" title="arch.bootstrap.IndependentSamplesBootstrap.conf_int.sampling (Python parameter) — Type of sampling to use: 'nonparametric', 'semi-parametric' (or 'semi') or 'parametric'.  The default is 'nonparametric'.  See notes about the changes to func required when using 'semi' or 'parametric'."><span class="n"><span class="pre">sampling</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'nonparametric'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'semi-parametric'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'semi'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'parametric'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'semiparametric'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;nonparametric&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IndependentSamplesBootstrap.conf_int.std_err_func" title="arch.bootstrap.IndependentSamplesBootstrap.conf_int.std_err_func (Python parameter) — Function to use when standardizing estimated parameters when using the studentized bootstrap.  Providing an analytical function eliminates the need for a nested bootstrap"><span class="n"><span class="pre">std_err_func</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.12)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IndependentSamplesBootstrap.conf_int.studentize_reps" title="arch.bootstrap.IndependentSamplesBootstrap.conf_int.studentize_reps (Python parameter) — Number of bootstraps to use in the inner bootstrap when using the studentized bootstrap.  Ignored when std_err_func is provided"><span class="n"><span class="pre">studentize_reps</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="headerlink" href="#arch.bootstrap.IndependentSamplesBootstrap.conf_int" title="Link to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">IndependentSamplesBootstrap.</span></span><span class="sig-name descname"><span class="pre">conf_int</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IndependentSamplesBootstrap.conf_int.func" title="arch.bootstrap.IndependentSamplesBootstrap.conf_int.func (Python parameter) — Function the computes parameter values.  See Notes for requirements"><span class="n"><span class="pre">func</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.12)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IndependentSamplesBootstrap.conf_int.reps" title="arch.bootstrap.IndependentSamplesBootstrap.conf_int.reps (Python parameter) — Number of bootstrap replications"><span class="n"><span class="pre">reps</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IndependentSamplesBootstrap.conf_int.method" title="arch.bootstrap.IndependentSamplesBootstrap.conf_int.method (Python parameter) — One of 'basic', 'percentile', 'studentized', 'norm' (identical to 'var', 'cov'), 'bc' (identical to 'debiased', 'bias-corrected'), or 'bca'"><span class="n"><span class="pre">method</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'basic'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'percentile'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'studentized'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'norm'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'bc'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'bca'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;basic&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IndependentSamplesBootstrap.conf_int.size" title="arch.bootstrap.IndependentSamplesBootstrap.conf_int.size (Python parameter) — Coverage of confidence interval"><span class="n"><span class="pre">size</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mf">0.95</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IndependentSamplesBootstrap.conf_int.tail" title="arch.bootstrap.IndependentSamplesBootstrap.conf_int.tail (Python parameter) — One of 'two', 'upper' or 'lower'."><span class="n"><span class="pre">tail</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'two'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'upper'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'lower'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;two&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IndependentSamplesBootstrap.conf_int.extra_kwargs" title="arch.bootstrap.IndependentSamplesBootstrap.conf_int.extra_kwargs (Python parameter) — Extra keyword arguments to use when calling func and std_err_func, when appropriate"><span class="n"><span class="pre">extra_kwargs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.12)"><span class="pre">Any</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IndependentSamplesBootstrap.conf_int.reuse" title="arch.bootstrap.IndependentSamplesBootstrap.conf_int.reuse (Python parameter) — Flag indicating whether to reuse previously computed bootstrap results.  This allows alternative methods to be compared without rerunning the bootstrap simulation.  Reuse is ignored if reps is not the same across multiple runs, func changes across calls, or method is 'studentized'."><span class="n"><span class="pre">reuse</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IndependentSamplesBootstrap.conf_int.sampling" title="arch.bootstrap.IndependentSamplesBootstrap.conf_int.sampling (Python parameter) — Type of sampling to use: 'nonparametric', 'semi-parametric' (or 'semi') or 'parametric'.  The default is 'nonparametric'.  See notes about the changes to func required when using 'semi' or 'parametric'."><span class="n"><span class="pre">sampling</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'nonparametric'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'semi-parametric'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'semi'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'parametric'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'semiparametric'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;nonparametric&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IndependentSamplesBootstrap.conf_int.std_err_func" title="arch.bootstrap.IndependentSamplesBootstrap.conf_int.std_err_func (Python parameter) — Function to use when standardizing estimated parameters when using the studentized bootstrap.  Providing an analytical function eliminates the need for a nested bootstrap"><span class="n"><span class="pre">std_err_func</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.12)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IndependentSamplesBootstrap.conf_int.studentize_reps" title="arch.bootstrap.IndependentSamplesBootstrap.conf_int.studentize_reps (Python parameter) — Number of bootstraps to use in the inner bootstrap when using the studentized bootstrap.  Ignored when std_err_func is provided"><span class="n"><span class="pre">studentize_reps</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="headerlink" href="#arch.bootstrap.IndependentSamplesBootstrap.conf_int" title="Link to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 <dt class="field-odd" id="arch.bootstrap.IndependentSamplesBootstrap.conf_int-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.IndependentSamplesBootstrap.conf_int-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
@@ -1167,7 +1167,7 @@ <h1 id="arch-bootstrap-independentsamplesbootstrap-conf-int">arch.bootstrap.Inde
 <dt id="arch.bootstrap.IndependentSamplesBootstrap.conf_int.extra_kwargs"><span class="n sig-name">extra_kwargs</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)">dict</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)">str</a><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.12)">Any</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.bootstrap.IndependentSamplesBootstrap.conf_int.extra_kwargs" title="Permalink to this definition">¶</a></dt><dd><p>Extra keyword arguments to use when calling func and std_err_func,
 when appropriate</p>
 </dd>
-<dt id="arch.bootstrap.IndependentSamplesBootstrap.conf_int.std_err_func"><span class="n sig-name">std_err_func</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.12)">Callable</a><span class="p">[</span><span class="p">[</span><span class="p">...</span><span class="p">]</span><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.bootstrap.IndependentSamplesBootstrap.conf_int.std_err_func" title="Permalink to this definition">¶</a></dt><dd><p>Function to use when standardizing estimated parameters when using
+<dt id="arch.bootstrap.IndependentSamplesBootstrap.conf_int.std_err_func"><span class="n sig-name">std_err_func</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.12)">Callable</a><span class="p">[</span><span class="p">[</span><span class="p">...</span><span class="p">]</span><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.bootstrap.IndependentSamplesBootstrap.conf_int.std_err_func" title="Permalink to this definition">¶</a></dt><dd><p>Function to use when standardizing estimated parameters when using
 the studentized bootstrap.  Providing an analytical function
 eliminates the need for a nested bootstrap</p>
 </dd>
@@ -1342,7 +1342,7 @@ <h1 id="arch-bootstrap-independentsamplesbootstrap-conf-int">arch.bootstrap.Inde
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diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.cov.html b/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.cov.html
index 936fbde075..07211843aa 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.cov.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.cov.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.IndependentSamplesBootstrap.cov - arch v6.2.1 (+21)</title>
+        <title>arch.bootstrap.IndependentSamplesBootstrap.cov - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
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+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
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@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
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-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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@@ -1053,12 +1053,12 @@
 <h1 id="arch-bootstrap-independentsamplesbootstrap-cov">arch.bootstrap.IndependentSamplesBootstrap.cov<a class="headerlink" href="#arch-bootstrap-independentsamplesbootstrap-cov" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.bootstrap.IndependentSamplesBootstrap.cov">
-<span class="sig-prename descclassname"><span class="pre">IndependentSamplesBootstrap.</span></span><span class="sig-name descname"><span class="pre">cov</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IndependentSamplesBootstrap.cov.func" title="arch.bootstrap.IndependentSamplesBootstrap.cov.func (Python parameter) — Callable function that returns the statistic of interest as a 1-d array"><span class="n"><span class="pre">func</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.12)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="p"><span class="pre">]</span></span></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IndependentSamplesBootstrap.cov.reps" title="arch.bootstrap.IndependentSamplesBootstrap.cov.reps (Python parameter) — Number of bootstrap replications"><span class="n"><span class="pre">reps</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IndependentSamplesBootstrap.cov.recenter" title="arch.bootstrap.IndependentSamplesBootstrap.cov.recenter (Python parameter) — Whether to center the bootstrap variance estimator on the average of the bootstrap samples (True) or to center on the original sample estimate (False).  Default is True."><span class="n"><span class="pre">recenter</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IndependentSamplesBootstrap.cov.extra_kwargs" title="arch.bootstrap.IndependentSamplesBootstrap.cov.extra_kwargs (Python parameter) — Dictionary of extra keyword arguments to pass to func"><span class="n"><span class="pre">extra_kwargs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.12)"><span class="pre">Any</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="headerlink" href="#arch.bootstrap.IndependentSamplesBootstrap.cov" title="Link to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">IndependentSamplesBootstrap.</span></span><span class="sig-name descname"><span class="pre">cov</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IndependentSamplesBootstrap.cov.func" title="arch.bootstrap.IndependentSamplesBootstrap.cov.func (Python parameter) — Callable function that returns the statistic of interest as a 1-d array"><span class="n"><span class="pre">func</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.12)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a><span class="p"><span class="pre">]</span></span></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IndependentSamplesBootstrap.cov.reps" title="arch.bootstrap.IndependentSamplesBootstrap.cov.reps (Python parameter) — Number of bootstrap replications"><span class="n"><span class="pre">reps</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IndependentSamplesBootstrap.cov.recenter" title="arch.bootstrap.IndependentSamplesBootstrap.cov.recenter (Python parameter) — Whether to center the bootstrap variance estimator on the average of the bootstrap samples (True) or to center on the original sample estimate (False).  Default is True."><span class="n"><span class="pre">recenter</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IndependentSamplesBootstrap.cov.extra_kwargs" title="arch.bootstrap.IndependentSamplesBootstrap.cov.extra_kwargs (Python parameter) — Dictionary of extra keyword arguments to pass to func"><span class="n"><span class="pre">extra_kwargs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.12)"><span class="pre">Any</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="headerlink" href="#arch.bootstrap.IndependentSamplesBootstrap.cov" title="Link to this definition">¶</a></dt>
 <dd><p>Compute parameter covariance using bootstrap</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.bootstrap.IndependentSamplesBootstrap.cov-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.IndependentSamplesBootstrap.cov-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.bootstrap.IndependentSamplesBootstrap.cov.func"><span class="n sig-name">func</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.12)">Callable</a><span class="p">[</span><span class="p">[</span><span class="p">...</span><span class="p">]</span><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a><span class="p">]</span></span></span><a class="headerlink" href="#arch.bootstrap.IndependentSamplesBootstrap.cov.func" title="Permalink to this definition">¶</a></dt><dd><p>Callable function that returns the statistic of interest as a
+<dt id="arch.bootstrap.IndependentSamplesBootstrap.cov.func"><span class="n sig-name">func</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.12)">Callable</a><span class="p">[</span><span class="p">[</span><span class="p">...</span><span class="p">]</span><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a><span class="p">]</span></span></span><a class="headerlink" href="#arch.bootstrap.IndependentSamplesBootstrap.cov.func" title="Permalink to this definition">¶</a></dt><dd><p>Callable function that returns the statistic of interest as a
 1-d array</p>
 </dd>
 <dt id="arch.bootstrap.IndependentSamplesBootstrap.cov.reps"><span class="n sig-name">reps</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code><a class="headerlink" href="#arch.bootstrap.IndependentSamplesBootstrap.cov.reps" title="Permalink to this definition">¶</a></dt><dd><p>Number of bootstrap replications</p>
@@ -1229,7 +1229,7 @@ <h1 id="arch-bootstrap-independentsamplesbootstrap-cov">arch.bootstrap.Independe
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.generator.html b/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.generator.html
index f70721d446..f40fe6788d 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.generator.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.generator.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.IndependentSamplesBootstrap.generator - arch v6.2.1 (+21)</title>
+        <title>arch.bootstrap.IndependentSamplesBootstrap.generator - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -1119,7 +1119,7 @@ <h1 id="arch-bootstrap-independentsamplesbootstrap-generator">arch.bootstrap.Ind
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.get_state.html b/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.get_state.html
index bf3aaecb7e..aded3317d4 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.get_state.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.get_state.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.IndependentSamplesBootstrap.get_state - arch v6.2.1 (+21)</title>
+        <title>arch.bootstrap.IndependentSamplesBootstrap.get_state - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -1097,7 +1097,7 @@ <h1 id="arch-bootstrap-independentsamplesbootstrap-get-state">arch.bootstrap.Ind
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.index.html b/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.index.html
index 83c99ced41..af066eeb0b 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.index.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.index.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.IndependentSamplesBootstrap.index - arch v6.2.1 (+21)</title>
+        <title>arch.bootstrap.IndependentSamplesBootstrap.index - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -1099,7 +1099,7 @@ <h1 id="arch-bootstrap-independentsamplesbootstrap-index">arch.bootstrap.Indepen
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.random_state.html b/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.random_state.html
index 4246423db7..5ee286fc31 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.random_state.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.random_state.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.IndependentSamplesBootstrap.random_state - arch v6.2.1 (+21)</title>
+        <title>arch.bootstrap.IndependentSamplesBootstrap.random_state - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -985,7 +985,7 @@
 <h1 id="arch-bootstrap-independentsamplesbootstrap-random-state">arch.bootstrap.IndependentSamplesBootstrap.random_state<a class="headerlink" href="#arch-bootstrap-independentsamplesbootstrap-random-state" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.bootstrap.IndependentSamplesBootstrap.random_state">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">IndependentSamplesBootstrap.</span></span><span class="sig-name descname"><span class="pre">random_state</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><span class="pre">Generator</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">RandomState</span></span><a class="headerlink" href="#arch.bootstrap.IndependentSamplesBootstrap.random_state" title="Link to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">IndependentSamplesBootstrap.</span></span><span class="sig-name descname"><span class="pre">random_state</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.26)"><span class="pre">Generator</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.26)"><span class="pre">RandomState</span></a></span><a class="headerlink" href="#arch.bootstrap.IndependentSamplesBootstrap.random_state" title="Link to this definition">¶</a></dt>
 <dd><p>Set or get the instance random state</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.bootstrap.IndependentSamplesBootstrap.random_state-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.IndependentSamplesBootstrap.random_state-parameters" title="Permalink to this headline">¶</a></dt>
@@ -1117,7 +1117,7 @@ <h1 id="arch-bootstrap-independentsamplesbootstrap-random-state">arch.bootstrap.
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.reset.html b/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.reset.html
index 0dd201905e..5bb12da980 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.reset.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.reset.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.IndependentSamplesBootstrap.reset - arch v6.2.1 (+21)</title>
+        <title>arch.bootstrap.IndependentSamplesBootstrap.reset - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -1111,7 +1111,7 @@ <h1 id="arch-bootstrap-independentsamplesbootstrap-reset">arch.bootstrap.Indepen
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.seed.html b/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.seed.html
index b6e55452d2..9b116cc983 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.seed.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.seed.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.IndependentSamplesBootstrap.seed - arch v6.2.1 (+21)</title>
+        <title>arch.bootstrap.IndependentSamplesBootstrap.seed - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -1109,7 +1109,7 @@ <h1 id="arch-bootstrap-independentsamplesbootstrap-seed">arch.bootstrap.Independ
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.set_state.html b/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.set_state.html
index f234de8bae..5050f692bf 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.set_state.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.set_state.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.IndependentSamplesBootstrap.set_state - arch v6.2.1 (+21)</title>
+        <title>arch.bootstrap.IndependentSamplesBootstrap.set_state - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -1109,7 +1109,7 @@ <h1 id="arch-bootstrap-independentsamplesbootstrap-set-state">arch.bootstrap.Ind
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.state.html b/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.state.html
index d3f37bc0a6..6388697938 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.state.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.state.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.IndependentSamplesBootstrap.state - arch v6.2.1 (+21)</title>
+        <title>arch.bootstrap.IndependentSamplesBootstrap.state - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -1099,7 +1099,7 @@ <h1 id="arch-bootstrap-independentsamplesbootstrap-state">arch.bootstrap.Indepen
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.update_indices.html b/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.update_indices.html
index 648d2f511f..a61ecfc653 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.update_indices.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.update_indices.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.IndependentSamplesBootstrap.update_indices - arch v6.2.1 (+21)</title>
+        <title>arch.bootstrap.IndependentSamplesBootstrap.update_indices - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -1050,7 +1050,7 @@ <h1 id="arch-bootstrap-independentsamplesbootstrap-update-indices">arch.bootstra
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.var.html b/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.var.html
index 59deb5f6d3..90fa48711a 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.var.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.IndependentSamplesBootstrap.var.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.IndependentSamplesBootstrap.var - arch v6.2.1 (+21)</title>
+        <title>arch.bootstrap.IndependentSamplesBootstrap.var - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -1053,12 +1053,12 @@
 <h1 id="arch-bootstrap-independentsamplesbootstrap-var">arch.bootstrap.IndependentSamplesBootstrap.var<a class="headerlink" href="#arch-bootstrap-independentsamplesbootstrap-var" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.bootstrap.IndependentSamplesBootstrap.var">
-<span class="sig-prename descclassname"><span class="pre">IndependentSamplesBootstrap.</span></span><span class="sig-name descname"><span class="pre">var</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IndependentSamplesBootstrap.var.func" title="arch.bootstrap.IndependentSamplesBootstrap.var.func (Python parameter) — Callable function that returns the statistic of interest as a 1-d array"><span class="n"><span class="pre">func</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.12)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="p"><span class="pre">]</span></span></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IndependentSamplesBootstrap.var.reps" title="arch.bootstrap.IndependentSamplesBootstrap.var.reps (Python parameter) — Number of bootstrap replications"><span class="n"><span class="pre">reps</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IndependentSamplesBootstrap.var.recenter" title="arch.bootstrap.IndependentSamplesBootstrap.var.recenter (Python parameter) — Whether to center the bootstrap variance estimator on the average of the bootstrap samples (True) or to center on the original sample estimate (False).  Default is True."><span class="n"><span class="pre">recenter</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IndependentSamplesBootstrap.var.extra_kwargs" title="arch.bootstrap.IndependentSamplesBootstrap.var.extra_kwargs (Python parameter) — Dictionary of extra keyword arguments to pass to func"><span class="n"><span class="pre">extra_kwargs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.12)"><span class="pre">Any</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="headerlink" href="#arch.bootstrap.IndependentSamplesBootstrap.var" title="Link to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">IndependentSamplesBootstrap.</span></span><span class="sig-name descname"><span class="pre">var</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IndependentSamplesBootstrap.var.func" title="arch.bootstrap.IndependentSamplesBootstrap.var.func (Python parameter) — Callable function that returns the statistic of interest as a 1-d array"><span class="n"><span class="pre">func</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.12)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a><span class="p"><span class="pre">]</span></span></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IndependentSamplesBootstrap.var.reps" title="arch.bootstrap.IndependentSamplesBootstrap.var.reps (Python parameter) — Number of bootstrap replications"><span class="n"><span class="pre">reps</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IndependentSamplesBootstrap.var.recenter" title="arch.bootstrap.IndependentSamplesBootstrap.var.recenter (Python parameter) — Whether to center the bootstrap variance estimator on the average of the bootstrap samples (True) or to center on the original sample estimate (False).  Default is True."><span class="n"><span class="pre">recenter</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.IndependentSamplesBootstrap.var.extra_kwargs" title="arch.bootstrap.IndependentSamplesBootstrap.var.extra_kwargs (Python parameter) — Dictionary of extra keyword arguments to pass to func"><span class="n"><span class="pre">extra_kwargs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.12)"><span class="pre">Any</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="headerlink" href="#arch.bootstrap.IndependentSamplesBootstrap.var" title="Link to this definition">¶</a></dt>
 <dd><p>Compute parameter variance using bootstrap</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.bootstrap.IndependentSamplesBootstrap.var-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.IndependentSamplesBootstrap.var-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.bootstrap.IndependentSamplesBootstrap.var.func"><span class="n sig-name">func</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.12)">Callable</a><span class="p">[</span><span class="p">[</span><span class="p">...</span><span class="p">]</span><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a><span class="p">]</span></span></span><a class="headerlink" href="#arch.bootstrap.IndependentSamplesBootstrap.var.func" title="Permalink to this definition">¶</a></dt><dd><p>Callable function that returns the statistic of interest as a
+<dt id="arch.bootstrap.IndependentSamplesBootstrap.var.func"><span class="n sig-name">func</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.12)">Callable</a><span class="p">[</span><span class="p">[</span><span class="p">...</span><span class="p">]</span><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a><span class="p">]</span></span></span><a class="headerlink" href="#arch.bootstrap.IndependentSamplesBootstrap.var.func" title="Permalink to this definition">¶</a></dt><dd><p>Callable function that returns the statistic of interest as a
 1-d array</p>
 </dd>
 <dt id="arch.bootstrap.IndependentSamplesBootstrap.var.reps"><span class="n sig-name">reps</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code><a class="headerlink" href="#arch.bootstrap.IndependentSamplesBootstrap.var.reps" title="Permalink to this definition">¶</a></dt><dd><p>Number of bootstrap replications</p>
@@ -1229,7 +1229,7 @@ <h1 id="arch-bootstrap-independentsamplesbootstrap-var">arch.bootstrap.Independe
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.apply.html b/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.apply.html
index 65f6cc393a..4fa6b51e24 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.apply.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.apply.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.MovingBlockBootstrap.apply - arch v6.2.1 (+21)</title>
+        <title>arch.bootstrap.MovingBlockBootstrap.apply - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -1084,12 +1084,12 @@
 <h1 id="arch-bootstrap-movingblockbootstrap-apply">arch.bootstrap.MovingBlockBootstrap.apply<a class="headerlink" href="#arch-bootstrap-movingblockbootstrap-apply" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.bootstrap.MovingBlockBootstrap.apply">
-<span class="sig-prename descclassname"><span class="pre">MovingBlockBootstrap.</span></span><span class="sig-name descname"><span class="pre">apply</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.MovingBlockBootstrap.apply.func" title="arch.bootstrap.MovingBlockBootstrap.apply.func (Python parameter) — Function the computes parameter values.  See Notes for requirements"><span class="n"><span class="pre">func</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.12)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="p"><span class="pre">]</span></span></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.MovingBlockBootstrap.apply.reps" title="arch.bootstrap.MovingBlockBootstrap.apply.reps (Python parameter) — Number of bootstrap replications"><span class="n"><span class="pre">reps</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.MovingBlockBootstrap.apply.extra_kwargs" title="arch.bootstrap.MovingBlockBootstrap.apply.extra_kwargs (Python parameter) — Extra keyword arguments to use when calling func.  Must not conflict with keyword arguments used to initialize bootstrap"><span class="n"><span class="pre">extra_kwargs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.12)"><span class="pre">Any</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="headerlink" href="#arch.bootstrap.MovingBlockBootstrap.apply" title="Link to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">MovingBlockBootstrap.</span></span><span class="sig-name descname"><span class="pre">apply</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.MovingBlockBootstrap.apply.func" title="arch.bootstrap.MovingBlockBootstrap.apply.func (Python parameter) — Function the computes parameter values.  See Notes for requirements"><span class="n"><span class="pre">func</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.12)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a><span class="p"><span class="pre">]</span></span></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.MovingBlockBootstrap.apply.reps" title="arch.bootstrap.MovingBlockBootstrap.apply.reps (Python parameter) — Number of bootstrap replications"><span class="n"><span class="pre">reps</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.MovingBlockBootstrap.apply.extra_kwargs" title="arch.bootstrap.MovingBlockBootstrap.apply.extra_kwargs (Python parameter) — Extra keyword arguments to use when calling func.  Must not conflict with keyword arguments used to initialize bootstrap"><span class="n"><span class="pre">extra_kwargs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.12)"><span class="pre">Any</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="headerlink" href="#arch.bootstrap.MovingBlockBootstrap.apply" title="Link to this definition">¶</a></dt>
 <dd><p>Applies a function to bootstrap replicated data</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.bootstrap.MovingBlockBootstrap.apply-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.MovingBlockBootstrap.apply-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.bootstrap.MovingBlockBootstrap.apply.func"><span class="n sig-name">func</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.12)">Callable</a><span class="p">[</span><span class="p">[</span><span class="p">...</span><span class="p">]</span><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a><span class="p">]</span></span></span><a class="headerlink" href="#arch.bootstrap.MovingBlockBootstrap.apply.func" title="Permalink to this definition">¶</a></dt><dd><p>Function the computes parameter values.  See Notes for requirements</p>
+<dt id="arch.bootstrap.MovingBlockBootstrap.apply.func"><span class="n sig-name">func</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.12)">Callable</a><span class="p">[</span><span class="p">[</span><span class="p">...</span><span class="p">]</span><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a><span class="p">]</span></span></span><a class="headerlink" href="#arch.bootstrap.MovingBlockBootstrap.apply.func" title="Permalink to this definition">¶</a></dt><dd><p>Function the computes parameter values.  See Notes for requirements</p>
 </dd>
 <dt id="arch.bootstrap.MovingBlockBootstrap.apply.reps"><span class="n sig-name">reps</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code><a class="headerlink" href="#arch.bootstrap.MovingBlockBootstrap.apply.reps" title="Permalink to this definition">¶</a></dt><dd><p>Number of bootstrap replications</p>
 </dd>
@@ -1240,7 +1240,7 @@ <h1 id="arch-bootstrap-movingblockbootstrap-apply">arch.bootstrap.MovingBlockBoo
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.bootstrap.html b/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.bootstrap.html
index 2642f4f1f3..142680fe79 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.bootstrap.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.bootstrap.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.MovingBlockBootstrap.bootstrap - arch v6.2.1 (+21)</title>
+        <title>arch.bootstrap.MovingBlockBootstrap.bootstrap - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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     <div class="md-nav__source">
@@ -1056,7 +1056,7 @@
 <h1 id="arch-bootstrap-movingblockbootstrap-bootstrap">arch.bootstrap.MovingBlockBootstrap.bootstrap<a class="headerlink" href="#arch-bootstrap-movingblockbootstrap-bootstrap" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.bootstrap.MovingBlockBootstrap.bootstrap">
-<span class="sig-prename descclassname"><span class="pre">MovingBlockBootstrap.</span></span><span class="sig-name descname"><span class="pre">bootstrap</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.MovingBlockBootstrap.bootstrap.reps" title="arch.bootstrap.MovingBlockBootstrap.bootstrap.reps (Python parameter) — Number of bootstrap replications"><span class="n"><span class="pre">reps</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Generator" title="(in Python v3.12)"><span class="pre">Generator</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="p"><span class="pre">]</span></span></span></span></span><a class="headerlink" href="#arch.bootstrap.MovingBlockBootstrap.bootstrap" title="Link to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">MovingBlockBootstrap.</span></span><span class="sig-name descname"><span class="pre">bootstrap</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.MovingBlockBootstrap.bootstrap.reps" title="arch.bootstrap.MovingBlockBootstrap.bootstrap.reps (Python parameter) — Number of bootstrap replications"><span class="n"><span class="pre">reps</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Generator" title="(in Python v3.12)"><span class="pre">Generator</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="p"><span class="pre">]</span></span></span></span></span><a class="headerlink" href="#arch.bootstrap.MovingBlockBootstrap.bootstrap" title="Link to this definition">¶</a></dt>
 <dd><p>Iterator for use when bootstrapping</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.bootstrap.MovingBlockBootstrap.bootstrap-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.MovingBlockBootstrap.bootstrap-parameters" title="Permalink to this headline">¶</a></dt>
@@ -1208,7 +1208,7 @@ <h1 id="arch-bootstrap-movingblockbootstrap-bootstrap">arch.bootstrap.MovingBloc
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.clone.html b/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.clone.html
index a3bf5ddd09..11afe8a80c 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.clone.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.clone.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.MovingBlockBootstrap.clone - arch v6.2.1 (+21)</title>
+        <title>arch.bootstrap.MovingBlockBootstrap.clone - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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     <div class="md-nav__source">
@@ -1084,16 +1084,16 @@
 <h1 id="arch-bootstrap-movingblockbootstrap-clone">arch.bootstrap.MovingBlockBootstrap.clone<a class="headerlink" href="#arch-bootstrap-movingblockbootstrap-clone" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.bootstrap.MovingBlockBootstrap.clone">
-<span class="sig-prename descclassname"><span class="pre">MovingBlockBootstrap.</span></span><span class="sig-name descname"><span class="pre">clone</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><span class="o"><span class="pre">*</span></span><a class="n reference internal" href="#arch.bootstrap.MovingBlockBootstrap.clone.args" title="arch.bootstrap.MovingBlockBootstrap.clone.args (Python parameter) — Positional arguments to bootstrap"><span class="n"><span class="pre">args</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="pre">ArrayLike</span></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.MovingBlockBootstrap.clone.seed" title="arch.bootstrap.MovingBlockBootstrap.clone.seed (Python parameter) — The seed value to pass to the closed generator"><span class="n"><span class="pre">seed</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">Generator</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">RandomState</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="o"><span class="pre">**</span></span><a class="n reference internal" href="#arch.bootstrap.MovingBlockBootstrap.clone.kwargs" title="arch.bootstrap.MovingBlockBootstrap.clone.kwargs (Python parameter) — Keyword arguments to bootstrap"><span class="n"><span class="pre">kwargs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="pre">ArrayLike</span></span></span></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference internal" href="../arch.bootstrap.CircularBlockBootstrap.html#arch.bootstrap.CircularBlockBootstrap" title="arch.bootstrap.CircularBlockBootstrap (Python class) — Bootstrap using blocks of the same length with end-to-start wrap around"><span class="pre">CircularBlockBootstrap</span></a></span></span></span><a class="headerlink" href="#arch.bootstrap.MovingBlockBootstrap.clone" title="Link to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">MovingBlockBootstrap.</span></span><span class="sig-name descname"><span class="pre">clone</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><span class="o"><span class="pre">*</span></span><a class="n reference internal" href="#arch.bootstrap.MovingBlockBootstrap.clone.args" title="arch.bootstrap.MovingBlockBootstrap.clone.args (Python parameter) — Positional arguments to bootstrap"><span class="n"><span class="pre">args</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.MovingBlockBootstrap.clone.seed" title="arch.bootstrap.MovingBlockBootstrap.clone.seed (Python parameter) — The seed value to pass to the closed generator"><span class="n"><span class="pre">seed</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.26)"><span class="pre">Generator</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.26)"><span class="pre">RandomState</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="o"><span class="pre">**</span></span><a class="n reference internal" href="#arch.bootstrap.MovingBlockBootstrap.clone.kwargs" title="arch.bootstrap.MovingBlockBootstrap.clone.kwargs (Python parameter) — Keyword arguments to bootstrap"><span class="n"><span class="pre">kwargs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a></span></span></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference internal" href="../arch.bootstrap.CircularBlockBootstrap.html#arch.bootstrap.CircularBlockBootstrap" title="arch.bootstrap.base.CircularBlockBootstrap (Python class)"><span class="pre">CircularBlockBootstrap</span></a></span></span></span><a class="headerlink" href="#arch.bootstrap.MovingBlockBootstrap.clone" title="Link to this definition">¶</a></dt>
 <dd><p>Clones the bootstrap using different data with a fresh prng.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.bootstrap.MovingBlockBootstrap.clone-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.MovingBlockBootstrap.clone-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.bootstrap.MovingBlockBootstrap.clone.args"><span class="o">*</span><span class="n sig-name">args</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype">ArrayLike</span></span><a class="headerlink" href="#arch.bootstrap.MovingBlockBootstrap.clone.args" title="Permalink to this definition">¶</a></dt><dd><p>Positional arguments to bootstrap</p>
+<dt id="arch.bootstrap.MovingBlockBootstrap.clone.args"><span class="o">*</span><span class="n sig-name">args</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a></span></span><a class="headerlink" href="#arch.bootstrap.MovingBlockBootstrap.clone.args" title="Permalink to this definition">¶</a></dt><dd><p>Positional arguments to bootstrap</p>
 </dd>
-<dt id="arch.bootstrap.MovingBlockBootstrap.clone.seed"><span class="n sig-name">seed</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span>Generator<span class="w"> </span><span class="p">|</span><span class="w"> </span>RandomState</span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.bootstrap.MovingBlockBootstrap.clone.seed" title="Permalink to this definition">¶</a></dt><dd><p>The seed value to pass to the closed generator</p>
+<dt id="arch.bootstrap.MovingBlockBootstrap.clone.seed"><span class="n sig-name">seed</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.26)">Generator</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.26)">RandomState</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.bootstrap.MovingBlockBootstrap.clone.seed" title="Permalink to this definition">¶</a></dt><dd><p>The seed value to pass to the closed generator</p>
 </dd>
-<dt id="arch.bootstrap.MovingBlockBootstrap.clone.kwargs"><span class="o">**</span><span class="n sig-name">kwargs</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype">ArrayLike</span></span><a class="headerlink" href="#arch.bootstrap.MovingBlockBootstrap.clone.kwargs" title="Permalink to this definition">¶</a></dt><dd><p>Keyword arguments to bootstrap</p>
+<dt id="arch.bootstrap.MovingBlockBootstrap.clone.kwargs"><span class="o">**</span><span class="n sig-name">kwargs</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a></span></span><a class="headerlink" href="#arch.bootstrap.MovingBlockBootstrap.clone.kwargs" title="Permalink to this definition">¶</a></dt><dd><p>Keyword arguments to bootstrap</p>
 </dd>
 </dl>
 </dd>
@@ -1220,7 +1220,7 @@ <h1 id="arch-bootstrap-movingblockbootstrap-clone">arch.bootstrap.MovingBlockBoo
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diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.conf_int.html b/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.conf_int.html
index 9ee521b645..4e9a57e254 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.conf_int.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.conf_int.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.MovingBlockBootstrap.conf_int - arch v6.2.1 (+21)</title>
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+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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@@ -1182,7 +1182,7 @@
 <h1 id="arch-bootstrap-movingblockbootstrap-conf-int">arch.bootstrap.MovingBlockBootstrap.conf_int<a class="headerlink" href="#arch-bootstrap-movingblockbootstrap-conf-int" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.bootstrap.MovingBlockBootstrap.conf_int">
-<span class="sig-prename descclassname"><span class="pre">MovingBlockBootstrap.</span></span><span class="sig-name descname"><span class="pre">conf_int</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.MovingBlockBootstrap.conf_int.func" title="arch.bootstrap.MovingBlockBootstrap.conf_int.func (Python parameter) — Function the computes parameter values.  See Notes for requirements"><span class="n"><span class="pre">func</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.12)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.MovingBlockBootstrap.conf_int.reps" title="arch.bootstrap.MovingBlockBootstrap.conf_int.reps (Python parameter) — Number of bootstrap replications"><span class="n"><span class="pre">reps</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.MovingBlockBootstrap.conf_int.method" title="arch.bootstrap.MovingBlockBootstrap.conf_int.method (Python parameter) — One of 'basic', 'percentile', 'studentized', 'norm' (identical to 'var', 'cov'), 'bc' (identical to 'debiased', 'bias-corrected'), or 'bca'"><span class="n"><span class="pre">method</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'basic'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'percentile'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'studentized'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'norm'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'bc'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'bca'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;basic&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.MovingBlockBootstrap.conf_int.size" title="arch.bootstrap.MovingBlockBootstrap.conf_int.size (Python parameter) — Coverage of confidence interval"><span class="n"><span class="pre">size</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mf">0.95</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.MovingBlockBootstrap.conf_int.tail" title="arch.bootstrap.MovingBlockBootstrap.conf_int.tail (Python parameter) — One of 'two', 'upper' or 'lower'."><span class="n"><span class="pre">tail</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'two'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'upper'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'lower'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;two&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.MovingBlockBootstrap.conf_int.extra_kwargs" title="arch.bootstrap.MovingBlockBootstrap.conf_int.extra_kwargs (Python parameter) — Extra keyword arguments to use when calling func and std_err_func, when appropriate"><span class="n"><span class="pre">extra_kwargs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.12)"><span class="pre">Any</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.MovingBlockBootstrap.conf_int.reuse" title="arch.bootstrap.MovingBlockBootstrap.conf_int.reuse (Python parameter) — Flag indicating whether to reuse previously computed bootstrap results.  This allows alternative methods to be compared without rerunning the bootstrap simulation.  Reuse is ignored if reps is not the same across multiple runs, func changes across calls, or method is 'studentized'."><span class="n"><span class="pre">reuse</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.MovingBlockBootstrap.conf_int.sampling" title="arch.bootstrap.MovingBlockBootstrap.conf_int.sampling (Python parameter) — Type of sampling to use: 'nonparametric', 'semi-parametric' (or 'semi') or 'parametric'.  The default is 'nonparametric'.  See notes about the changes to func required when using 'semi' or 'parametric'."><span class="n"><span class="pre">sampling</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'nonparametric'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'semi-parametric'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'semi'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'parametric'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'semiparametric'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;nonparametric&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.MovingBlockBootstrap.conf_int.std_err_func" title="arch.bootstrap.MovingBlockBootstrap.conf_int.std_err_func (Python parameter) — Function to use when standardizing estimated parameters when using the studentized bootstrap.  Providing an analytical function eliminates the need for a nested bootstrap"><span class="n"><span class="pre">std_err_func</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.12)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.MovingBlockBootstrap.conf_int.studentize_reps" title="arch.bootstrap.MovingBlockBootstrap.conf_int.studentize_reps (Python parameter) — Number of bootstraps to use in the inner bootstrap when using the studentized bootstrap.  Ignored when std_err_func is provided"><span class="n"><span class="pre">studentize_reps</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="headerlink" href="#arch.bootstrap.MovingBlockBootstrap.conf_int" title="Link to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">MovingBlockBootstrap.</span></span><span class="sig-name descname"><span class="pre">conf_int</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.MovingBlockBootstrap.conf_int.func" title="arch.bootstrap.MovingBlockBootstrap.conf_int.func (Python parameter) — Function the computes parameter values.  See Notes for requirements"><span class="n"><span class="pre">func</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.12)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.MovingBlockBootstrap.conf_int.reps" title="arch.bootstrap.MovingBlockBootstrap.conf_int.reps (Python parameter) — Number of bootstrap replications"><span class="n"><span class="pre">reps</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.MovingBlockBootstrap.conf_int.method" title="arch.bootstrap.MovingBlockBootstrap.conf_int.method (Python parameter) — One of 'basic', 'percentile', 'studentized', 'norm' (identical to 'var', 'cov'), 'bc' (identical to 'debiased', 'bias-corrected'), or 'bca'"><span class="n"><span class="pre">method</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'basic'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'percentile'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'studentized'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'norm'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'bc'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'bca'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;basic&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.MovingBlockBootstrap.conf_int.size" title="arch.bootstrap.MovingBlockBootstrap.conf_int.size (Python parameter) — Coverage of confidence interval"><span class="n"><span class="pre">size</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mf">0.95</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.MovingBlockBootstrap.conf_int.tail" title="arch.bootstrap.MovingBlockBootstrap.conf_int.tail (Python parameter) — One of 'two', 'upper' or 'lower'."><span class="n"><span class="pre">tail</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'two'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'upper'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'lower'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;two&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.MovingBlockBootstrap.conf_int.extra_kwargs" title="arch.bootstrap.MovingBlockBootstrap.conf_int.extra_kwargs (Python parameter) — Extra keyword arguments to use when calling func and std_err_func, when appropriate"><span class="n"><span class="pre">extra_kwargs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.12)"><span class="pre">Any</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.MovingBlockBootstrap.conf_int.reuse" title="arch.bootstrap.MovingBlockBootstrap.conf_int.reuse (Python parameter) — Flag indicating whether to reuse previously computed bootstrap results.  This allows alternative methods to be compared without rerunning the bootstrap simulation.  Reuse is ignored if reps is not the same across multiple runs, func changes across calls, or method is 'studentized'."><span class="n"><span class="pre">reuse</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.MovingBlockBootstrap.conf_int.sampling" title="arch.bootstrap.MovingBlockBootstrap.conf_int.sampling (Python parameter) — Type of sampling to use: 'nonparametric', 'semi-parametric' (or 'semi') or 'parametric'.  The default is 'nonparametric'.  See notes about the changes to func required when using 'semi' or 'parametric'."><span class="n"><span class="pre">sampling</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'nonparametric'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'semi-parametric'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'semi'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'parametric'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'semiparametric'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;nonparametric&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.MovingBlockBootstrap.conf_int.std_err_func" title="arch.bootstrap.MovingBlockBootstrap.conf_int.std_err_func (Python parameter) — Function to use when standardizing estimated parameters when using the studentized bootstrap.  Providing an analytical function eliminates the need for a nested bootstrap"><span class="n"><span class="pre">std_err_func</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.12)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.MovingBlockBootstrap.conf_int.studentize_reps" title="arch.bootstrap.MovingBlockBootstrap.conf_int.studentize_reps (Python parameter) — Number of bootstraps to use in the inner bootstrap when using the studentized bootstrap.  Ignored when std_err_func is provided"><span class="n"><span class="pre">studentize_reps</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="headerlink" href="#arch.bootstrap.MovingBlockBootstrap.conf_int" title="Link to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 <dt class="field-odd" id="arch.bootstrap.MovingBlockBootstrap.conf_int-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.MovingBlockBootstrap.conf_int-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
@@ -1212,7 +1212,7 @@ <h1 id="arch-bootstrap-movingblockbootstrap-conf-int">arch.bootstrap.MovingBlock
 <dt id="arch.bootstrap.MovingBlockBootstrap.conf_int.extra_kwargs"><span class="n sig-name">extra_kwargs</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)">dict</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)">str</a><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.12)">Any</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.bootstrap.MovingBlockBootstrap.conf_int.extra_kwargs" title="Permalink to this definition">¶</a></dt><dd><p>Extra keyword arguments to use when calling func and std_err_func,
 when appropriate</p>
 </dd>
-<dt id="arch.bootstrap.MovingBlockBootstrap.conf_int.std_err_func"><span class="n sig-name">std_err_func</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.12)">Callable</a><span class="p">[</span><span class="p">[</span><span class="p">...</span><span class="p">]</span><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.bootstrap.MovingBlockBootstrap.conf_int.std_err_func" title="Permalink to this definition">¶</a></dt><dd><p>Function to use when standardizing estimated parameters when using
+<dt id="arch.bootstrap.MovingBlockBootstrap.conf_int.std_err_func"><span class="n sig-name">std_err_func</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.12)">Callable</a><span class="p">[</span><span class="p">[</span><span class="p">...</span><span class="p">]</span><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.bootstrap.MovingBlockBootstrap.conf_int.std_err_func" title="Permalink to this definition">¶</a></dt><dd><p>Function to use when standardizing estimated parameters when using
 the studentized bootstrap.  Providing an analytical function
 eliminates the need for a nested bootstrap</p>
 </dd>
@@ -1387,7 +1387,7 @@ <h1 id="arch-bootstrap-movingblockbootstrap-conf-int">arch.bootstrap.MovingBlock
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+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
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diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.cov.html b/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.cov.html
index 7cd8b66e16..aa3d0b72f5 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.cov.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.cov.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.MovingBlockBootstrap.cov - arch v6.2.1 (+21)</title>
+        <title>arch.bootstrap.MovingBlockBootstrap.cov - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
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@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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     <div class="md-nav__source">
@@ -1098,12 +1098,12 @@
 <h1 id="arch-bootstrap-movingblockbootstrap-cov">arch.bootstrap.MovingBlockBootstrap.cov<a class="headerlink" href="#arch-bootstrap-movingblockbootstrap-cov" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.bootstrap.MovingBlockBootstrap.cov">
-<span class="sig-prename descclassname"><span class="pre">MovingBlockBootstrap.</span></span><span class="sig-name descname"><span class="pre">cov</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.MovingBlockBootstrap.cov.func" title="arch.bootstrap.MovingBlockBootstrap.cov.func (Python parameter) — Callable function that returns the statistic of interest as a 1-d array"><span class="n"><span class="pre">func</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.12)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="p"><span class="pre">]</span></span></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.MovingBlockBootstrap.cov.reps" title="arch.bootstrap.MovingBlockBootstrap.cov.reps (Python parameter) — Number of bootstrap replications"><span class="n"><span class="pre">reps</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.MovingBlockBootstrap.cov.recenter" title="arch.bootstrap.MovingBlockBootstrap.cov.recenter (Python parameter) — Whether to center the bootstrap variance estimator on the average of the bootstrap samples (True) or to center on the original sample estimate (False).  Default is True."><span class="n"><span class="pre">recenter</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.MovingBlockBootstrap.cov.extra_kwargs" title="arch.bootstrap.MovingBlockBootstrap.cov.extra_kwargs (Python parameter) — Dictionary of extra keyword arguments to pass to func"><span class="n"><span class="pre">extra_kwargs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.12)"><span class="pre">Any</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="headerlink" href="#arch.bootstrap.MovingBlockBootstrap.cov" title="Link to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">MovingBlockBootstrap.</span></span><span class="sig-name descname"><span class="pre">cov</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.MovingBlockBootstrap.cov.func" title="arch.bootstrap.MovingBlockBootstrap.cov.func (Python parameter) — Callable function that returns the statistic of interest as a 1-d array"><span class="n"><span class="pre">func</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.12)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a><span class="p"><span class="pre">]</span></span></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.MovingBlockBootstrap.cov.reps" title="arch.bootstrap.MovingBlockBootstrap.cov.reps (Python parameter) — Number of bootstrap replications"><span class="n"><span class="pre">reps</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.MovingBlockBootstrap.cov.recenter" title="arch.bootstrap.MovingBlockBootstrap.cov.recenter (Python parameter) — Whether to center the bootstrap variance estimator on the average of the bootstrap samples (True) or to center on the original sample estimate (False).  Default is True."><span class="n"><span class="pre">recenter</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.MovingBlockBootstrap.cov.extra_kwargs" title="arch.bootstrap.MovingBlockBootstrap.cov.extra_kwargs (Python parameter) — Dictionary of extra keyword arguments to pass to func"><span class="n"><span class="pre">extra_kwargs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.12)"><span class="pre">Any</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="headerlink" href="#arch.bootstrap.MovingBlockBootstrap.cov" title="Link to this definition">¶</a></dt>
 <dd><p>Compute parameter covariance using bootstrap</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.bootstrap.MovingBlockBootstrap.cov-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.MovingBlockBootstrap.cov-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.bootstrap.MovingBlockBootstrap.cov.func"><span class="n sig-name">func</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.12)">Callable</a><span class="p">[</span><span class="p">[</span><span class="p">...</span><span class="p">]</span><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a><span class="p">]</span></span></span><a class="headerlink" href="#arch.bootstrap.MovingBlockBootstrap.cov.func" title="Permalink to this definition">¶</a></dt><dd><p>Callable function that returns the statistic of interest as a
+<dt id="arch.bootstrap.MovingBlockBootstrap.cov.func"><span class="n sig-name">func</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.12)">Callable</a><span class="p">[</span><span class="p">[</span><span class="p">...</span><span class="p">]</span><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a><span class="p">]</span></span></span><a class="headerlink" href="#arch.bootstrap.MovingBlockBootstrap.cov.func" title="Permalink to this definition">¶</a></dt><dd><p>Callable function that returns the statistic of interest as a
 1-d array</p>
 </dd>
 <dt id="arch.bootstrap.MovingBlockBootstrap.cov.reps"><span class="n sig-name">reps</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code><a class="headerlink" href="#arch.bootstrap.MovingBlockBootstrap.cov.reps" title="Permalink to this definition">¶</a></dt><dd><p>Number of bootstrap replications</p>
@@ -1274,7 +1274,7 @@ <h1 id="arch-bootstrap-movingblockbootstrap-cov">arch.bootstrap.MovingBlockBoots
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.generator.html b/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.generator.html
index c8f480beb2..00d209e7bb 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.generator.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.generator.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.MovingBlockBootstrap.generator - arch v6.2.1 (+21)</title>
+        <title>arch.bootstrap.MovingBlockBootstrap.generator - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -1030,7 +1030,7 @@
 <h1 id="arch-bootstrap-movingblockbootstrap-generator">arch.bootstrap.MovingBlockBootstrap.generator<a class="headerlink" href="#arch-bootstrap-movingblockbootstrap-generator" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.bootstrap.MovingBlockBootstrap.generator">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">MovingBlockBootstrap.</span></span><span class="sig-name descname"><span class="pre">generator</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><span class="pre">Generator</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">RandomState</span></span><a class="headerlink" href="#arch.bootstrap.MovingBlockBootstrap.generator" title="Link to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">MovingBlockBootstrap.</span></span><span class="sig-name descname"><span class="pre">generator</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.26)"><span class="pre">Generator</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.26)"><span class="pre">RandomState</span></a></span><a class="headerlink" href="#arch.bootstrap.MovingBlockBootstrap.generator" title="Link to this definition">¶</a></dt>
 <dd><p>Set or get the instance PRNG</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.bootstrap.MovingBlockBootstrap.generator-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.MovingBlockBootstrap.generator-parameters" title="Permalink to this headline">¶</a></dt>
@@ -1164,7 +1164,7 @@ <h1 id="arch-bootstrap-movingblockbootstrap-generator">arch.bootstrap.MovingBloc
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.get_state.html b/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.get_state.html
index 6252254f4a..29a7a575ae 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.get_state.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.get_state.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.MovingBlockBootstrap.get_state - arch v6.2.1 (+21)</title>
+        <title>arch.bootstrap.MovingBlockBootstrap.get_state - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -1142,7 +1142,7 @@ <h1 id="arch-bootstrap-movingblockbootstrap-get-state">arch.bootstrap.MovingBloc
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.index.html b/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.index.html
index 5ace9ad189..c7d6b647f3 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.index.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.index.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.MovingBlockBootstrap.index - arch v6.2.1 (+21)</title>
+        <title>arch.bootstrap.MovingBlockBootstrap.index - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -1094,7 +1094,7 @@ <h1 id="arch-bootstrap-movingblockbootstrap-index">arch.bootstrap.MovingBlockBoo
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.random_state.html b/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.random_state.html
index 209229e870..91c44d7e53 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.random_state.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.random_state.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.MovingBlockBootstrap.random_state - arch v6.2.1 (+21)</title>
+        <title>arch.bootstrap.MovingBlockBootstrap.random_state - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -1030,7 +1030,7 @@
 <h1 id="arch-bootstrap-movingblockbootstrap-random-state">arch.bootstrap.MovingBlockBootstrap.random_state<a class="headerlink" href="#arch-bootstrap-movingblockbootstrap-random-state" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.bootstrap.MovingBlockBootstrap.random_state">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">MovingBlockBootstrap.</span></span><span class="sig-name descname"><span class="pre">random_state</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><span class="pre">Generator</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">RandomState</span></span><a class="headerlink" href="#arch.bootstrap.MovingBlockBootstrap.random_state" title="Link to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">MovingBlockBootstrap.</span></span><span class="sig-name descname"><span class="pre">random_state</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.26)"><span class="pre">Generator</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.26)"><span class="pre">RandomState</span></a></span><a class="headerlink" href="#arch.bootstrap.MovingBlockBootstrap.random_state" title="Link to this definition">¶</a></dt>
 <dd><p>Set or get the instance random state</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.bootstrap.MovingBlockBootstrap.random_state-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.MovingBlockBootstrap.random_state-parameters" title="Permalink to this headline">¶</a></dt>
@@ -1162,7 +1162,7 @@ <h1 id="arch-bootstrap-movingblockbootstrap-random-state">arch.bootstrap.MovingB
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.reset.html b/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.reset.html
index 103e046ea3..b044abb523 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.reset.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.reset.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.MovingBlockBootstrap.reset - arch v6.2.1 (+21)</title>
+        <title>arch.bootstrap.MovingBlockBootstrap.reset - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -1156,7 +1156,7 @@ <h1 id="arch-bootstrap-movingblockbootstrap-reset">arch.bootstrap.MovingBlockBoo
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.seed.html b/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.seed.html
index d95aaf1c5f..bee69c7139 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.seed.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.seed.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.MovingBlockBootstrap.seed - arch v6.2.1 (+21)</title>
+        <title>arch.bootstrap.MovingBlockBootstrap.seed - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -1154,7 +1154,7 @@ <h1 id="arch-bootstrap-movingblockbootstrap-seed">arch.bootstrap.MovingBlockBoot
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.set_state.html b/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.set_state.html
index b9984d46d4..655c63f149 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.set_state.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.set_state.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.MovingBlockBootstrap.set_state - arch v6.2.1 (+21)</title>
+        <title>arch.bootstrap.MovingBlockBootstrap.set_state - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -1154,7 +1154,7 @@ <h1 id="arch-bootstrap-movingblockbootstrap-set-state">arch.bootstrap.MovingBloc
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.state.html b/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.state.html
index a80d6b721c..d2204daf16 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.state.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.state.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.MovingBlockBootstrap.state - arch v6.2.1 (+21)</title>
+        <title>arch.bootstrap.MovingBlockBootstrap.state - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -1144,7 +1144,7 @@ <h1 id="arch-bootstrap-movingblockbootstrap-state">arch.bootstrap.MovingBlockBoo
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.update_indices.html b/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.update_indices.html
index 3955ff6651..1d35fce550 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.update_indices.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.update_indices.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.MovingBlockBootstrap.update_indices - arch v6.2.1 (+21)</title>
+        <title>arch.bootstrap.MovingBlockBootstrap.update_indices - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -1095,7 +1095,7 @@ <h1 id="arch-bootstrap-movingblockbootstrap-update-indices">arch.bootstrap.Movin
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.var.html b/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.var.html
index d8845304f2..a15edd33bd 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.var.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.MovingBlockBootstrap.var.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.MovingBlockBootstrap.var - arch v6.2.1 (+21)</title>
+        <title>arch.bootstrap.MovingBlockBootstrap.var - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -1098,12 +1098,12 @@
 <h1 id="arch-bootstrap-movingblockbootstrap-var">arch.bootstrap.MovingBlockBootstrap.var<a class="headerlink" href="#arch-bootstrap-movingblockbootstrap-var" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.bootstrap.MovingBlockBootstrap.var">
-<span class="sig-prename descclassname"><span class="pre">MovingBlockBootstrap.</span></span><span class="sig-name descname"><span class="pre">var</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.MovingBlockBootstrap.var.func" title="arch.bootstrap.MovingBlockBootstrap.var.func (Python parameter) — Callable function that returns the statistic of interest as a 1-d array"><span class="n"><span class="pre">func</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.12)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="p"><span class="pre">]</span></span></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.MovingBlockBootstrap.var.reps" title="arch.bootstrap.MovingBlockBootstrap.var.reps (Python parameter) — Number of bootstrap replications"><span class="n"><span class="pre">reps</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.MovingBlockBootstrap.var.recenter" title="arch.bootstrap.MovingBlockBootstrap.var.recenter (Python parameter) — Whether to center the bootstrap variance estimator on the average of the bootstrap samples (True) or to center on the original sample estimate (False).  Default is True."><span class="n"><span class="pre">recenter</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.MovingBlockBootstrap.var.extra_kwargs" title="arch.bootstrap.MovingBlockBootstrap.var.extra_kwargs (Python parameter) — Dictionary of extra keyword arguments to pass to func"><span class="n"><span class="pre">extra_kwargs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.12)"><span class="pre">Any</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="headerlink" href="#arch.bootstrap.MovingBlockBootstrap.var" title="Link to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">MovingBlockBootstrap.</span></span><span class="sig-name descname"><span class="pre">var</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.MovingBlockBootstrap.var.func" title="arch.bootstrap.MovingBlockBootstrap.var.func (Python parameter) — Callable function that returns the statistic of interest as a 1-d array"><span class="n"><span class="pre">func</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.12)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a><span class="p"><span class="pre">]</span></span></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.MovingBlockBootstrap.var.reps" title="arch.bootstrap.MovingBlockBootstrap.var.reps (Python parameter) — Number of bootstrap replications"><span class="n"><span class="pre">reps</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.MovingBlockBootstrap.var.recenter" title="arch.bootstrap.MovingBlockBootstrap.var.recenter (Python parameter) — Whether to center the bootstrap variance estimator on the average of the bootstrap samples (True) or to center on the original sample estimate (False).  Default is True."><span class="n"><span class="pre">recenter</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.MovingBlockBootstrap.var.extra_kwargs" title="arch.bootstrap.MovingBlockBootstrap.var.extra_kwargs (Python parameter) — Dictionary of extra keyword arguments to pass to func"><span class="n"><span class="pre">extra_kwargs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.12)"><span class="pre">Any</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="headerlink" href="#arch.bootstrap.MovingBlockBootstrap.var" title="Link to this definition">¶</a></dt>
 <dd><p>Compute parameter variance using bootstrap</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.bootstrap.MovingBlockBootstrap.var-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.MovingBlockBootstrap.var-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.bootstrap.MovingBlockBootstrap.var.func"><span class="n sig-name">func</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.12)">Callable</a><span class="p">[</span><span class="p">[</span><span class="p">...</span><span class="p">]</span><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a><span class="p">]</span></span></span><a class="headerlink" href="#arch.bootstrap.MovingBlockBootstrap.var.func" title="Permalink to this definition">¶</a></dt><dd><p>Callable function that returns the statistic of interest as a
+<dt id="arch.bootstrap.MovingBlockBootstrap.var.func"><span class="n sig-name">func</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.12)">Callable</a><span class="p">[</span><span class="p">[</span><span class="p">...</span><span class="p">]</span><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a><span class="p">]</span></span></span><a class="headerlink" href="#arch.bootstrap.MovingBlockBootstrap.var.func" title="Permalink to this definition">¶</a></dt><dd><p>Callable function that returns the statistic of interest as a
 1-d array</p>
 </dd>
 <dt id="arch.bootstrap.MovingBlockBootstrap.var.reps"><span class="n sig-name">reps</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code><a class="headerlink" href="#arch.bootstrap.MovingBlockBootstrap.var.reps" title="Permalink to this definition">¶</a></dt><dd><p>Number of bootstrap replications</p>
@@ -1274,7 +1274,7 @@ <h1 id="arch-bootstrap-movingblockbootstrap-var">arch.bootstrap.MovingBlockBoots
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diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.apply.html b/devel/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.apply.html
index 071011dc2f..62fb549750 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.apply.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.apply.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.StationaryBootstrap.apply - arch v6.2.1 (+21)</title>
+        <title>arch.bootstrap.StationaryBootstrap.apply - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
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@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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@@ -102,7 +102,7 @@
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+            arch v6.2.1 (+23)
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@@ -189,10 +189,10 @@
 
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+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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@@ -1084,12 +1084,12 @@
 <h1 id="arch-bootstrap-stationarybootstrap-apply">arch.bootstrap.StationaryBootstrap.apply<a class="headerlink" href="#arch-bootstrap-stationarybootstrap-apply" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.bootstrap.StationaryBootstrap.apply">
-<span class="sig-prename descclassname"><span class="pre">StationaryBootstrap.</span></span><span class="sig-name descname"><span class="pre">apply</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.StationaryBootstrap.apply.func" title="arch.bootstrap.StationaryBootstrap.apply.func (Python parameter) — Function the computes parameter values.  See Notes for requirements"><span class="n"><span class="pre">func</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.12)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="p"><span class="pre">]</span></span></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.StationaryBootstrap.apply.reps" title="arch.bootstrap.StationaryBootstrap.apply.reps (Python parameter) — Number of bootstrap replications"><span class="n"><span class="pre">reps</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.StationaryBootstrap.apply.extra_kwargs" title="arch.bootstrap.StationaryBootstrap.apply.extra_kwargs (Python parameter) — Extra keyword arguments to use when calling func.  Must not conflict with keyword arguments used to initialize bootstrap"><span class="n"><span class="pre">extra_kwargs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.12)"><span class="pre">Any</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="headerlink" href="#arch.bootstrap.StationaryBootstrap.apply" title="Link to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">StationaryBootstrap.</span></span><span class="sig-name descname"><span class="pre">apply</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.StationaryBootstrap.apply.func" title="arch.bootstrap.StationaryBootstrap.apply.func (Python parameter) — Function the computes parameter values.  See Notes for requirements"><span class="n"><span class="pre">func</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.12)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a><span class="p"><span class="pre">]</span></span></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.StationaryBootstrap.apply.reps" title="arch.bootstrap.StationaryBootstrap.apply.reps (Python parameter) — Number of bootstrap replications"><span class="n"><span class="pre">reps</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.StationaryBootstrap.apply.extra_kwargs" title="arch.bootstrap.StationaryBootstrap.apply.extra_kwargs (Python parameter) — Extra keyword arguments to use when calling func.  Must not conflict with keyword arguments used to initialize bootstrap"><span class="n"><span class="pre">extra_kwargs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.12)"><span class="pre">Any</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="headerlink" href="#arch.bootstrap.StationaryBootstrap.apply" title="Link to this definition">¶</a></dt>
 <dd><p>Applies a function to bootstrap replicated data</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.bootstrap.StationaryBootstrap.apply-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.StationaryBootstrap.apply-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.bootstrap.StationaryBootstrap.apply.func"><span class="n sig-name">func</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.12)">Callable</a><span class="p">[</span><span class="p">[</span><span class="p">...</span><span class="p">]</span><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a><span class="p">]</span></span></span><a class="headerlink" href="#arch.bootstrap.StationaryBootstrap.apply.func" title="Permalink to this definition">¶</a></dt><dd><p>Function the computes parameter values.  See Notes for requirements</p>
+<dt id="arch.bootstrap.StationaryBootstrap.apply.func"><span class="n sig-name">func</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.12)">Callable</a><span class="p">[</span><span class="p">[</span><span class="p">...</span><span class="p">]</span><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a><span class="p">]</span></span></span><a class="headerlink" href="#arch.bootstrap.StationaryBootstrap.apply.func" title="Permalink to this definition">¶</a></dt><dd><p>Function the computes parameter values.  See Notes for requirements</p>
 </dd>
 <dt id="arch.bootstrap.StationaryBootstrap.apply.reps"><span class="n sig-name">reps</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code><a class="headerlink" href="#arch.bootstrap.StationaryBootstrap.apply.reps" title="Permalink to this definition">¶</a></dt><dd><p>Number of bootstrap replications</p>
 </dd>
@@ -1240,7 +1240,7 @@ <h1 id="arch-bootstrap-stationarybootstrap-apply">arch.bootstrap.StationaryBoots
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.bootstrap.html b/devel/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.bootstrap.html
index 31f20030f5..676d8667c6 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.bootstrap.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.bootstrap.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.StationaryBootstrap.bootstrap - arch v6.2.1 (+21)</title>
+        <title>arch.bootstrap.StationaryBootstrap.bootstrap - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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     <div class="md-nav__source">
@@ -1056,7 +1056,7 @@
 <h1 id="arch-bootstrap-stationarybootstrap-bootstrap">arch.bootstrap.StationaryBootstrap.bootstrap<a class="headerlink" href="#arch-bootstrap-stationarybootstrap-bootstrap" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.bootstrap.StationaryBootstrap.bootstrap">
-<span class="sig-prename descclassname"><span class="pre">StationaryBootstrap.</span></span><span class="sig-name descname"><span class="pre">bootstrap</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.StationaryBootstrap.bootstrap.reps" title="arch.bootstrap.StationaryBootstrap.bootstrap.reps (Python parameter) — Number of bootstrap replications"><span class="n"><span class="pre">reps</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Generator" title="(in Python v3.12)"><span class="pre">Generator</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="p"><span class="pre">]</span></span></span></span></span><a class="headerlink" href="#arch.bootstrap.StationaryBootstrap.bootstrap" title="Link to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">StationaryBootstrap.</span></span><span class="sig-name descname"><span class="pre">bootstrap</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.StationaryBootstrap.bootstrap.reps" title="arch.bootstrap.StationaryBootstrap.bootstrap.reps (Python parameter) — Number of bootstrap replications"><span class="n"><span class="pre">reps</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Generator" title="(in Python v3.12)"><span class="pre">Generator</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="p"><span class="pre">]</span></span></span></span></span><a class="headerlink" href="#arch.bootstrap.StationaryBootstrap.bootstrap" title="Link to this definition">¶</a></dt>
 <dd><p>Iterator for use when bootstrapping</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.bootstrap.StationaryBootstrap.bootstrap-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.StationaryBootstrap.bootstrap-parameters" title="Permalink to this headline">¶</a></dt>
@@ -1208,7 +1208,7 @@ <h1 id="arch-bootstrap-stationarybootstrap-bootstrap">arch.bootstrap.StationaryB
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-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.clone.html b/devel/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.clone.html
index 86c7336ec3..3d809432fd 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.clone.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.clone.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.StationaryBootstrap.clone - arch v6.2.1 (+21)</title>
+        <title>arch.bootstrap.StationaryBootstrap.clone - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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@@ -1084,16 +1084,16 @@
 <h1 id="arch-bootstrap-stationarybootstrap-clone">arch.bootstrap.StationaryBootstrap.clone<a class="headerlink" href="#arch-bootstrap-stationarybootstrap-clone" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.bootstrap.StationaryBootstrap.clone">
-<span class="sig-prename descclassname"><span class="pre">StationaryBootstrap.</span></span><span class="sig-name descname"><span class="pre">clone</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><span class="o"><span class="pre">*</span></span><a class="n reference internal" href="#arch.bootstrap.StationaryBootstrap.clone.args" title="arch.bootstrap.StationaryBootstrap.clone.args (Python parameter) — Positional arguments to bootstrap"><span class="n"><span class="pre">args</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.StationaryBootstrap.clone.seed" title="arch.bootstrap.StationaryBootstrap.clone.seed (Python parameter) — The seed value to pass to the closed generator"><span class="n"><span class="pre">seed</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.26)"><span class="pre">Generator</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.26)"><span class="pre">RandomState</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="o"><span class="pre">**</span></span><a class="n reference internal" href="#arch.bootstrap.StationaryBootstrap.clone.kwargs" title="arch.bootstrap.StationaryBootstrap.clone.kwargs (Python parameter) — Keyword arguments to bootstrap"><span class="n"><span class="pre">kwargs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference internal" href="../arch.bootstrap.CircularBlockBootstrap.html#arch.bootstrap.CircularBlockBootstrap" title="arch.bootstrap.base.CircularBlockBootstrap (Python class)"><span class="pre">CircularBlockBootstrap</span></a></span></span></span><a class="headerlink" href="#arch.bootstrap.StationaryBootstrap.clone" title="Link to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">StationaryBootstrap.</span></span><span class="sig-name descname"><span class="pre">clone</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><span class="o"><span class="pre">*</span></span><a class="n reference internal" href="#arch.bootstrap.StationaryBootstrap.clone.args" title="arch.bootstrap.StationaryBootstrap.clone.args (Python parameter) — Positional arguments to bootstrap"><span class="n"><span class="pre">args</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.StationaryBootstrap.clone.seed" title="arch.bootstrap.StationaryBootstrap.clone.seed (Python parameter) — The seed value to pass to the closed generator"><span class="n"><span class="pre">seed</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.26)"><span class="pre">Generator</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.26)"><span class="pre">RandomState</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="o"><span class="pre">**</span></span><a class="n reference internal" href="#arch.bootstrap.StationaryBootstrap.clone.kwargs" title="arch.bootstrap.StationaryBootstrap.clone.kwargs (Python parameter) — Keyword arguments to bootstrap"><span class="n"><span class="pre">kwargs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a></span></span></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference internal" href="../arch.bootstrap.CircularBlockBootstrap.html#arch.bootstrap.CircularBlockBootstrap" title="arch.bootstrap.base.CircularBlockBootstrap (Python class)"><span class="pre">CircularBlockBootstrap</span></a></span></span></span><a class="headerlink" href="#arch.bootstrap.StationaryBootstrap.clone" title="Link to this definition">¶</a></dt>
 <dd><p>Clones the bootstrap using different data with a fresh prng.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.bootstrap.StationaryBootstrap.clone-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.StationaryBootstrap.clone-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.bootstrap.StationaryBootstrap.clone.args"><span class="o">*</span><span class="n sig-name">args</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.bootstrap.StationaryBootstrap.clone.args" title="Permalink to this definition">¶</a></dt><dd><p>Positional arguments to bootstrap</p>
+<dt id="arch.bootstrap.StationaryBootstrap.clone.args"><span class="o">*</span><span class="n sig-name">args</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a></span></span><a class="headerlink" href="#arch.bootstrap.StationaryBootstrap.clone.args" title="Permalink to this definition">¶</a></dt><dd><p>Positional arguments to bootstrap</p>
 </dd>
 <dt id="arch.bootstrap.StationaryBootstrap.clone.seed"><span class="n sig-name">seed</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.26)">Generator</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.26)">RandomState</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.bootstrap.StationaryBootstrap.clone.seed" title="Permalink to this definition">¶</a></dt><dd><p>The seed value to pass to the closed generator</p>
 </dd>
-<dt id="arch.bootstrap.StationaryBootstrap.clone.kwargs"><span class="o">**</span><span class="n sig-name">kwargs</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.bootstrap.StationaryBootstrap.clone.kwargs" title="Permalink to this definition">¶</a></dt><dd><p>Keyword arguments to bootstrap</p>
+<dt id="arch.bootstrap.StationaryBootstrap.clone.kwargs"><span class="o">**</span><span class="n sig-name">kwargs</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a></span></span><a class="headerlink" href="#arch.bootstrap.StationaryBootstrap.clone.kwargs" title="Permalink to this definition">¶</a></dt><dd><p>Keyword arguments to bootstrap</p>
 </dd>
 </dl>
 </dd>
@@ -1220,7 +1220,7 @@ <h1 id="arch-bootstrap-stationarybootstrap-clone">arch.bootstrap.StationaryBoots
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 <h1 id="arch-bootstrap-stationarybootstrap-conf-int">arch.bootstrap.StationaryBootstrap.conf_int<a class="headerlink" href="#arch-bootstrap-stationarybootstrap-conf-int" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.bootstrap.StationaryBootstrap.conf_int">
-<span class="sig-prename descclassname"><span class="pre">StationaryBootstrap.</span></span><span class="sig-name descname"><span class="pre">conf_int</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.StationaryBootstrap.conf_int.func" title="arch.bootstrap.StationaryBootstrap.conf_int.func (Python parameter) — Function the computes parameter values.  See Notes for requirements"><span class="n"><span class="pre">func</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.12)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.StationaryBootstrap.conf_int.reps" title="arch.bootstrap.StationaryBootstrap.conf_int.reps (Python parameter) — Number of bootstrap replications"><span class="n"><span class="pre">reps</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.StationaryBootstrap.conf_int.method" title="arch.bootstrap.StationaryBootstrap.conf_int.method (Python parameter) — One of 'basic', 'percentile', 'studentized', 'norm' (identical to 'var', 'cov'), 'bc' (identical to 'debiased', 'bias-corrected'), or 'bca'"><span class="n"><span class="pre">method</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'basic'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'percentile'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'studentized'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'norm'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'bc'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'bca'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;basic&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.StationaryBootstrap.conf_int.size" title="arch.bootstrap.StationaryBootstrap.conf_int.size (Python parameter) — Coverage of confidence interval"><span class="n"><span class="pre">size</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mf">0.95</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.StationaryBootstrap.conf_int.tail" title="arch.bootstrap.StationaryBootstrap.conf_int.tail (Python parameter) — One of 'two', 'upper' or 'lower'."><span class="n"><span class="pre">tail</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'two'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'upper'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'lower'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;two&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.StationaryBootstrap.conf_int.extra_kwargs" title="arch.bootstrap.StationaryBootstrap.conf_int.extra_kwargs (Python parameter) — Extra keyword arguments to use when calling func and std_err_func, when appropriate"><span class="n"><span class="pre">extra_kwargs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.12)"><span class="pre">Any</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.StationaryBootstrap.conf_int.reuse" title="arch.bootstrap.StationaryBootstrap.conf_int.reuse (Python parameter) — Flag indicating whether to reuse previously computed bootstrap results.  This allows alternative methods to be compared without rerunning the bootstrap simulation.  Reuse is ignored if reps is not the same across multiple runs, func changes across calls, or method is 'studentized'."><span class="n"><span class="pre">reuse</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.StationaryBootstrap.conf_int.sampling" title="arch.bootstrap.StationaryBootstrap.conf_int.sampling (Python parameter) — Type of sampling to use: 'nonparametric', 'semi-parametric' (or 'semi') or 'parametric'.  The default is 'nonparametric'.  See notes about the changes to func required when using 'semi' or 'parametric'."><span class="n"><span class="pre">sampling</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'nonparametric'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'semi-parametric'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'semi'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'parametric'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'semiparametric'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;nonparametric&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.StationaryBootstrap.conf_int.std_err_func" title="arch.bootstrap.StationaryBootstrap.conf_int.std_err_func (Python parameter) — Function to use when standardizing estimated parameters when using the studentized bootstrap.  Providing an analytical function eliminates the need for a nested bootstrap"><span class="n"><span class="pre">std_err_func</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.12)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.StationaryBootstrap.conf_int.studentize_reps" title="arch.bootstrap.StationaryBootstrap.conf_int.studentize_reps (Python parameter) — Number of bootstraps to use in the inner bootstrap when using the studentized bootstrap.  Ignored when std_err_func is provided"><span class="n"><span class="pre">studentize_reps</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="headerlink" href="#arch.bootstrap.StationaryBootstrap.conf_int" title="Link to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">StationaryBootstrap.</span></span><span class="sig-name descname"><span class="pre">conf_int</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.StationaryBootstrap.conf_int.func" title="arch.bootstrap.StationaryBootstrap.conf_int.func (Python parameter) — Function the computes parameter values.  See Notes for requirements"><span class="n"><span class="pre">func</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.12)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.StationaryBootstrap.conf_int.reps" title="arch.bootstrap.StationaryBootstrap.conf_int.reps (Python parameter) — Number of bootstrap replications"><span class="n"><span class="pre">reps</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.StationaryBootstrap.conf_int.method" title="arch.bootstrap.StationaryBootstrap.conf_int.method (Python parameter) — One of 'basic', 'percentile', 'studentized', 'norm' (identical to 'var', 'cov'), 'bc' (identical to 'debiased', 'bias-corrected'), or 'bca'"><span class="n"><span class="pre">method</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'basic'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'percentile'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'studentized'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'norm'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'bc'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'bca'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;basic&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.StationaryBootstrap.conf_int.size" title="arch.bootstrap.StationaryBootstrap.conf_int.size (Python parameter) — Coverage of confidence interval"><span class="n"><span class="pre">size</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mf">0.95</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.StationaryBootstrap.conf_int.tail" title="arch.bootstrap.StationaryBootstrap.conf_int.tail (Python parameter) — One of 'two', 'upper' or 'lower'."><span class="n"><span class="pre">tail</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'two'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'upper'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'lower'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;two&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.StationaryBootstrap.conf_int.extra_kwargs" title="arch.bootstrap.StationaryBootstrap.conf_int.extra_kwargs (Python parameter) — Extra keyword arguments to use when calling func and std_err_func, when appropriate"><span class="n"><span class="pre">extra_kwargs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.12)"><span class="pre">Any</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.StationaryBootstrap.conf_int.reuse" title="arch.bootstrap.StationaryBootstrap.conf_int.reuse (Python parameter) — Flag indicating whether to reuse previously computed bootstrap results.  This allows alternative methods to be compared without rerunning the bootstrap simulation.  Reuse is ignored if reps is not the same across multiple runs, func changes across calls, or method is 'studentized'."><span class="n"><span class="pre">reuse</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.StationaryBootstrap.conf_int.sampling" title="arch.bootstrap.StationaryBootstrap.conf_int.sampling (Python parameter) — Type of sampling to use: 'nonparametric', 'semi-parametric' (or 'semi') or 'parametric'.  The default is 'nonparametric'.  See notes about the changes to func required when using 'semi' or 'parametric'."><span class="n"><span class="pre">sampling</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'nonparametric'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'semi-parametric'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'semi'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'parametric'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'semiparametric'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;nonparametric&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.StationaryBootstrap.conf_int.std_err_func" title="arch.bootstrap.StationaryBootstrap.conf_int.std_err_func (Python parameter) — Function to use when standardizing estimated parameters when using the studentized bootstrap.  Providing an analytical function eliminates the need for a nested bootstrap"><span class="n"><span class="pre">std_err_func</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.12)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.StationaryBootstrap.conf_int.studentize_reps" title="arch.bootstrap.StationaryBootstrap.conf_int.studentize_reps (Python parameter) — Number of bootstraps to use in the inner bootstrap when using the studentized bootstrap.  Ignored when std_err_func is provided"><span class="n"><span class="pre">studentize_reps</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="headerlink" href="#arch.bootstrap.StationaryBootstrap.conf_int" title="Link to this definition">¶</a></dt>
 <dd><dl class="field-list simple">
 <dt class="field-odd" id="arch.bootstrap.StationaryBootstrap.conf_int-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.StationaryBootstrap.conf_int-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
@@ -1212,7 +1212,7 @@ <h1 id="arch-bootstrap-stationarybootstrap-conf-int">arch.bootstrap.StationaryBo
 <dt id="arch.bootstrap.StationaryBootstrap.conf_int.extra_kwargs"><span class="n sig-name">extra_kwargs</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)">dict</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)">str</a><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.12)">Any</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.bootstrap.StationaryBootstrap.conf_int.extra_kwargs" title="Permalink to this definition">¶</a></dt><dd><p>Extra keyword arguments to use when calling func and std_err_func,
 when appropriate</p>
 </dd>
-<dt id="arch.bootstrap.StationaryBootstrap.conf_int.std_err_func"><span class="n sig-name">std_err_func</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.12)">Callable</a><span class="p">[</span><span class="p">[</span><span class="p">...</span><span class="p">]</span><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.bootstrap.StationaryBootstrap.conf_int.std_err_func" title="Permalink to this definition">¶</a></dt><dd><p>Function to use when standardizing estimated parameters when using
+<dt id="arch.bootstrap.StationaryBootstrap.conf_int.std_err_func"><span class="n sig-name">std_err_func</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.12)">Callable</a><span class="p">[</span><span class="p">[</span><span class="p">...</span><span class="p">]</span><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.bootstrap.StationaryBootstrap.conf_int.std_err_func" title="Permalink to this definition">¶</a></dt><dd><p>Function to use when standardizing estimated parameters when using
 the studentized bootstrap.  Providing an analytical function
 eliminates the need for a nested bootstrap</p>
 </dd>
@@ -1387,7 +1387,7 @@ <h1 id="arch-bootstrap-stationarybootstrap-conf-int">arch.bootstrap.StationaryBo
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diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.cov.html b/devel/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.cov.html
index 0a4cfefd1c..cf0223a911 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.cov.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.cov.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.StationaryBootstrap.cov - arch v6.2.1 (+21)</title>
+        <title>arch.bootstrap.StationaryBootstrap.cov - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
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@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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     <div class="md-nav__source">
@@ -1098,12 +1098,12 @@
 <h1 id="arch-bootstrap-stationarybootstrap-cov">arch.bootstrap.StationaryBootstrap.cov<a class="headerlink" href="#arch-bootstrap-stationarybootstrap-cov" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.bootstrap.StationaryBootstrap.cov">
-<span class="sig-prename descclassname"><span class="pre">StationaryBootstrap.</span></span><span class="sig-name descname"><span class="pre">cov</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.StationaryBootstrap.cov.func" title="arch.bootstrap.StationaryBootstrap.cov.func (Python parameter) — Callable function that returns the statistic of interest as a 1-d array"><span class="n"><span class="pre">func</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.12)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="p"><span class="pre">]</span></span></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.StationaryBootstrap.cov.reps" title="arch.bootstrap.StationaryBootstrap.cov.reps (Python parameter) — Number of bootstrap replications"><span class="n"><span class="pre">reps</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.StationaryBootstrap.cov.recenter" title="arch.bootstrap.StationaryBootstrap.cov.recenter (Python parameter) — Whether to center the bootstrap variance estimator on the average of the bootstrap samples (True) or to center on the original sample estimate (False).  Default is True."><span class="n"><span class="pre">recenter</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.StationaryBootstrap.cov.extra_kwargs" title="arch.bootstrap.StationaryBootstrap.cov.extra_kwargs (Python parameter) — Dictionary of extra keyword arguments to pass to func"><span class="n"><span class="pre">extra_kwargs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.12)"><span class="pre">Any</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="headerlink" href="#arch.bootstrap.StationaryBootstrap.cov" title="Link to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">StationaryBootstrap.</span></span><span class="sig-name descname"><span class="pre">cov</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.StationaryBootstrap.cov.func" title="arch.bootstrap.StationaryBootstrap.cov.func (Python parameter) — Callable function that returns the statistic of interest as a 1-d array"><span class="n"><span class="pre">func</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.12)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a><span class="p"><span class="pre">]</span></span></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.StationaryBootstrap.cov.reps" title="arch.bootstrap.StationaryBootstrap.cov.reps (Python parameter) — Number of bootstrap replications"><span class="n"><span class="pre">reps</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.StationaryBootstrap.cov.recenter" title="arch.bootstrap.StationaryBootstrap.cov.recenter (Python parameter) — Whether to center the bootstrap variance estimator on the average of the bootstrap samples (True) or to center on the original sample estimate (False).  Default is True."><span class="n"><span class="pre">recenter</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.StationaryBootstrap.cov.extra_kwargs" title="arch.bootstrap.StationaryBootstrap.cov.extra_kwargs (Python parameter) — Dictionary of extra keyword arguments to pass to func"><span class="n"><span class="pre">extra_kwargs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.12)"><span class="pre">Any</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="headerlink" href="#arch.bootstrap.StationaryBootstrap.cov" title="Link to this definition">¶</a></dt>
 <dd><p>Compute parameter covariance using bootstrap</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.bootstrap.StationaryBootstrap.cov-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.StationaryBootstrap.cov-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.bootstrap.StationaryBootstrap.cov.func"><span class="n sig-name">func</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.12)">Callable</a><span class="p">[</span><span class="p">[</span><span class="p">...</span><span class="p">]</span><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a><span class="p">]</span></span></span><a class="headerlink" href="#arch.bootstrap.StationaryBootstrap.cov.func" title="Permalink to this definition">¶</a></dt><dd><p>Callable function that returns the statistic of interest as a
+<dt id="arch.bootstrap.StationaryBootstrap.cov.func"><span class="n sig-name">func</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.12)">Callable</a><span class="p">[</span><span class="p">[</span><span class="p">...</span><span class="p">]</span><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a><span class="p">]</span></span></span><a class="headerlink" href="#arch.bootstrap.StationaryBootstrap.cov.func" title="Permalink to this definition">¶</a></dt><dd><p>Callable function that returns the statistic of interest as a
 1-d array</p>
 </dd>
 <dt id="arch.bootstrap.StationaryBootstrap.cov.reps"><span class="n sig-name">reps</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code><a class="headerlink" href="#arch.bootstrap.StationaryBootstrap.cov.reps" title="Permalink to this definition">¶</a></dt><dd><p>Number of bootstrap replications</p>
@@ -1274,7 +1274,7 @@ <h1 id="arch-bootstrap-stationarybootstrap-cov">arch.bootstrap.StationaryBootstr
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
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diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.generator.html b/devel/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.generator.html
index 0a4e1bc652..dd68ebb52c 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.generator.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.generator.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.StationaryBootstrap.generator - arch v6.2.1 (+21)</title>
+        <title>arch.bootstrap.StationaryBootstrap.generator - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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@@ -1164,7 +1164,7 @@ <h1 id="arch-bootstrap-stationarybootstrap-generator">arch.bootstrap.StationaryB
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-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
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diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.get_state.html b/devel/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.get_state.html
index 87ea1c0d7b..16e87d958f 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.get_state.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.get_state.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.StationaryBootstrap.get_state - arch v6.2.1 (+21)</title>
+        <title>arch.bootstrap.StationaryBootstrap.get_state - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -1142,7 +1142,7 @@ <h1 id="arch-bootstrap-stationarybootstrap-get-state">arch.bootstrap.StationaryB
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.index.html b/devel/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.index.html
index 7ceb7b9de9..f24156c9a0 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.index.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.index.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.StationaryBootstrap.index - arch v6.2.1 (+21)</title>
+        <title>arch.bootstrap.StationaryBootstrap.index - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -1094,7 +1094,7 @@ <h1 id="arch-bootstrap-stationarybootstrap-index">arch.bootstrap.StationaryBoots
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.random_state.html b/devel/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.random_state.html
index 27288a1286..f8fc1df9d2 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.random_state.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.random_state.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.StationaryBootstrap.random_state - arch v6.2.1 (+21)</title>
+        <title>arch.bootstrap.StationaryBootstrap.random_state - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -1162,7 +1162,7 @@ <h1 id="arch-bootstrap-stationarybootstrap-random-state">arch.bootstrap.Stationa
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.reset.html b/devel/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.reset.html
index 9847fe8580..ef41567f9d 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.reset.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.reset.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.StationaryBootstrap.reset - arch v6.2.1 (+21)</title>
+        <title>arch.bootstrap.StationaryBootstrap.reset - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -1156,7 +1156,7 @@ <h1 id="arch-bootstrap-stationarybootstrap-reset">arch.bootstrap.StationaryBoots
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.seed.html b/devel/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.seed.html
index 17659ad22f..3bf2ca9e4a 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.seed.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.seed.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.StationaryBootstrap.seed - arch v6.2.1 (+21)</title>
+        <title>arch.bootstrap.StationaryBootstrap.seed - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -1154,7 +1154,7 @@ <h1 id="arch-bootstrap-stationarybootstrap-seed">arch.bootstrap.StationaryBootst
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.set_state.html b/devel/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.set_state.html
index fa00d1894e..5ae700d9a0 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.set_state.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.set_state.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.StationaryBootstrap.set_state - arch v6.2.1 (+21)</title>
+        <title>arch.bootstrap.StationaryBootstrap.set_state - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -1154,7 +1154,7 @@ <h1 id="arch-bootstrap-stationarybootstrap-set-state">arch.bootstrap.StationaryB
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.state.html b/devel/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.state.html
index c46a4dcf79..b224e3ddde 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.state.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.state.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.StationaryBootstrap.state - arch v6.2.1 (+21)</title>
+        <title>arch.bootstrap.StationaryBootstrap.state - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -1144,7 +1144,7 @@ <h1 id="arch-bootstrap-stationarybootstrap-state">arch.bootstrap.StationaryBoots
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.update_indices.html b/devel/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.update_indices.html
index 4a6f75b571..2115a93f0a 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.update_indices.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.update_indices.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.StationaryBootstrap.update_indices - arch v6.2.1 (+21)</title>
+        <title>arch.bootstrap.StationaryBootstrap.update_indices - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -1095,7 +1095,7 @@ <h1 id="arch-bootstrap-stationarybootstrap-update-indices">arch.bootstrap.Statio
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/devel/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.var.html b/devel/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.var.html
index a6082850ae..ce7a741bda 100644
--- a/devel/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.var.html
+++ b/devel/bootstrap/generated/generated/arch.bootstrap.StationaryBootstrap.var.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.StationaryBootstrap.var - arch v6.2.1 (+21)</title>
+        <title>arch.bootstrap.StationaryBootstrap.var - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -1098,12 +1098,12 @@
 <h1 id="arch-bootstrap-stationarybootstrap-var">arch.bootstrap.StationaryBootstrap.var<a class="headerlink" href="#arch-bootstrap-stationarybootstrap-var" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.bootstrap.StationaryBootstrap.var">
-<span class="sig-prename descclassname"><span class="pre">StationaryBootstrap.</span></span><span class="sig-name descname"><span class="pre">var</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.StationaryBootstrap.var.func" title="arch.bootstrap.StationaryBootstrap.var.func (Python parameter) — Callable function that returns the statistic of interest as a 1-d array"><span class="n"><span class="pre">func</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.12)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="p"><span class="pre">]</span></span></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.StationaryBootstrap.var.reps" title="arch.bootstrap.StationaryBootstrap.var.reps (Python parameter) — Number of bootstrap replications"><span class="n"><span class="pre">reps</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.StationaryBootstrap.var.recenter" title="arch.bootstrap.StationaryBootstrap.var.recenter (Python parameter) — Whether to center the bootstrap variance estimator on the average of the bootstrap samples (True) or to center on the original sample estimate (False).  Default is True."><span class="n"><span class="pre">recenter</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.StationaryBootstrap.var.extra_kwargs" title="arch.bootstrap.StationaryBootstrap.var.extra_kwargs (Python parameter) — Dictionary of extra keyword arguments to pass to func"><span class="n"><span class="pre">extra_kwargs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.12)"><span class="pre">Any</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="headerlink" href="#arch.bootstrap.StationaryBootstrap.var" title="Link to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">StationaryBootstrap.</span></span><span class="sig-name descname"><span class="pre">var</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.StationaryBootstrap.var.func" title="arch.bootstrap.StationaryBootstrap.var.func (Python parameter) — Callable function that returns the statistic of interest as a 1-d array"><span class="n"><span class="pre">func</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.12)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a><span class="p"><span class="pre">]</span></span></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.StationaryBootstrap.var.reps" title="arch.bootstrap.StationaryBootstrap.var.reps (Python parameter) — Number of bootstrap replications"><span class="n"><span class="pre">reps</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.StationaryBootstrap.var.recenter" title="arch.bootstrap.StationaryBootstrap.var.recenter (Python parameter) — Whether to center the bootstrap variance estimator on the average of the bootstrap samples (True) or to center on the original sample estimate (False).  Default is True."><span class="n"><span class="pre">recenter</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.StationaryBootstrap.var.extra_kwargs" title="arch.bootstrap.StationaryBootstrap.var.extra_kwargs (Python parameter) — Dictionary of extra keyword arguments to pass to func"><span class="n"><span class="pre">extra_kwargs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.12)"><span class="pre">Any</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="headerlink" href="#arch.bootstrap.StationaryBootstrap.var" title="Link to this definition">¶</a></dt>
 <dd><p>Compute parameter variance using bootstrap</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.bootstrap.StationaryBootstrap.var-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.StationaryBootstrap.var-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.bootstrap.StationaryBootstrap.var.func"><span class="n sig-name">func</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.12)">Callable</a><span class="p">[</span><span class="p">[</span><span class="p">...</span><span class="p">]</span><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a><span class="p">]</span></span></span><a class="headerlink" href="#arch.bootstrap.StationaryBootstrap.var.func" title="Permalink to this definition">¶</a></dt><dd><p>Callable function that returns the statistic of interest as a
+<dt id="arch.bootstrap.StationaryBootstrap.var.func"><span class="n sig-name">func</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.12)">Callable</a><span class="p">[</span><span class="p">[</span><span class="p">...</span><span class="p">]</span><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a><span class="p">]</span></span></span><a class="headerlink" href="#arch.bootstrap.StationaryBootstrap.var.func" title="Permalink to this definition">¶</a></dt><dd><p>Callable function that returns the statistic of interest as a
 1-d array</p>
 </dd>
 <dt id="arch.bootstrap.StationaryBootstrap.var.reps"><span class="n sig-name">reps</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code><a class="headerlink" href="#arch.bootstrap.StationaryBootstrap.var.reps" title="Permalink to this definition">¶</a></dt><dd><p>Number of bootstrap replications</p>
@@ -1274,7 +1274,7 @@ <h1 id="arch-bootstrap-stationarybootstrap-var">arch.bootstrap.StationaryBootstr
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/devel/bootstrap/iid-bootstraps.html b/devel/bootstrap/iid-bootstraps.html
index 9633ed2eb1..a6f8e99deb 100644
--- a/devel/bootstrap/iid-bootstraps.html
+++ b/devel/bootstrap/iid-bootstraps.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>Independent, Identical Distributed Data (i.i.d.) - arch v6.2.1 (+21)</title>
+        <title>Independent, Identical Distributed Data (i.i.d.) - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
@@ -87,7 +87,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -97,7 +97,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -184,10 +184,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -843,7 +843,7 @@ <h1 id="independent-samples">Independent Samples<a class="headerlink" href="#ind
     <script id="__config" type="application/json">{"base": "..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/devel/bootstrap/low-level-interface.html b/devel/bootstrap/low-level-interface.html
index 57493f7203..312adc23bb 100644
--- a/devel/bootstrap/low-level-interface.html
+++ b/devel/bootstrap/low-level-interface.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>Low-level Interfaces - arch v6.2.1 (+21)</title>
+        <title>Low-level Interfaces - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -788,7 +788,7 @@ <h2 id="the-bootstrap-iterator">The Bootstrap Iterator<a class="headerlink" href
     <script id="__config" type="application/json">{"base": "..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/devel/bootstrap/parameter-covariance-estimation.html b/devel/bootstrap/parameter-covariance-estimation.html
index 1a2f44755c..fbdda7f45d 100644
--- a/devel/bootstrap/parameter-covariance-estimation.html
+++ b/devel/bootstrap/parameter-covariance-estimation.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>Covariance Estimation - arch v6.2.1 (+21)</title>
+        <title>Covariance Estimation - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
@@ -87,7 +87,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -97,7 +97,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -184,10 +184,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -722,7 +722,7 @@ <h1 id="covariance-estimation">Covariance Estimation<a class="headerlink" href="
     <script id="__config" type="application/json">{"base": "..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/devel/bootstrap/semiparametric-parametric-bootstrap.html b/devel/bootstrap/semiparametric-parametric-bootstrap.html
index 8cf2286153..5c404e29f0 100644
--- a/devel/bootstrap/semiparametric-parametric-bootstrap.html
+++ b/devel/bootstrap/semiparametric-parametric-bootstrap.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>Semiparametric Bootstraps - arch v6.2.1 (+21)</title>
+        <title>Semiparametric Bootstraps - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -918,7 +918,7 @@ <h1 id="parametric-bootstraps"><span id="id2"></span>Parametric Bootstraps<a cla
     <script id="__config" type="application/json">{"base": "..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/devel/bootstrap/timeseries-bootstraps.html b/devel/bootstrap/timeseries-bootstraps.html
index 14cc223489..d82ac327f2 100644
--- a/devel/bootstrap/timeseries-bootstraps.html
+++ b/devel/bootstrap/timeseries-bootstraps.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>Time-series Bootstraps - arch v6.2.1 (+21)</title>
+        <title>Time-series Bootstraps - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
@@ -87,7 +87,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -97,7 +97,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -184,10 +184,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -830,7 +830,7 @@ <h1 id="time-series-bootstraps">Time-series Bootstraps<a class="headerlink" href
     <script id="__config" type="application/json">{"base": "..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/devel/changes.html b/devel/changes.html
index bf0f3f8053..1de523ad9f 100644
--- a/devel/changes.html
+++ b/devel/changes.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>Change Logs - arch v6.2.1 (+21)</title>
+        <title>Change Logs - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL(".",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -831,7 +831,7 @@ <h2 id="past-releases">Past Releases<a class="headerlink" href="#past-releases"
     <script id="__config" type="application/json">{"base": ".", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/devel/changes/1.0.html b/devel/changes/1.0.html
index 3dc4bbb530..ef1bd1def4 100644
--- a/devel/changes/1.0.html
+++ b/devel/changes/1.0.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>Version 1 - arch v6.2.1 (+21)</title>
+        <title>Version 1 - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -673,7 +673,7 @@ <h2 id="version-1-1">Version 1.1<a class="headerlink" href="#version-1-1" title=
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-        <script src="../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
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   </body>
diff --git a/devel/changes/2.0.html b/devel/changes/2.0.html
index 29acff6794..934fa9d908 100644
--- a/devel/changes/2.0.html
+++ b/devel/changes/2.0.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>Version 2 - arch v6.2.1 (+21)</title>
+        <title>Version 2 - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
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-        <link rel="stylesheet" type="text/css" href="../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -707,7 +707,7 @@ <h2 id="version-2-1">Version 2.1<a class="headerlink" href="#version-2-1" title=
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-        <script src="../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/devel/changes/3.0.html b/devel/changes/3.0.html
index 2e637b86b9..221513195c 100644
--- a/devel/changes/3.0.html
+++ b/devel/changes/3.0.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>Version 3 - arch v6.2.1 (+21)</title>
+        <title>Version 3 - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
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-        <link rel="stylesheet" type="text/css" href="../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
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@@ -87,7 +87,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -97,7 +97,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -184,10 +184,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -642,7 +642,7 @@ <h1 id="version-3">Version 3<a class="headerlink" href="#version-3" title="Link
     <script id="__config" type="application/json">{"base": "..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/devel/changes/4.0.html b/devel/changes/4.0.html
index 7f028ee838..838d207337 100644
--- a/devel/changes/4.0.html
+++ b/devel/changes/4.0.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>Version 4 - arch v6.2.1 (+21)</title>
+        <title>Version 4 - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -1126,7 +1126,7 @@ <h2 id="release-4-2">Release 4.2<a class="headerlink" href="#release-4-2" title=
     <script id="__config" type="application/json">{"base": "..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
         <script>window.MathJax = {"tex": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true}, "options": {"ignoreHtmlClass": "tex2jax_ignore|mathjax_ignore|document", "processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
         <script id="MathJax-script" src="../_static/mathjax/tex-mml-chtml.js?v=cadf963e"></script>
diff --git a/devel/changes/5.0.html b/devel/changes/5.0.html
index a7529d4b48..3650cb4b4c 100644
--- a/devel/changes/5.0.html
+++ b/devel/changes/5.0.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>Version 5 - arch v6.2.1 (+21)</title>
+        <title>Version 5 - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -942,7 +942,7 @@ <h3 id="setup">Setup<a class="headerlink" href="#setup" title="Link to this head
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-        <script src="../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/devel/changes/6.0.html b/devel/changes/6.0.html
index 0290fbbedb..97c1e5d955 100644
--- a/devel/changes/6.0.html
+++ b/devel/changes/6.0.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>Version 6 - arch v6.2.1 (+21)</title>
+        <title>Version 6 - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
@@ -87,7 +87,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -97,7 +97,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -184,10 +184,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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diff --git a/devel/covariance/covariance.html b/devel/covariance/covariance.html
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diff --git a/devel/covariance/generated/arch.covariance.kernel.Andrews.html b/devel/covariance/generated/arch.covariance.kernel.Andrews.html
index e05a3b6207..87aa51a306 100644
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@@ -92,7 +92,7 @@
 
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+    <a href="../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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 <h1 id="arch-covariance-kernel-andrews">arch.covariance.kernel.Andrews<a class="headerlink" href="#arch-covariance-kernel-andrews" title="Link to this heading">¶</a></h1>
 <dl class="py class objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.covariance.kernel.Andrews">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.covariance.kernel.</span></span><span class="sig-name descname"><span class="pre">Andrews</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">x</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">bandwidth</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">df_adjust</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">0</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">center</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">weights</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">force_int</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/covariance/kernel.html#Andrews"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.covariance.kernel.Andrews" title="Link to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.covariance.kernel.</span></span><span class="sig-name descname"><span class="pre">Andrews</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">x</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">bandwidth</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">df_adjust</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">0</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">center</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">weights</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">force_int</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/covariance/kernel.html#Andrews"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.covariance.kernel.Andrews" title="Link to this definition">¶</a></dt>
 <dd><p>Alternative name of the QuadraticSpectral covariance estimator.</p>
 <div class="seealso admonition">
 <p class="admonition-title">See also</p>
@@ -1142,7 +1142,7 @@ <h1 id="arch-covariance-kernel-andrews">arch.covariance.kernel.Andrews<a class="
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diff --git a/devel/covariance/generated/arch.covariance.kernel.Bartlett.html b/devel/covariance/generated/arch.covariance.kernel.Bartlett.html
index 71c6bca49a..0739737b6f 100644
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+++ b/devel/covariance/generated/arch.covariance.kernel.Bartlett.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.covariance.kernel.Bartlett - arch v6.2.1 (+21)</title>
+        <title>arch.covariance.kernel.Bartlett - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
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@@ -1096,12 +1096,12 @@
 <h1 id="arch-covariance-kernel-bartlett">arch.covariance.kernel.Bartlett<a class="headerlink" href="#arch-covariance-kernel-bartlett" title="Link to this heading">¶</a></h1>
 <dl class="py class objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.covariance.kernel.Bartlett">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.covariance.kernel.</span></span><span class="sig-name descname"><span class="pre">Bartlett</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.Bartlett.__init__.x" title="arch.covariance.kernel.Bartlett.__init__.x (Python parameter) — The data to use in covariance estimation."><span class="n"><span class="pre">x</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.Bartlett.__init__.bandwidth" title="arch.covariance.kernel.Bartlett.__init__.bandwidth (Python parameter) — The kernel's bandwidth.  If None, optimal bandwidth is estimated."><span class="n"><span class="pre">bandwidth</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.Bartlett.__init__.df_adjust" title="arch.covariance.kernel.Bartlett.__init__.df_adjust (Python parameter) — Degrees of freedom to remove when adjusting the covariance. Uses the number of observations in x minus df_adjust when dividing inner-products."><span class="n"><span class="pre">df_adjust</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">0</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.Bartlett.__init__.center" title="arch.covariance.kernel.Bartlett.__init__.center (Python parameter) — A flag indicating whether x should be demeaned before estimating the covariance."><span class="n"><span class="pre">center</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.Bartlett.__init__.weights" title="arch.covariance.kernel.Bartlett.__init__.weights (Python parameter) — An array of weights used to combine when estimating optimal bandwidth. If not provided, a vector of 1s is used. Must have nvar elements."><span class="n"><span class="pre">weights</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.Bartlett.__init__.force_int" title="arch.covariance.kernel.Bartlett.__init__.force_int (Python parameter) — Force bandwidth to be an integer."><span class="n"><span class="pre">force_int</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/covariance/kernel.html#Bartlett"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.covariance.kernel.Bartlett" title="Link to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.covariance.kernel.</span></span><span class="sig-name descname"><span class="pre">Bartlett</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.Bartlett.__init__.x" title="arch.covariance.kernel.Bartlett.__init__.x (Python parameter) — The data to use in covariance estimation."><span class="n"><span class="pre">x</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.Bartlett.__init__.bandwidth" title="arch.covariance.kernel.Bartlett.__init__.bandwidth (Python parameter) — The kernel's bandwidth.  If None, optimal bandwidth is estimated."><span class="n"><span class="pre">bandwidth</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.Bartlett.__init__.df_adjust" title="arch.covariance.kernel.Bartlett.__init__.df_adjust (Python parameter) — Degrees of freedom to remove when adjusting the covariance. Uses the number of observations in x minus df_adjust when dividing inner-products."><span class="n"><span class="pre">df_adjust</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">0</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.Bartlett.__init__.center" title="arch.covariance.kernel.Bartlett.__init__.center (Python parameter) — A flag indicating whether x should be demeaned before estimating the covariance."><span class="n"><span class="pre">center</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.Bartlett.__init__.weights" title="arch.covariance.kernel.Bartlett.__init__.weights (Python parameter) — An array of weights used to combine when estimating optimal bandwidth. If not provided, a vector of 1s is used. Must have nvar elements."><span class="n"><span class="pre">weights</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.Bartlett.__init__.force_int" title="arch.covariance.kernel.Bartlett.__init__.force_int (Python parameter) — Force bandwidth to be an integer."><span class="n"><span class="pre">force_int</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/covariance/kernel.html#Bartlett"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.covariance.kernel.Bartlett" title="Link to this definition">¶</a></dt>
 <dd><p>Bartlett’s (Newey-West) kernel covariance estimation.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.covariance.kernel.Bartlett-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.Bartlett-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.covariance.kernel.Bartlett.__init__.x"><span class="n sig-name">x</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.covariance.kernel.Bartlett.__init__.x" title="Permalink to this definition">¶</a></dt><dd><p>The data to use in covariance estimation.</p>
+<dt id="arch.covariance.kernel.Bartlett.__init__.x"><span class="n sig-name">x</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a></span></span><a class="headerlink" href="#arch.covariance.kernel.Bartlett.__init__.x" title="Permalink to this definition">¶</a></dt><dd><p>The data to use in covariance estimation.</p>
 </dd>
 <dt id="arch.covariance.kernel.Bartlett.__init__.bandwidth"><span class="n sig-name">bandwidth</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.covariance.kernel.Bartlett.__init__.bandwidth" title="Permalink to this definition">¶</a></dt><dd><p>The kernel’s bandwidth.  If None, optimal bandwidth is estimated.</p>
 </dd>
@@ -1112,7 +1112,7 @@ <h1 id="arch-covariance-kernel-bartlett">arch.covariance.kernel.Bartlett<a class
 <dt id="arch.covariance.kernel.Bartlett.__init__.center"><span class="n sig-name">center</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)">bool</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code><a class="headerlink" href="#arch.covariance.kernel.Bartlett.__init__.center" title="Permalink to this definition">¶</a></dt><dd><p>A flag indicating whether x should be demeaned before estimating the
 covariance.</p>
 </dd>
-<dt id="arch.covariance.kernel.Bartlett.__init__.weights"><span class="n sig-name">weights</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.covariance.kernel.Bartlett.__init__.weights" title="Permalink to this definition">¶</a></dt><dd><p>An array of weights used to combine when estimating optimal bandwidth.
+<dt id="arch.covariance.kernel.Bartlett.__init__.weights"><span class="n sig-name">weights</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.covariance.kernel.Bartlett.__init__.weights" title="Permalink to this definition">¶</a></dt><dd><p>An array of weights used to combine when estimating optimal bandwidth.
 If not provided, a vector of 1s is used. Must have nvar elements.</p>
 </dd>
 <dt id="arch.covariance.kernel.Bartlett.__init__.force_int"><span class="n sig-name">force_int</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)">bool</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code><a class="headerlink" href="#arch.covariance.kernel.Bartlett.__init__.force_int" title="Permalink to this definition">¶</a></dt><dd><p>Force bandwidth to be an integer.</p>
@@ -1281,7 +1281,7 @@ <h1 id="arch-covariance-kernel-bartlett">arch.covariance.kernel.Bartlett<a class
     <script id="__config" type="application/json">{"base": "../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
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diff --git a/devel/covariance/generated/arch.covariance.kernel.CovarianceEstimate.html b/devel/covariance/generated/arch.covariance.kernel.CovarianceEstimate.html
index 1d5f5b18fa..1b5aecbdd8 100644
--- a/devel/covariance/generated/arch.covariance.kernel.CovarianceEstimate.html
+++ b/devel/covariance/generated/arch.covariance.kernel.CovarianceEstimate.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.covariance.kernel.CovarianceEstimate - arch v6.2.1 (+21)</title>
+        <title>arch.covariance.kernel.CovarianceEstimate - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
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@@ -92,7 +92,7 @@
 
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-    <a href="../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
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-    <a href="../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../_static/bw-logo.svg" alt="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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     <div class="md-nav__source">
@@ -785,7 +785,7 @@
 <h1 id="arch-covariance-kernel-covarianceestimate">arch.covariance.kernel.CovarianceEstimate<a class="headerlink" href="#arch-covariance-kernel-covarianceestimate" title="Link to this heading">¶</a></h1>
 <dl class="py class objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.covariance.kernel.CovarianceEstimate">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.covariance.kernel.</span></span><span class="sig-name descname"><span class="pre">CovarianceEstimate</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.CovarianceEstimate.__init__.short_run" title="arch.covariance.kernel.CovarianceEstimate.__init__.short_run (Python parameter) — The short-run covariance estimate."><span class="n"><span class="pre">short_run</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.CovarianceEstimate.__init__.one_sided_strict" title="arch.covariance.kernel.CovarianceEstimate.__init__.one_sided_strict (Python parameter) — The one-sided-strict covariance estimate. If not provided, computed from short_run and one_sided_strict."><span class="n"><span class="pre">one_sided_strict</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.CovarianceEstimate.__init__.columns" title="arch.covariance.kernel.CovarianceEstimate.__init__.columns (Python parameter) — Column labels to use if covariance estimates are returned as DataFrames."><span class="n"><span class="pre">columns</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Index.html#pandas.Index" title="(in pandas v2.1.1)"><span class="pre">Index</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.CovarianceEstimate.__init__.long_run" title="arch.covariance.kernel.CovarianceEstimate.__init__.long_run (Python parameter) — The long-run covariance estimate. If not provided, computed from short_run and one_sided_strict."><span class="n"><span class="pre">long_run</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">one_sided</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/covariance/kernel.html#CovarianceEstimate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.covariance.kernel.CovarianceEstimate" title="Link to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.covariance.kernel.</span></span><span class="sig-name descname"><span class="pre">CovarianceEstimate</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.CovarianceEstimate.__init__.short_run" title="arch.covariance.kernel.CovarianceEstimate.__init__.short_run (Python parameter) — The short-run covariance estimate."><span class="n"><span class="pre">short_run</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.CovarianceEstimate.__init__.one_sided_strict" title="arch.covariance.kernel.CovarianceEstimate.__init__.one_sided_strict (Python parameter) — The one-sided-strict covariance estimate. If not provided, computed from short_run and one_sided_strict."><span class="n"><span class="pre">one_sided_strict</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.CovarianceEstimate.__init__.columns" title="arch.covariance.kernel.CovarianceEstimate.__init__.columns (Python parameter) — Column labels to use if covariance estimates are returned as DataFrames."><span class="n"><span class="pre">columns</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Index.html#pandas.Index" title="(in pandas v2.1.4)"><span class="pre">Index</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.CovarianceEstimate.__init__.long_run" title="arch.covariance.kernel.CovarianceEstimate.__init__.long_run (Python parameter) — The long-run covariance estimate. If not provided, computed from short_run and one_sided_strict."><span class="n"><span class="pre">long_run</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">one_sided</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/covariance/kernel.html#CovarianceEstimate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.covariance.kernel.CovarianceEstimate" title="Link to this definition">¶</a></dt>
 <dd><p>Covariance estimate using a long-run covariance estimator</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.covariance.kernel.CovarianceEstimate-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.CovarianceEstimate-parameters" title="Permalink to this headline">¶</a></dt>
@@ -794,7 +794,7 @@ <h1 id="arch-covariance-kernel-covarianceestimate">arch.covariance.kernel.Covari
 </dd>
 <dt id="arch.covariance.kernel.CovarianceEstimate.__init__.one_sided_strict"><span class="n sig-name">one_sided_strict</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.covariance.kernel.CovarianceEstimate.__init__.one_sided_strict" title="Permalink to this definition">¶</a></dt><dd><p>The one-sided strict covariance estimate.</p>
 </dd>
-<dt id="arch.covariance.kernel.CovarianceEstimate.__init__.columns"><span class="n sig-name">columns</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Index.html#pandas.Index" title="(in pandas v2.1.1)">Index</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)">list</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)">str</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.covariance.kernel.CovarianceEstimate.__init__.columns" title="Permalink to this definition">¶</a></dt><dd><p>Column labels to use if covariance estimates are returned as
+<dt id="arch.covariance.kernel.CovarianceEstimate.__init__.columns"><span class="n sig-name">columns</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Index.html#pandas.Index" title="(in pandas v2.1.4)">Index</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)">list</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)">str</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.covariance.kernel.CovarianceEstimate.__init__.columns" title="Permalink to this definition">¶</a></dt><dd><p>Column labels to use if covariance estimates are returned as
 DataFrames.</p>
 </dd>
 <dt id="arch.covariance.kernel.CovarianceEstimate.__init__.long_run"><span class="n sig-name">long_run</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.covariance.kernel.CovarianceEstimate.__init__.long_run" title="Permalink to this definition">¶</a></dt><dd><p>The long-run covariance estimate. If not provided, computed from
@@ -952,7 +952,7 @@ <h1 id="arch-covariance-kernel-covarianceestimate">arch.covariance.kernel.Covari
     <script id="__config" type="application/json">{"base": "../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
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diff --git a/devel/covariance/generated/arch.covariance.kernel.Gallant.html b/devel/covariance/generated/arch.covariance.kernel.Gallant.html
index 69b24076d5..7aa8072bf7 100644
--- a/devel/covariance/generated/arch.covariance.kernel.Gallant.html
+++ b/devel/covariance/generated/arch.covariance.kernel.Gallant.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.covariance.kernel.Gallant - arch v6.2.1 (+21)</title>
+        <title>arch.covariance.kernel.Gallant - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
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@@ -980,7 +980,7 @@
 <h1 id="arch-covariance-kernel-gallant">arch.covariance.kernel.Gallant<a class="headerlink" href="#arch-covariance-kernel-gallant" title="Link to this heading">¶</a></h1>
 <dl class="py class objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.covariance.kernel.Gallant">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.covariance.kernel.</span></span><span class="sig-name descname"><span class="pre">Gallant</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">x</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">bandwidth</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">df_adjust</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">0</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">center</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">weights</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">force_int</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/covariance/kernel.html#Gallant"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.covariance.kernel.Gallant" title="Link to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.covariance.kernel.</span></span><span class="sig-name descname"><span class="pre">Gallant</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">x</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">bandwidth</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">df_adjust</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">0</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">center</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">weights</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">force_int</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/covariance/kernel.html#Gallant"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.covariance.kernel.Gallant" title="Link to this definition">¶</a></dt>
 <dd><p>Alternative name for Parzen covariance estimator.</p>
 <div class="seealso admonition">
 <p class="admonition-title">See also</p>
@@ -1142,7 +1142,7 @@ <h1 id="arch-covariance-kernel-gallant">arch.covariance.kernel.Gallant<a class="
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diff --git a/devel/covariance/generated/arch.covariance.kernel.NeweyWest.html b/devel/covariance/generated/arch.covariance.kernel.NeweyWest.html
index eef6de11d7..d8586f23b6 100644
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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+    <a href="../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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@@ -980,7 +980,7 @@
 <h1 id="arch-covariance-kernel-neweywest">arch.covariance.kernel.NeweyWest<a class="headerlink" href="#arch-covariance-kernel-neweywest" title="Link to this heading">¶</a></h1>
 <dl class="py class objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.covariance.kernel.NeweyWest">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.covariance.kernel.</span></span><span class="sig-name descname"><span class="pre">NeweyWest</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">x</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">bandwidth</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">df_adjust</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">0</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">center</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">weights</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">force_int</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/covariance/kernel.html#NeweyWest"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.covariance.kernel.NeweyWest" title="Link to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.covariance.kernel.</span></span><span class="sig-name descname"><span class="pre">NeweyWest</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">x</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">bandwidth</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">df_adjust</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">0</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">center</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">weights</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">force_int</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/covariance/kernel.html#NeweyWest"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.covariance.kernel.NeweyWest" title="Link to this definition">¶</a></dt>
 <dd><p>Alternative name for Bartlett covariance estimator.</p>
 <div class="seealso admonition">
 <p class="admonition-title">See also</p>
@@ -1142,7 +1142,7 @@ <h1 id="arch-covariance-kernel-neweywest">arch.covariance.kernel.NeweyWest<a cla
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diff --git a/devel/covariance/generated/arch.covariance.kernel.Parzen.html b/devel/covariance/generated/arch.covariance.kernel.Parzen.html
index ab2c6a00be..b49506c24e 100644
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@@ -15,7 +15,7 @@
     
   
       
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@@ -1096,12 +1096,12 @@
 <h1 id="arch-covariance-kernel-parzen">arch.covariance.kernel.Parzen<a class="headerlink" href="#arch-covariance-kernel-parzen" title="Link to this heading">¶</a></h1>
 <dl class="py class objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.covariance.kernel.Parzen">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.covariance.kernel.</span></span><span class="sig-name descname"><span class="pre">Parzen</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.Parzen.__init__.x" title="arch.covariance.kernel.Parzen.__init__.x (Python parameter) — The data to use in covariance estimation."><span class="n"><span class="pre">x</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.Parzen.__init__.bandwidth" title="arch.covariance.kernel.Parzen.__init__.bandwidth (Python parameter) — The kernel's bandwidth.  If None, optimal bandwidth is estimated."><span class="n"><span class="pre">bandwidth</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.Parzen.__init__.df_adjust" title="arch.covariance.kernel.Parzen.__init__.df_adjust (Python parameter) — Degrees of freedom to remove when adjusting the covariance. Uses the number of observations in x minus df_adjust when dividing inner-products."><span class="n"><span class="pre">df_adjust</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">0</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.Parzen.__init__.center" title="arch.covariance.kernel.Parzen.__init__.center (Python parameter) — A flag indicating whether x should be demeaned before estimating the covariance."><span class="n"><span class="pre">center</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.Parzen.__init__.weights" title="arch.covariance.kernel.Parzen.__init__.weights (Python parameter) — An array of weights used to combine when estimating optimal bandwidth. If not provided, a vector of 1s is used. Must have nvar elements."><span class="n"><span class="pre">weights</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.Parzen.__init__.force_int" title="arch.covariance.kernel.Parzen.__init__.force_int (Python parameter) — Force bandwidth to be an integer."><span class="n"><span class="pre">force_int</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/covariance/kernel.html#Parzen"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.covariance.kernel.Parzen" title="Link to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.covariance.kernel.</span></span><span class="sig-name descname"><span class="pre">Parzen</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.Parzen.__init__.x" title="arch.covariance.kernel.Parzen.__init__.x (Python parameter) — The data to use in covariance estimation."><span class="n"><span class="pre">x</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.Parzen.__init__.bandwidth" title="arch.covariance.kernel.Parzen.__init__.bandwidth (Python parameter) — The kernel's bandwidth.  If None, optimal bandwidth is estimated."><span class="n"><span class="pre">bandwidth</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.Parzen.__init__.df_adjust" title="arch.covariance.kernel.Parzen.__init__.df_adjust (Python parameter) — Degrees of freedom to remove when adjusting the covariance. Uses the number of observations in x minus df_adjust when dividing inner-products."><span class="n"><span class="pre">df_adjust</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">0</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.Parzen.__init__.center" title="arch.covariance.kernel.Parzen.__init__.center (Python parameter) — A flag indicating whether x should be demeaned before estimating the covariance."><span class="n"><span class="pre">center</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.Parzen.__init__.weights" title="arch.covariance.kernel.Parzen.__init__.weights (Python parameter) — An array of weights used to combine when estimating optimal bandwidth. If not provided, a vector of 1s is used. Must have nvar elements."><span class="n"><span class="pre">weights</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.Parzen.__init__.force_int" title="arch.covariance.kernel.Parzen.__init__.force_int (Python parameter) — Force bandwidth to be an integer."><span class="n"><span class="pre">force_int</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/covariance/kernel.html#Parzen"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.covariance.kernel.Parzen" title="Link to this definition">¶</a></dt>
 <dd><p>Parzen’s kernel covariance estimation.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.covariance.kernel.Parzen-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.Parzen-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.covariance.kernel.Parzen.__init__.x"><span class="n sig-name">x</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.covariance.kernel.Parzen.__init__.x" title="Permalink to this definition">¶</a></dt><dd><p>The data to use in covariance estimation.</p>
+<dt id="arch.covariance.kernel.Parzen.__init__.x"><span class="n sig-name">x</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a></span></span><a class="headerlink" href="#arch.covariance.kernel.Parzen.__init__.x" title="Permalink to this definition">¶</a></dt><dd><p>The data to use in covariance estimation.</p>
 </dd>
 <dt id="arch.covariance.kernel.Parzen.__init__.bandwidth"><span class="n sig-name">bandwidth</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.covariance.kernel.Parzen.__init__.bandwidth" title="Permalink to this definition">¶</a></dt><dd><p>The kernel’s bandwidth.  If None, optimal bandwidth is estimated.</p>
 </dd>
@@ -1112,7 +1112,7 @@ <h1 id="arch-covariance-kernel-parzen">arch.covariance.kernel.Parzen<a class="he
 <dt id="arch.covariance.kernel.Parzen.__init__.center"><span class="n sig-name">center</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)">bool</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code><a class="headerlink" href="#arch.covariance.kernel.Parzen.__init__.center" title="Permalink to this definition">¶</a></dt><dd><p>A flag indicating whether x should be demeaned before estimating the
 covariance.</p>
 </dd>
-<dt id="arch.covariance.kernel.Parzen.__init__.weights"><span class="n sig-name">weights</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.covariance.kernel.Parzen.__init__.weights" title="Permalink to this definition">¶</a></dt><dd><p>An array of weights used to combine when estimating optimal bandwidth.
+<dt id="arch.covariance.kernel.Parzen.__init__.weights"><span class="n sig-name">weights</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.covariance.kernel.Parzen.__init__.weights" title="Permalink to this definition">¶</a></dt><dd><p>An array of weights used to combine when estimating optimal bandwidth.
 If not provided, a vector of 1s is used. Must have nvar elements.</p>
 </dd>
 <dt id="arch.covariance.kernel.Parzen.__init__.force_int"><span class="n sig-name">force_int</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)">bool</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code><a class="headerlink" href="#arch.covariance.kernel.Parzen.__init__.force_int" title="Permalink to this definition">¶</a></dt><dd><p>Force bandwidth to be an integer.</p>
@@ -1281,7 +1281,7 @@ <h1 id="arch-covariance-kernel-parzen">arch.covariance.kernel.Parzen<a class="he
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index dcde16f064..35560394e0 100644
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+++ b/devel/covariance/generated/arch.covariance.kernel.ParzenCauchy.html
@@ -15,7 +15,7 @@
     
   
       
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-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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@@ -1096,12 +1096,12 @@
 <h1 id="arch-covariance-kernel-parzencauchy">arch.covariance.kernel.ParzenCauchy<a class="headerlink" href="#arch-covariance-kernel-parzencauchy" title="Link to this heading">¶</a></h1>
 <dl class="py class objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.covariance.kernel.ParzenCauchy">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.covariance.kernel.</span></span><span class="sig-name descname"><span class="pre">ParzenCauchy</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.ParzenCauchy.__init__.x" title="arch.covariance.kernel.ParzenCauchy.__init__.x (Python parameter) — The data to use in covariance estimation."><span class="n"><span class="pre">x</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.ParzenCauchy.__init__.bandwidth" title="arch.covariance.kernel.ParzenCauchy.__init__.bandwidth (Python parameter) — The kernel's bandwidth.  If None, optimal bandwidth is estimated."><span class="n"><span class="pre">bandwidth</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.ParzenCauchy.__init__.df_adjust" title="arch.covariance.kernel.ParzenCauchy.__init__.df_adjust (Python parameter) — Degrees of freedom to remove when adjusting the covariance. Uses the number of observations in x minus df_adjust when dividing inner-products."><span class="n"><span class="pre">df_adjust</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">0</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.ParzenCauchy.__init__.center" title="arch.covariance.kernel.ParzenCauchy.__init__.center (Python parameter) — A flag indicating whether x should be demeaned before estimating the covariance."><span class="n"><span class="pre">center</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.ParzenCauchy.__init__.weights" title="arch.covariance.kernel.ParzenCauchy.__init__.weights (Python parameter) — An array of weights used to combine when estimating optimal bandwidth. If not provided, a vector of 1s is used. Must have nvar elements."><span class="n"><span class="pre">weights</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.ParzenCauchy.__init__.force_int" title="arch.covariance.kernel.ParzenCauchy.__init__.force_int (Python parameter) — Force bandwidth to be an integer."><span class="n"><span class="pre">force_int</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/covariance/kernel.html#ParzenCauchy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.covariance.kernel.ParzenCauchy" title="Link to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.covariance.kernel.</span></span><span class="sig-name descname"><span class="pre">ParzenCauchy</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.ParzenCauchy.__init__.x" title="arch.covariance.kernel.ParzenCauchy.__init__.x (Python parameter) — The data to use in covariance estimation."><span class="n"><span class="pre">x</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.ParzenCauchy.__init__.bandwidth" title="arch.covariance.kernel.ParzenCauchy.__init__.bandwidth (Python parameter) — The kernel's bandwidth.  If None, optimal bandwidth is estimated."><span class="n"><span class="pre">bandwidth</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.ParzenCauchy.__init__.df_adjust" title="arch.covariance.kernel.ParzenCauchy.__init__.df_adjust (Python parameter) — Degrees of freedom to remove when adjusting the covariance. Uses the number of observations in x minus df_adjust when dividing inner-products."><span class="n"><span class="pre">df_adjust</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">0</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.ParzenCauchy.__init__.center" title="arch.covariance.kernel.ParzenCauchy.__init__.center (Python parameter) — A flag indicating whether x should be demeaned before estimating the covariance."><span class="n"><span class="pre">center</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.ParzenCauchy.__init__.weights" title="arch.covariance.kernel.ParzenCauchy.__init__.weights (Python parameter) — An array of weights used to combine when estimating optimal bandwidth. If not provided, a vector of 1s is used. Must have nvar elements."><span class="n"><span class="pre">weights</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.ParzenCauchy.__init__.force_int" title="arch.covariance.kernel.ParzenCauchy.__init__.force_int (Python parameter) — Force bandwidth to be an integer."><span class="n"><span class="pre">force_int</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/covariance/kernel.html#ParzenCauchy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.covariance.kernel.ParzenCauchy" title="Link to this definition">¶</a></dt>
 <dd><p>Parzen’s Cauchy kernel covariance estimation.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.covariance.kernel.ParzenCauchy-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.ParzenCauchy-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.covariance.kernel.ParzenCauchy.__init__.x"><span class="n sig-name">x</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.covariance.kernel.ParzenCauchy.__init__.x" title="Permalink to this definition">¶</a></dt><dd><p>The data to use in covariance estimation.</p>
+<dt id="arch.covariance.kernel.ParzenCauchy.__init__.x"><span class="n sig-name">x</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a></span></span><a class="headerlink" href="#arch.covariance.kernel.ParzenCauchy.__init__.x" title="Permalink to this definition">¶</a></dt><dd><p>The data to use in covariance estimation.</p>
 </dd>
 <dt id="arch.covariance.kernel.ParzenCauchy.__init__.bandwidth"><span class="n sig-name">bandwidth</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.covariance.kernel.ParzenCauchy.__init__.bandwidth" title="Permalink to this definition">¶</a></dt><dd><p>The kernel’s bandwidth.  If None, optimal bandwidth is estimated.</p>
 </dd>
@@ -1112,7 +1112,7 @@ <h1 id="arch-covariance-kernel-parzencauchy">arch.covariance.kernel.ParzenCauchy
 <dt id="arch.covariance.kernel.ParzenCauchy.__init__.center"><span class="n sig-name">center</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)">bool</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code><a class="headerlink" href="#arch.covariance.kernel.ParzenCauchy.__init__.center" title="Permalink to this definition">¶</a></dt><dd><p>A flag indicating whether x should be demeaned before estimating the
 covariance.</p>
 </dd>
-<dt id="arch.covariance.kernel.ParzenCauchy.__init__.weights"><span class="n sig-name">weights</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.covariance.kernel.ParzenCauchy.__init__.weights" title="Permalink to this definition">¶</a></dt><dd><p>An array of weights used to combine when estimating optimal bandwidth.
+<dt id="arch.covariance.kernel.ParzenCauchy.__init__.weights"><span class="n sig-name">weights</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.covariance.kernel.ParzenCauchy.__init__.weights" title="Permalink to this definition">¶</a></dt><dd><p>An array of weights used to combine when estimating optimal bandwidth.
 If not provided, a vector of 1s is used. Must have nvar elements.</p>
 </dd>
 <dt id="arch.covariance.kernel.ParzenCauchy.__init__.force_int"><span class="n sig-name">force_int</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)">bool</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code><a class="headerlink" href="#arch.covariance.kernel.ParzenCauchy.__init__.force_int" title="Permalink to this definition">¶</a></dt><dd><p>Force bandwidth to be an integer.</p>
@@ -1281,7 +1281,7 @@ <h1 id="arch-covariance-kernel-parzencauchy">arch.covariance.kernel.ParzenCauchy
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diff --git a/devel/covariance/generated/arch.covariance.kernel.ParzenGeometric.html b/devel/covariance/generated/arch.covariance.kernel.ParzenGeometric.html
index 1b5c3b44ca..9891c34026 100644
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+++ b/devel/covariance/generated/arch.covariance.kernel.ParzenGeometric.html
@@ -15,7 +15,7 @@
     
   
       
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+    <a href="../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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@@ -1096,12 +1096,12 @@
 <h1 id="arch-covariance-kernel-parzengeometric">arch.covariance.kernel.ParzenGeometric<a class="headerlink" href="#arch-covariance-kernel-parzengeometric" title="Link to this heading">¶</a></h1>
 <dl class="py class objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.covariance.kernel.ParzenGeometric">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.covariance.kernel.</span></span><span class="sig-name descname"><span class="pre">ParzenGeometric</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.ParzenGeometric.__init__.x" title="arch.covariance.kernel.ParzenGeometric.__init__.x (Python parameter) — The data to use in covariance estimation."><span class="n"><span class="pre">x</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.ParzenGeometric.__init__.bandwidth" title="arch.covariance.kernel.ParzenGeometric.__init__.bandwidth (Python parameter) — The kernel's bandwidth.  If None, optimal bandwidth is estimated."><span class="n"><span class="pre">bandwidth</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.ParzenGeometric.__init__.df_adjust" title="arch.covariance.kernel.ParzenGeometric.__init__.df_adjust (Python parameter) — Degrees of freedom to remove when adjusting the covariance. Uses the number of observations in x minus df_adjust when dividing inner-products."><span class="n"><span class="pre">df_adjust</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">0</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.ParzenGeometric.__init__.center" title="arch.covariance.kernel.ParzenGeometric.__init__.center (Python parameter) — A flag indicating whether x should be demeaned before estimating the covariance."><span class="n"><span class="pre">center</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.ParzenGeometric.__init__.weights" title="arch.covariance.kernel.ParzenGeometric.__init__.weights (Python parameter) — An array of weights used to combine when estimating optimal bandwidth. If not provided, a vector of 1s is used. Must have nvar elements."><span class="n"><span class="pre">weights</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.ParzenGeometric.__init__.force_int" title="arch.covariance.kernel.ParzenGeometric.__init__.force_int (Python parameter) — Force bandwidth to be an integer."><span class="n"><span class="pre">force_int</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/covariance/kernel.html#ParzenGeometric"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.covariance.kernel.ParzenGeometric" title="Link to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.covariance.kernel.</span></span><span class="sig-name descname"><span class="pre">ParzenGeometric</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.ParzenGeometric.__init__.x" title="arch.covariance.kernel.ParzenGeometric.__init__.x (Python parameter) — The data to use in covariance estimation."><span class="n"><span class="pre">x</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.ParzenGeometric.__init__.bandwidth" title="arch.covariance.kernel.ParzenGeometric.__init__.bandwidth (Python parameter) — The kernel's bandwidth.  If None, optimal bandwidth is estimated."><span class="n"><span class="pre">bandwidth</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.ParzenGeometric.__init__.df_adjust" title="arch.covariance.kernel.ParzenGeometric.__init__.df_adjust (Python parameter) — Degrees of freedom to remove when adjusting the covariance. Uses the number of observations in x minus df_adjust when dividing inner-products."><span class="n"><span class="pre">df_adjust</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">0</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.ParzenGeometric.__init__.center" title="arch.covariance.kernel.ParzenGeometric.__init__.center (Python parameter) — A flag indicating whether x should be demeaned before estimating the covariance."><span class="n"><span class="pre">center</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.ParzenGeometric.__init__.weights" title="arch.covariance.kernel.ParzenGeometric.__init__.weights (Python parameter) — An array of weights used to combine when estimating optimal bandwidth. If not provided, a vector of 1s is used. Must have nvar elements."><span class="n"><span class="pre">weights</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.ParzenGeometric.__init__.force_int" title="arch.covariance.kernel.ParzenGeometric.__init__.force_int (Python parameter) — Force bandwidth to be an integer."><span class="n"><span class="pre">force_int</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/covariance/kernel.html#ParzenGeometric"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.covariance.kernel.ParzenGeometric" title="Link to this definition">¶</a></dt>
 <dd><p>Parzen’s Geometric kernel covariance estimation.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.covariance.kernel.ParzenGeometric-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.ParzenGeometric-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.covariance.kernel.ParzenGeometric.__init__.x"><span class="n sig-name">x</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.covariance.kernel.ParzenGeometric.__init__.x" title="Permalink to this definition">¶</a></dt><dd><p>The data to use in covariance estimation.</p>
+<dt id="arch.covariance.kernel.ParzenGeometric.__init__.x"><span class="n sig-name">x</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a></span></span><a class="headerlink" href="#arch.covariance.kernel.ParzenGeometric.__init__.x" title="Permalink to this definition">¶</a></dt><dd><p>The data to use in covariance estimation.</p>
 </dd>
 <dt id="arch.covariance.kernel.ParzenGeometric.__init__.bandwidth"><span class="n sig-name">bandwidth</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.covariance.kernel.ParzenGeometric.__init__.bandwidth" title="Permalink to this definition">¶</a></dt><dd><p>The kernel’s bandwidth.  If None, optimal bandwidth is estimated.</p>
 </dd>
@@ -1112,7 +1112,7 @@ <h1 id="arch-covariance-kernel-parzengeometric">arch.covariance.kernel.ParzenGeo
 <dt id="arch.covariance.kernel.ParzenGeometric.__init__.center"><span class="n sig-name">center</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)">bool</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code><a class="headerlink" href="#arch.covariance.kernel.ParzenGeometric.__init__.center" title="Permalink to this definition">¶</a></dt><dd><p>A flag indicating whether x should be demeaned before estimating the
 covariance.</p>
 </dd>
-<dt id="arch.covariance.kernel.ParzenGeometric.__init__.weights"><span class="n sig-name">weights</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.covariance.kernel.ParzenGeometric.__init__.weights" title="Permalink to this definition">¶</a></dt><dd><p>An array of weights used to combine when estimating optimal bandwidth.
+<dt id="arch.covariance.kernel.ParzenGeometric.__init__.weights"><span class="n sig-name">weights</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.covariance.kernel.ParzenGeometric.__init__.weights" title="Permalink to this definition">¶</a></dt><dd><p>An array of weights used to combine when estimating optimal bandwidth.
 If not provided, a vector of 1s is used. Must have nvar elements.</p>
 </dd>
 <dt id="arch.covariance.kernel.ParzenGeometric.__init__.force_int"><span class="n sig-name">force_int</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)">bool</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code><a class="headerlink" href="#arch.covariance.kernel.ParzenGeometric.__init__.force_int" title="Permalink to this definition">¶</a></dt><dd><p>Force bandwidth to be an integer.</p>
@@ -1281,7 +1281,7 @@ <h1 id="arch-covariance-kernel-parzengeometric">arch.covariance.kernel.ParzenGeo
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@@ -15,7 +15,7 @@
     
   
       
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@@ -1096,12 +1096,12 @@
 <h1 id="arch-covariance-kernel-parzenriesz">arch.covariance.kernel.ParzenRiesz<a class="headerlink" href="#arch-covariance-kernel-parzenriesz" title="Link to this heading">¶</a></h1>
 <dl class="py class objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.covariance.kernel.ParzenRiesz">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.covariance.kernel.</span></span><span class="sig-name descname"><span class="pre">ParzenRiesz</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.ParzenRiesz.__init__.x" title="arch.covariance.kernel.ParzenRiesz.__init__.x (Python parameter) — The data to use in covariance estimation."><span class="n"><span class="pre">x</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.ParzenRiesz.__init__.bandwidth" title="arch.covariance.kernel.ParzenRiesz.__init__.bandwidth (Python parameter) — The kernel's bandwidth.  If None, optimal bandwidth is estimated."><span class="n"><span class="pre">bandwidth</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.ParzenRiesz.__init__.df_adjust" title="arch.covariance.kernel.ParzenRiesz.__init__.df_adjust (Python parameter) — Degrees of freedom to remove when adjusting the covariance. Uses the number of observations in x minus df_adjust when dividing inner-products."><span class="n"><span class="pre">df_adjust</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">0</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.ParzenRiesz.__init__.center" title="arch.covariance.kernel.ParzenRiesz.__init__.center (Python parameter) — A flag indicating whether x should be demeaned before estimating the covariance."><span class="n"><span class="pre">center</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.ParzenRiesz.__init__.weights" title="arch.covariance.kernel.ParzenRiesz.__init__.weights (Python parameter) — An array of weights used to combine when estimating optimal bandwidth. If not provided, a vector of 1s is used. Must have nvar elements."><span class="n"><span class="pre">weights</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.ParzenRiesz.__init__.force_int" title="arch.covariance.kernel.ParzenRiesz.__init__.force_int (Python parameter) — Force bandwidth to be an integer."><span class="n"><span class="pre">force_int</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/covariance/kernel.html#ParzenRiesz"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.covariance.kernel.ParzenRiesz" title="Link to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.covariance.kernel.</span></span><span class="sig-name descname"><span class="pre">ParzenRiesz</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.ParzenRiesz.__init__.x" title="arch.covariance.kernel.ParzenRiesz.__init__.x (Python parameter) — The data to use in covariance estimation."><span class="n"><span class="pre">x</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.ParzenRiesz.__init__.bandwidth" title="arch.covariance.kernel.ParzenRiesz.__init__.bandwidth (Python parameter) — The kernel's bandwidth.  If None, optimal bandwidth is estimated."><span class="n"><span class="pre">bandwidth</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.ParzenRiesz.__init__.df_adjust" title="arch.covariance.kernel.ParzenRiesz.__init__.df_adjust (Python parameter) — Degrees of freedom to remove when adjusting the covariance. Uses the number of observations in x minus df_adjust when dividing inner-products."><span class="n"><span class="pre">df_adjust</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">0</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.ParzenRiesz.__init__.center" title="arch.covariance.kernel.ParzenRiesz.__init__.center (Python parameter) — A flag indicating whether x should be demeaned before estimating the covariance."><span class="n"><span class="pre">center</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.ParzenRiesz.__init__.weights" title="arch.covariance.kernel.ParzenRiesz.__init__.weights (Python parameter) — An array of weights used to combine when estimating optimal bandwidth. If not provided, a vector of 1s is used. Must have nvar elements."><span class="n"><span class="pre">weights</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.ParzenRiesz.__init__.force_int" title="arch.covariance.kernel.ParzenRiesz.__init__.force_int (Python parameter) — Force bandwidth to be an integer."><span class="n"><span class="pre">force_int</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/covariance/kernel.html#ParzenRiesz"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.covariance.kernel.ParzenRiesz" title="Link to this definition">¶</a></dt>
 <dd><p>Parzen-Reisz kernel covariance estimation.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.covariance.kernel.ParzenRiesz-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.ParzenRiesz-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.covariance.kernel.ParzenRiesz.__init__.x"><span class="n sig-name">x</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.covariance.kernel.ParzenRiesz.__init__.x" title="Permalink to this definition">¶</a></dt><dd><p>The data to use in covariance estimation.</p>
+<dt id="arch.covariance.kernel.ParzenRiesz.__init__.x"><span class="n sig-name">x</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a></span></span><a class="headerlink" href="#arch.covariance.kernel.ParzenRiesz.__init__.x" title="Permalink to this definition">¶</a></dt><dd><p>The data to use in covariance estimation.</p>
 </dd>
 <dt id="arch.covariance.kernel.ParzenRiesz.__init__.bandwidth"><span class="n sig-name">bandwidth</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.covariance.kernel.ParzenRiesz.__init__.bandwidth" title="Permalink to this definition">¶</a></dt><dd><p>The kernel’s bandwidth.  If None, optimal bandwidth is estimated.</p>
 </dd>
@@ -1112,7 +1112,7 @@ <h1 id="arch-covariance-kernel-parzenriesz">arch.covariance.kernel.ParzenRiesz<a
 <dt id="arch.covariance.kernel.ParzenRiesz.__init__.center"><span class="n sig-name">center</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)">bool</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code><a class="headerlink" href="#arch.covariance.kernel.ParzenRiesz.__init__.center" title="Permalink to this definition">¶</a></dt><dd><p>A flag indicating whether x should be demeaned before estimating the
 covariance.</p>
 </dd>
-<dt id="arch.covariance.kernel.ParzenRiesz.__init__.weights"><span class="n sig-name">weights</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.covariance.kernel.ParzenRiesz.__init__.weights" title="Permalink to this definition">¶</a></dt><dd><p>An array of weights used to combine when estimating optimal bandwidth.
+<dt id="arch.covariance.kernel.ParzenRiesz.__init__.weights"><span class="n sig-name">weights</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.covariance.kernel.ParzenRiesz.__init__.weights" title="Permalink to this definition">¶</a></dt><dd><p>An array of weights used to combine when estimating optimal bandwidth.
 If not provided, a vector of 1s is used. Must have nvar elements.</p>
 </dd>
 <dt id="arch.covariance.kernel.ParzenRiesz.__init__.force_int"><span class="n sig-name">force_int</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)">bool</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code><a class="headerlink" href="#arch.covariance.kernel.ParzenRiesz.__init__.force_int" title="Permalink to this definition">¶</a></dt><dd><p>Force bandwidth to be an integer.</p>
@@ -1281,7 +1281,7 @@ <h1 id="arch-covariance-kernel-parzenriesz">arch.covariance.kernel.ParzenRiesz<a
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diff --git a/devel/covariance/generated/arch.covariance.kernel.QuadraticSpectral.html b/devel/covariance/generated/arch.covariance.kernel.QuadraticSpectral.html
index 85bd6380b3..d7a7df35dc 100644
--- a/devel/covariance/generated/arch.covariance.kernel.QuadraticSpectral.html
+++ b/devel/covariance/generated/arch.covariance.kernel.QuadraticSpectral.html
@@ -15,7 +15,7 @@
     
   
       
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@@ -102,7 +102,7 @@
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-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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@@ -189,10 +189,10 @@
 
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@@ -1096,12 +1096,12 @@
 <h1 id="arch-covariance-kernel-quadraticspectral">arch.covariance.kernel.QuadraticSpectral<a class="headerlink" href="#arch-covariance-kernel-quadraticspectral" title="Link to this heading">¶</a></h1>
 <dl class="py class objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.covariance.kernel.QuadraticSpectral">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.covariance.kernel.</span></span><span class="sig-name descname"><span class="pre">QuadraticSpectral</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.QuadraticSpectral.__init__.x" title="arch.covariance.kernel.QuadraticSpectral.__init__.x (Python parameter) — The data to use in covariance estimation."><span class="n"><span class="pre">x</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.QuadraticSpectral.__init__.bandwidth" title="arch.covariance.kernel.QuadraticSpectral.__init__.bandwidth (Python parameter) — The kernel's bandwidth.  If None, optimal bandwidth is estimated."><span class="n"><span class="pre">bandwidth</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.QuadraticSpectral.__init__.df_adjust" title="arch.covariance.kernel.QuadraticSpectral.__init__.df_adjust (Python parameter) — Degrees of freedom to remove when adjusting the covariance. Uses the number of observations in x minus df_adjust when dividing inner-products."><span class="n"><span class="pre">df_adjust</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">0</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.QuadraticSpectral.__init__.center" title="arch.covariance.kernel.QuadraticSpectral.__init__.center (Python parameter) — A flag indicating whether x should be demeaned before estimating the covariance."><span class="n"><span class="pre">center</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.QuadraticSpectral.__init__.weights" title="arch.covariance.kernel.QuadraticSpectral.__init__.weights (Python parameter) — An array of weights used to combine when estimating optimal bandwidth. If not provided, a vector of 1s is used. Must have nvar elements."><span class="n"><span class="pre">weights</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.QuadraticSpectral.__init__.force_int" title="arch.covariance.kernel.QuadraticSpectral.__init__.force_int (Python parameter) — Force bandwidth to be an integer."><span class="n"><span class="pre">force_int</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/covariance/kernel.html#QuadraticSpectral"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.covariance.kernel.QuadraticSpectral" title="Link to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.covariance.kernel.</span></span><span class="sig-name descname"><span class="pre">QuadraticSpectral</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.QuadraticSpectral.__init__.x" title="arch.covariance.kernel.QuadraticSpectral.__init__.x (Python parameter) — The data to use in covariance estimation."><span class="n"><span class="pre">x</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.QuadraticSpectral.__init__.bandwidth" title="arch.covariance.kernel.QuadraticSpectral.__init__.bandwidth (Python parameter) — The kernel's bandwidth.  If None, optimal bandwidth is estimated."><span class="n"><span class="pre">bandwidth</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.QuadraticSpectral.__init__.df_adjust" title="arch.covariance.kernel.QuadraticSpectral.__init__.df_adjust (Python parameter) — Degrees of freedom to remove when adjusting the covariance. Uses the number of observations in x minus df_adjust when dividing inner-products."><span class="n"><span class="pre">df_adjust</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">0</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.QuadraticSpectral.__init__.center" title="arch.covariance.kernel.QuadraticSpectral.__init__.center (Python parameter) — A flag indicating whether x should be demeaned before estimating the covariance."><span class="n"><span class="pre">center</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.QuadraticSpectral.__init__.weights" title="arch.covariance.kernel.QuadraticSpectral.__init__.weights (Python parameter) — An array of weights used to combine when estimating optimal bandwidth. If not provided, a vector of 1s is used. Must have nvar elements."><span class="n"><span class="pre">weights</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.QuadraticSpectral.__init__.force_int" title="arch.covariance.kernel.QuadraticSpectral.__init__.force_int (Python parameter) — Force bandwidth to be an integer."><span class="n"><span class="pre">force_int</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/covariance/kernel.html#QuadraticSpectral"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.covariance.kernel.QuadraticSpectral" title="Link to this definition">¶</a></dt>
 <dd><p>Quadratic-Spectral (Andrews’) kernel covariance estimation.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.covariance.kernel.QuadraticSpectral-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.QuadraticSpectral-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.covariance.kernel.QuadraticSpectral.__init__.x"><span class="n sig-name">x</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.covariance.kernel.QuadraticSpectral.__init__.x" title="Permalink to this definition">¶</a></dt><dd><p>The data to use in covariance estimation.</p>
+<dt id="arch.covariance.kernel.QuadraticSpectral.__init__.x"><span class="n sig-name">x</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a></span></span><a class="headerlink" href="#arch.covariance.kernel.QuadraticSpectral.__init__.x" title="Permalink to this definition">¶</a></dt><dd><p>The data to use in covariance estimation.</p>
 </dd>
 <dt id="arch.covariance.kernel.QuadraticSpectral.__init__.bandwidth"><span class="n sig-name">bandwidth</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.covariance.kernel.QuadraticSpectral.__init__.bandwidth" title="Permalink to this definition">¶</a></dt><dd><p>The kernel’s bandwidth.  If None, optimal bandwidth is estimated.</p>
 </dd>
@@ -1112,7 +1112,7 @@ <h1 id="arch-covariance-kernel-quadraticspectral">arch.covariance.kernel.Quadrat
 <dt id="arch.covariance.kernel.QuadraticSpectral.__init__.center"><span class="n sig-name">center</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)">bool</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code><a class="headerlink" href="#arch.covariance.kernel.QuadraticSpectral.__init__.center" title="Permalink to this definition">¶</a></dt><dd><p>A flag indicating whether x should be demeaned before estimating the
 covariance.</p>
 </dd>
-<dt id="arch.covariance.kernel.QuadraticSpectral.__init__.weights"><span class="n sig-name">weights</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.covariance.kernel.QuadraticSpectral.__init__.weights" title="Permalink to this definition">¶</a></dt><dd><p>An array of weights used to combine when estimating optimal bandwidth.
+<dt id="arch.covariance.kernel.QuadraticSpectral.__init__.weights"><span class="n sig-name">weights</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.covariance.kernel.QuadraticSpectral.__init__.weights" title="Permalink to this definition">¶</a></dt><dd><p>An array of weights used to combine when estimating optimal bandwidth.
 If not provided, a vector of 1s is used. Must have nvar elements.</p>
 </dd>
 <dt id="arch.covariance.kernel.QuadraticSpectral.__init__.force_int"><span class="n sig-name">force_int</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)">bool</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code><a class="headerlink" href="#arch.covariance.kernel.QuadraticSpectral.__init__.force_int" title="Permalink to this definition">¶</a></dt><dd><p>Force bandwidth to be an integer.</p>
@@ -1281,7 +1281,7 @@ <h1 id="arch-covariance-kernel-quadraticspectral">arch.covariance.kernel.Quadrat
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index 2605838bb5..51e2411260 100644
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+++ b/devel/covariance/generated/arch.covariance.kernel.TukeyHamming.html
@@ -15,7 +15,7 @@
     
   
       
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@@ -102,7 +102,7 @@
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-            arch v6.2.1 (+21)
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@@ -1096,12 +1096,12 @@
 <h1 id="arch-covariance-kernel-tukeyhamming">arch.covariance.kernel.TukeyHamming<a class="headerlink" href="#arch-covariance-kernel-tukeyhamming" title="Link to this heading">¶</a></h1>
 <dl class="py class objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.covariance.kernel.TukeyHamming">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.covariance.kernel.</span></span><span class="sig-name descname"><span class="pre">TukeyHamming</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.TukeyHamming.__init__.x" title="arch.covariance.kernel.TukeyHamming.__init__.x (Python parameter) — The data to use in covariance estimation."><span class="n"><span class="pre">x</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.TukeyHamming.__init__.bandwidth" title="arch.covariance.kernel.TukeyHamming.__init__.bandwidth (Python parameter) — The kernel's bandwidth.  If None, optimal bandwidth is estimated."><span class="n"><span class="pre">bandwidth</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.TukeyHamming.__init__.df_adjust" title="arch.covariance.kernel.TukeyHamming.__init__.df_adjust (Python parameter) — Degrees of freedom to remove when adjusting the covariance. Uses the number of observations in x minus df_adjust when dividing inner-products."><span class="n"><span class="pre">df_adjust</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">0</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.TukeyHamming.__init__.center" title="arch.covariance.kernel.TukeyHamming.__init__.center (Python parameter) — A flag indicating whether x should be demeaned before estimating the covariance."><span class="n"><span class="pre">center</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.TukeyHamming.__init__.weights" title="arch.covariance.kernel.TukeyHamming.__init__.weights (Python parameter) — An array of weights used to combine when estimating optimal bandwidth. If not provided, a vector of 1s is used. Must have nvar elements."><span class="n"><span class="pre">weights</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.TukeyHamming.__init__.force_int" title="arch.covariance.kernel.TukeyHamming.__init__.force_int (Python parameter) — Force bandwidth to be an integer."><span class="n"><span class="pre">force_int</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/covariance/kernel.html#TukeyHamming"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.covariance.kernel.TukeyHamming" title="Link to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.covariance.kernel.</span></span><span class="sig-name descname"><span class="pre">TukeyHamming</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.TukeyHamming.__init__.x" title="arch.covariance.kernel.TukeyHamming.__init__.x (Python parameter) — The data to use in covariance estimation."><span class="n"><span class="pre">x</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.TukeyHamming.__init__.bandwidth" title="arch.covariance.kernel.TukeyHamming.__init__.bandwidth (Python parameter) — The kernel's bandwidth.  If None, optimal bandwidth is estimated."><span class="n"><span class="pre">bandwidth</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.TukeyHamming.__init__.df_adjust" title="arch.covariance.kernel.TukeyHamming.__init__.df_adjust (Python parameter) — Degrees of freedom to remove when adjusting the covariance. Uses the number of observations in x minus df_adjust when dividing inner-products."><span class="n"><span class="pre">df_adjust</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">0</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.TukeyHamming.__init__.center" title="arch.covariance.kernel.TukeyHamming.__init__.center (Python parameter) — A flag indicating whether x should be demeaned before estimating the covariance."><span class="n"><span class="pre">center</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.TukeyHamming.__init__.weights" title="arch.covariance.kernel.TukeyHamming.__init__.weights (Python parameter) — An array of weights used to combine when estimating optimal bandwidth. If not provided, a vector of 1s is used. Must have nvar elements."><span class="n"><span class="pre">weights</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.TukeyHamming.__init__.force_int" title="arch.covariance.kernel.TukeyHamming.__init__.force_int (Python parameter) — Force bandwidth to be an integer."><span class="n"><span class="pre">force_int</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/covariance/kernel.html#TukeyHamming"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.covariance.kernel.TukeyHamming" title="Link to this definition">¶</a></dt>
 <dd><p>Tukey-Hamming kernel covariance estimation.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.covariance.kernel.TukeyHamming-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.TukeyHamming-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.covariance.kernel.TukeyHamming.__init__.x"><span class="n sig-name">x</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.covariance.kernel.TukeyHamming.__init__.x" title="Permalink to this definition">¶</a></dt><dd><p>The data to use in covariance estimation.</p>
+<dt id="arch.covariance.kernel.TukeyHamming.__init__.x"><span class="n sig-name">x</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a></span></span><a class="headerlink" href="#arch.covariance.kernel.TukeyHamming.__init__.x" title="Permalink to this definition">¶</a></dt><dd><p>The data to use in covariance estimation.</p>
 </dd>
 <dt id="arch.covariance.kernel.TukeyHamming.__init__.bandwidth"><span class="n sig-name">bandwidth</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.covariance.kernel.TukeyHamming.__init__.bandwidth" title="Permalink to this definition">¶</a></dt><dd><p>The kernel’s bandwidth.  If None, optimal bandwidth is estimated.</p>
 </dd>
@@ -1112,7 +1112,7 @@ <h1 id="arch-covariance-kernel-tukeyhamming">arch.covariance.kernel.TukeyHamming
 <dt id="arch.covariance.kernel.TukeyHamming.__init__.center"><span class="n sig-name">center</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)">bool</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code><a class="headerlink" href="#arch.covariance.kernel.TukeyHamming.__init__.center" title="Permalink to this definition">¶</a></dt><dd><p>A flag indicating whether x should be demeaned before estimating the
 covariance.</p>
 </dd>
-<dt id="arch.covariance.kernel.TukeyHamming.__init__.weights"><span class="n sig-name">weights</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.covariance.kernel.TukeyHamming.__init__.weights" title="Permalink to this definition">¶</a></dt><dd><p>An array of weights used to combine when estimating optimal bandwidth.
+<dt id="arch.covariance.kernel.TukeyHamming.__init__.weights"><span class="n sig-name">weights</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.covariance.kernel.TukeyHamming.__init__.weights" title="Permalink to this definition">¶</a></dt><dd><p>An array of weights used to combine when estimating optimal bandwidth.
 If not provided, a vector of 1s is used. Must have nvar elements.</p>
 </dd>
 <dt id="arch.covariance.kernel.TukeyHamming.__init__.force_int"><span class="n sig-name">force_int</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)">bool</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code><a class="headerlink" href="#arch.covariance.kernel.TukeyHamming.__init__.force_int" title="Permalink to this definition">¶</a></dt><dd><p>Force bandwidth to be an integer.</p>
@@ -1281,7 +1281,7 @@ <h1 id="arch-covariance-kernel-tukeyhamming">arch.covariance.kernel.TukeyHamming
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index b9e5a8ec9e..f9720b2cdf 100644
--- a/devel/covariance/generated/arch.covariance.kernel.TukeyHanning.html
+++ b/devel/covariance/generated/arch.covariance.kernel.TukeyHanning.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.covariance.kernel.TukeyHanning - arch v6.2.1 (+21)</title>
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+    <a href="../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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@@ -189,10 +189,10 @@
 
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+    <a href="../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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@@ -1096,12 +1096,12 @@
 <h1 id="arch-covariance-kernel-tukeyhanning">arch.covariance.kernel.TukeyHanning<a class="headerlink" href="#arch-covariance-kernel-tukeyhanning" title="Link to this heading">¶</a></h1>
 <dl class="py class objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.covariance.kernel.TukeyHanning">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.covariance.kernel.</span></span><span class="sig-name descname"><span class="pre">TukeyHanning</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.TukeyHanning.__init__.x" title="arch.covariance.kernel.TukeyHanning.__init__.x (Python parameter) — The data to use in covariance estimation."><span class="n"><span class="pre">x</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.TukeyHanning.__init__.bandwidth" title="arch.covariance.kernel.TukeyHanning.__init__.bandwidth (Python parameter) — The kernel's bandwidth.  If None, optimal bandwidth is estimated."><span class="n"><span class="pre">bandwidth</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.TukeyHanning.__init__.df_adjust" title="arch.covariance.kernel.TukeyHanning.__init__.df_adjust (Python parameter) — Degrees of freedom to remove when adjusting the covariance. Uses the number of observations in x minus df_adjust when dividing inner-products."><span class="n"><span class="pre">df_adjust</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">0</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.TukeyHanning.__init__.center" title="arch.covariance.kernel.TukeyHanning.__init__.center (Python parameter) — A flag indicating whether x should be demeaned before estimating the covariance."><span class="n"><span class="pre">center</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.TukeyHanning.__init__.weights" title="arch.covariance.kernel.TukeyHanning.__init__.weights (Python parameter) — An array of weights used to combine when estimating optimal bandwidth. If not provided, a vector of 1s is used. Must have nvar elements."><span class="n"><span class="pre">weights</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.TukeyHanning.__init__.force_int" title="arch.covariance.kernel.TukeyHanning.__init__.force_int (Python parameter) — Force bandwidth to be an integer."><span class="n"><span class="pre">force_int</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/covariance/kernel.html#TukeyHanning"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.covariance.kernel.TukeyHanning" title="Link to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.covariance.kernel.</span></span><span class="sig-name descname"><span class="pre">TukeyHanning</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.TukeyHanning.__init__.x" title="arch.covariance.kernel.TukeyHanning.__init__.x (Python parameter) — The data to use in covariance estimation."><span class="n"><span class="pre">x</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.TukeyHanning.__init__.bandwidth" title="arch.covariance.kernel.TukeyHanning.__init__.bandwidth (Python parameter) — The kernel's bandwidth.  If None, optimal bandwidth is estimated."><span class="n"><span class="pre">bandwidth</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.TukeyHanning.__init__.df_adjust" title="arch.covariance.kernel.TukeyHanning.__init__.df_adjust (Python parameter) — Degrees of freedom to remove when adjusting the covariance. Uses the number of observations in x minus df_adjust when dividing inner-products."><span class="n"><span class="pre">df_adjust</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">0</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.TukeyHanning.__init__.center" title="arch.covariance.kernel.TukeyHanning.__init__.center (Python parameter) — A flag indicating whether x should be demeaned before estimating the covariance."><span class="n"><span class="pre">center</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.TukeyHanning.__init__.weights" title="arch.covariance.kernel.TukeyHanning.__init__.weights (Python parameter) — An array of weights used to combine when estimating optimal bandwidth. If not provided, a vector of 1s is used. Must have nvar elements."><span class="n"><span class="pre">weights</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.TukeyHanning.__init__.force_int" title="arch.covariance.kernel.TukeyHanning.__init__.force_int (Python parameter) — Force bandwidth to be an integer."><span class="n"><span class="pre">force_int</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/covariance/kernel.html#TukeyHanning"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.covariance.kernel.TukeyHanning" title="Link to this definition">¶</a></dt>
 <dd><p>Tukey-Hanning kernel covariance estimation.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.covariance.kernel.TukeyHanning-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.TukeyHanning-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.covariance.kernel.TukeyHanning.__init__.x"><span class="n sig-name">x</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.covariance.kernel.TukeyHanning.__init__.x" title="Permalink to this definition">¶</a></dt><dd><p>The data to use in covariance estimation.</p>
+<dt id="arch.covariance.kernel.TukeyHanning.__init__.x"><span class="n sig-name">x</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a></span></span><a class="headerlink" href="#arch.covariance.kernel.TukeyHanning.__init__.x" title="Permalink to this definition">¶</a></dt><dd><p>The data to use in covariance estimation.</p>
 </dd>
 <dt id="arch.covariance.kernel.TukeyHanning.__init__.bandwidth"><span class="n sig-name">bandwidth</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.covariance.kernel.TukeyHanning.__init__.bandwidth" title="Permalink to this definition">¶</a></dt><dd><p>The kernel’s bandwidth.  If None, optimal bandwidth is estimated.</p>
 </dd>
@@ -1112,7 +1112,7 @@ <h1 id="arch-covariance-kernel-tukeyhanning">arch.covariance.kernel.TukeyHanning
 <dt id="arch.covariance.kernel.TukeyHanning.__init__.center"><span class="n sig-name">center</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)">bool</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code><a class="headerlink" href="#arch.covariance.kernel.TukeyHanning.__init__.center" title="Permalink to this definition">¶</a></dt><dd><p>A flag indicating whether x should be demeaned before estimating the
 covariance.</p>
 </dd>
-<dt id="arch.covariance.kernel.TukeyHanning.__init__.weights"><span class="n sig-name">weights</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.covariance.kernel.TukeyHanning.__init__.weights" title="Permalink to this definition">¶</a></dt><dd><p>An array of weights used to combine when estimating optimal bandwidth.
+<dt id="arch.covariance.kernel.TukeyHanning.__init__.weights"><span class="n sig-name">weights</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.covariance.kernel.TukeyHanning.__init__.weights" title="Permalink to this definition">¶</a></dt><dd><p>An array of weights used to combine when estimating optimal bandwidth.
 If not provided, a vector of 1s is used. Must have nvar elements.</p>
 </dd>
 <dt id="arch.covariance.kernel.TukeyHanning.__init__.force_int"><span class="n sig-name">force_int</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)">bool</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code><a class="headerlink" href="#arch.covariance.kernel.TukeyHanning.__init__.force_int" title="Permalink to this definition">¶</a></dt><dd><p>Force bandwidth to be an integer.</p>
@@ -1281,7 +1281,7 @@ <h1 id="arch-covariance-kernel-tukeyhanning">arch.covariance.kernel.TukeyHanning
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index 2f221deb6f..42be6d3960 100644
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+++ b/devel/covariance/generated/arch.covariance.kernel.TukeyParzen.html
@@ -15,7 +15,7 @@
     
   
       
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-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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@@ -1096,12 +1096,12 @@
 <h1 id="arch-covariance-kernel-tukeyparzen">arch.covariance.kernel.TukeyParzen<a class="headerlink" href="#arch-covariance-kernel-tukeyparzen" title="Link to this heading">¶</a></h1>
 <dl class="py class objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.covariance.kernel.TukeyParzen">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.covariance.kernel.</span></span><span class="sig-name descname"><span class="pre">TukeyParzen</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.TukeyParzen.__init__.x" title="arch.covariance.kernel.TukeyParzen.__init__.x (Python parameter) — The data to use in covariance estimation."><span class="n"><span class="pre">x</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.TukeyParzen.__init__.bandwidth" title="arch.covariance.kernel.TukeyParzen.__init__.bandwidth (Python parameter) — The kernel's bandwidth.  If None, optimal bandwidth is estimated."><span class="n"><span class="pre">bandwidth</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.TukeyParzen.__init__.df_adjust" title="arch.covariance.kernel.TukeyParzen.__init__.df_adjust (Python parameter) — Degrees of freedom to remove when adjusting the covariance. Uses the number of observations in x minus df_adjust when dividing inner-products."><span class="n"><span class="pre">df_adjust</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">0</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.TukeyParzen.__init__.center" title="arch.covariance.kernel.TukeyParzen.__init__.center (Python parameter) — A flag indicating whether x should be demeaned before estimating the covariance."><span class="n"><span class="pre">center</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.TukeyParzen.__init__.weights" title="arch.covariance.kernel.TukeyParzen.__init__.weights (Python parameter) — An array of weights used to combine when estimating optimal bandwidth. If not provided, a vector of 1s is used. Must have nvar elements."><span class="n"><span class="pre">weights</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.TukeyParzen.__init__.force_int" title="arch.covariance.kernel.TukeyParzen.__init__.force_int (Python parameter) — Force bandwidth to be an integer."><span class="n"><span class="pre">force_int</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/covariance/kernel.html#TukeyParzen"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.covariance.kernel.TukeyParzen" title="Link to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.covariance.kernel.</span></span><span class="sig-name descname"><span class="pre">TukeyParzen</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.TukeyParzen.__init__.x" title="arch.covariance.kernel.TukeyParzen.__init__.x (Python parameter) — The data to use in covariance estimation."><span class="n"><span class="pre">x</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.TukeyParzen.__init__.bandwidth" title="arch.covariance.kernel.TukeyParzen.__init__.bandwidth (Python parameter) — The kernel's bandwidth.  If None, optimal bandwidth is estimated."><span class="n"><span class="pre">bandwidth</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.TukeyParzen.__init__.df_adjust" title="arch.covariance.kernel.TukeyParzen.__init__.df_adjust (Python parameter) — Degrees of freedom to remove when adjusting the covariance. Uses the number of observations in x minus df_adjust when dividing inner-products."><span class="n"><span class="pre">df_adjust</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">0</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.TukeyParzen.__init__.center" title="arch.covariance.kernel.TukeyParzen.__init__.center (Python parameter) — A flag indicating whether x should be demeaned before estimating the covariance."><span class="n"><span class="pre">center</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.TukeyParzen.__init__.weights" title="arch.covariance.kernel.TukeyParzen.__init__.weights (Python parameter) — An array of weights used to combine when estimating optimal bandwidth. If not provided, a vector of 1s is used. Must have nvar elements."><span class="n"><span class="pre">weights</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.covariance.kernel.TukeyParzen.__init__.force_int" title="arch.covariance.kernel.TukeyParzen.__init__.force_int (Python parameter) — Force bandwidth to be an integer."><span class="n"><span class="pre">force_int</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/covariance/kernel.html#TukeyParzen"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.covariance.kernel.TukeyParzen" title="Link to this definition">¶</a></dt>
 <dd><p>Tukey-Parzen kernel covariance estimation.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.covariance.kernel.TukeyParzen-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.covariance.kernel.TukeyParzen-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.covariance.kernel.TukeyParzen.__init__.x"><span class="n sig-name">x</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.covariance.kernel.TukeyParzen.__init__.x" title="Permalink to this definition">¶</a></dt><dd><p>The data to use in covariance estimation.</p>
+<dt id="arch.covariance.kernel.TukeyParzen.__init__.x"><span class="n sig-name">x</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a></span></span><a class="headerlink" href="#arch.covariance.kernel.TukeyParzen.__init__.x" title="Permalink to this definition">¶</a></dt><dd><p>The data to use in covariance estimation.</p>
 </dd>
 <dt id="arch.covariance.kernel.TukeyParzen.__init__.bandwidth"><span class="n sig-name">bandwidth</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.covariance.kernel.TukeyParzen.__init__.bandwidth" title="Permalink to this definition">¶</a></dt><dd><p>The kernel’s bandwidth.  If None, optimal bandwidth is estimated.</p>
 </dd>
@@ -1112,7 +1112,7 @@ <h1 id="arch-covariance-kernel-tukeyparzen">arch.covariance.kernel.TukeyParzen<a
 <dt id="arch.covariance.kernel.TukeyParzen.__init__.center"><span class="n sig-name">center</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)">bool</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code><a class="headerlink" href="#arch.covariance.kernel.TukeyParzen.__init__.center" title="Permalink to this definition">¶</a></dt><dd><p>A flag indicating whether x should be demeaned before estimating the
 covariance.</p>
 </dd>
-<dt id="arch.covariance.kernel.TukeyParzen.__init__.weights"><span class="n sig-name">weights</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.covariance.kernel.TukeyParzen.__init__.weights" title="Permalink to this definition">¶</a></dt><dd><p>An array of weights used to combine when estimating optimal bandwidth.
+<dt id="arch.covariance.kernel.TukeyParzen.__init__.weights"><span class="n sig-name">weights</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.covariance.kernel.TukeyParzen.__init__.weights" title="Permalink to this definition">¶</a></dt><dd><p>An array of weights used to combine when estimating optimal bandwidth.
 If not provided, a vector of 1s is used. Must have nvar elements.</p>
 </dd>
 <dt id="arch.covariance.kernel.TukeyParzen.__init__.force_int"><span class="n sig-name">force_int</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)">bool</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code><a class="headerlink" href="#arch.covariance.kernel.TukeyParzen.__init__.force_int" title="Permalink to this definition">¶</a></dt><dd><p>Force bandwidth to be an integer.</p>
@@ -1281,7 +1281,7 @@ <h1 id="arch-covariance-kernel-tukeyparzen">arch.covariance.kernel.TukeyParzen<a
     <script id="__config" type="application/json">{"base": "../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
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diff --git a/devel/covariance/generated/generated/arch.covariance.kernel.Andrews.bandwidth.html b/devel/covariance/generated/generated/arch.covariance.kernel.Andrews.bandwidth.html
index 46184a2612..d680cee8b4 100644
--- a/devel/covariance/generated/generated/arch.covariance.kernel.Andrews.bandwidth.html
+++ b/devel/covariance/generated/generated/arch.covariance.kernel.Andrews.bandwidth.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.covariance.kernel.Andrews.bandwidth - arch v6.2.1 (+21)</title>
+        <title>arch.covariance.kernel.Andrews.bandwidth - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
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@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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@@ -1067,7 +1067,7 @@ <h1 id="arch-covariance-kernel-andrews-bandwidth">arch.covariance.kernel.Andrews
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
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diff --git a/devel/covariance/generated/generated/arch.covariance.kernel.Andrews.bandwidth_scale.html b/devel/covariance/generated/generated/arch.covariance.kernel.Andrews.bandwidth_scale.html
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+++ b/devel/covariance/generated/generated/arch.covariance.kernel.Andrews.bandwidth_scale.html
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diff --git a/devel/covariance/generated/generated/arch.covariance.kernel.CovarianceEstimate.long_run.html b/devel/covariance/generated/generated/arch.covariance.kernel.CovarianceEstimate.long_run.html
index 0d2fd9736f..3942de74c8 100644
--- a/devel/covariance/generated/generated/arch.covariance.kernel.CovarianceEstimate.long_run.html
+++ b/devel/covariance/generated/generated/arch.covariance.kernel.CovarianceEstimate.long_run.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.covariance.kernel.CovarianceEstimate.long_run - arch v6.2.1 (+21)</title>
+        <title>arch.covariance.kernel.CovarianceEstimate.long_run - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
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@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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@@ -102,7 +102,7 @@
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           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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@@ -189,10 +189,10 @@
 
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+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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@@ -646,7 +646,7 @@
 <h1 id="arch-covariance-kernel-covarianceestimate-long-run">arch.covariance.kernel.CovarianceEstimate.long_run<a class="headerlink" href="#arch-covariance-kernel-covarianceestimate-long-run" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.covariance.kernel.CovarianceEstimate.long_run">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">CovarianceEstimate.</span></span><span class="sig-name descname"><span class="pre">long_run</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><span class="pre">Float64Array</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">DataFrame</span></span><a class="headerlink" href="#arch.covariance.kernel.CovarianceEstimate.long_run" title="Link to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">CovarianceEstimate.</span></span><span class="sig-name descname"><span class="pre">long_run</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><span class="pre">DataFrame</span></a></span><a class="headerlink" href="#arch.covariance.kernel.CovarianceEstimate.long_run" title="Link to this definition">¶</a></dt>
 <dd><p>The long-run covariance estimate.</p>
 </dd></dl>
 
@@ -764,7 +764,7 @@ <h1 id="arch-covariance-kernel-covarianceestimate-long-run">arch.covariance.kern
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
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diff --git a/devel/covariance/generated/generated/arch.covariance.kernel.CovarianceEstimate.one_sided.html b/devel/covariance/generated/generated/arch.covariance.kernel.CovarianceEstimate.one_sided.html
index 2a2e54dc34..ee21950d73 100644
--- a/devel/covariance/generated/generated/arch.covariance.kernel.CovarianceEstimate.one_sided.html
+++ b/devel/covariance/generated/generated/arch.covariance.kernel.CovarianceEstimate.one_sided.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.covariance.kernel.CovarianceEstimate.one_sided - arch v6.2.1 (+21)</title>
+        <title>arch.covariance.kernel.CovarianceEstimate.one_sided - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
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@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
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   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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     <div class="md-nav__source">
@@ -646,7 +646,7 @@
 <h1 id="arch-covariance-kernel-covarianceestimate-one-sided">arch.covariance.kernel.CovarianceEstimate.one_sided<a class="headerlink" href="#arch-covariance-kernel-covarianceestimate-one-sided" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.covariance.kernel.CovarianceEstimate.one_sided">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">CovarianceEstimate.</span></span><span class="sig-name descname"><span class="pre">one_sided</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><span class="pre">Float64Array</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="pre">DataFrame</span></span><a class="headerlink" href="#arch.covariance.kernel.CovarianceEstimate.one_sided" title="Link to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">CovarianceEstimate.</span></span><span class="sig-name descname"><span class="pre">one_sided</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><span class="pre">DataFrame</span></a></span><a class="headerlink" href="#arch.covariance.kernel.CovarianceEstimate.one_sided" title="Link to this definition">¶</a></dt>
 <dd><p>The one-sided covariance estimate.</p>
 </dd></dl>
 
@@ -764,7 +764,7 @@ <h1 id="arch-covariance-kernel-covarianceestimate-one-sided">arch.covariance.ker
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
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   </body>
diff --git a/devel/covariance/generated/generated/arch.covariance.kernel.CovarianceEstimate.one_sided_strict.html b/devel/covariance/generated/generated/arch.covariance.kernel.CovarianceEstimate.one_sided_strict.html
index 4276c7cf1d..2eafd7430c 100644
--- a/devel/covariance/generated/generated/arch.covariance.kernel.CovarianceEstimate.one_sided_strict.html
+++ b/devel/covariance/generated/generated/arch.covariance.kernel.CovarianceEstimate.one_sided_strict.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.covariance.kernel.CovarianceEstimate.one_sided_strict - arch v6.2.1 (+21)</title>
+        <title>arch.covariance.kernel.CovarianceEstimate.one_sided_strict - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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     <div class="md-nav__source">
@@ -646,7 +646,7 @@
 <h1 id="arch-covariance-kernel-covarianceestimate-one-sided-strict">arch.covariance.kernel.CovarianceEstimate.one_sided_strict<a class="headerlink" href="#arch-covariance-kernel-covarianceestimate-one-sided-strict" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.covariance.kernel.CovarianceEstimate.one_sided_strict">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">CovarianceEstimate.</span></span><span class="sig-name descname"><span class="pre">one_sided_strict</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a></span><a class="headerlink" href="#arch.covariance.kernel.CovarianceEstimate.one_sided_strict" title="Link to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">CovarianceEstimate.</span></span><span class="sig-name descname"><span class="pre">one_sided_strict</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><span class="pre">DataFrame</span></a></span><a class="headerlink" href="#arch.covariance.kernel.CovarianceEstimate.one_sided_strict" title="Link to this definition">¶</a></dt>
 <dd><p>The one-sided strict covariance estimate.</p>
 </dd></dl>
 
@@ -764,7 +764,7 @@ <h1 id="arch-covariance-kernel-covarianceestimate-one-sided-strict">arch.covaria
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
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   </body>
diff --git a/devel/covariance/generated/generated/arch.covariance.kernel.CovarianceEstimate.short_run.html b/devel/covariance/generated/generated/arch.covariance.kernel.CovarianceEstimate.short_run.html
index b91f598c34..cc24c4a44b 100644
--- a/devel/covariance/generated/generated/arch.covariance.kernel.CovarianceEstimate.short_run.html
+++ b/devel/covariance/generated/generated/arch.covariance.kernel.CovarianceEstimate.short_run.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.covariance.kernel.CovarianceEstimate.short_run - arch v6.2.1 (+21)</title>
+        <title>arch.covariance.kernel.CovarianceEstimate.short_run - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
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@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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@@ -102,7 +102,7 @@
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@@ -189,10 +189,10 @@
 
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+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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@@ -646,7 +646,7 @@
 <h1 id="arch-covariance-kernel-covarianceestimate-short-run">arch.covariance.kernel.CovarianceEstimate.short_run<a class="headerlink" href="#arch-covariance-kernel-covarianceestimate-short-run" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.covariance.kernel.CovarianceEstimate.short_run">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">CovarianceEstimate.</span></span><span class="sig-name descname"><span class="pre">short_run</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a></span><a class="headerlink" href="#arch.covariance.kernel.CovarianceEstimate.short_run" title="Link to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">CovarianceEstimate.</span></span><span class="sig-name descname"><span class="pre">short_run</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><span class="pre">DataFrame</span></a></span><a class="headerlink" href="#arch.covariance.kernel.CovarianceEstimate.short_run" title="Link to this definition">¶</a></dt>
 <dd><p>The short-run covariance estimate.</p>
 </dd></dl>
 
@@ -764,7 +764,7 @@ <h1 id="arch-covariance-kernel-covarianceestimate-short-run">arch.covariance.ker
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diff --git a/devel/covariance/generated/generated/arch.covariance.kernel.Gallant.bandwidth.html b/devel/covariance/generated/generated/arch.covariance.kernel.Gallant.bandwidth.html
index 1a1c9a8be1..3e6923f094 100644
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+++ b/devel/covariance/generated/generated/arch.covariance.kernel.Gallant.bandwidth.html
@@ -15,7 +15,7 @@
     
   
       
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@@ -47,7 +47,7 @@
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@@ -102,7 +102,7 @@
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-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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diff --git a/devel/covariance/generated/generated/arch.covariance.kernel.Gallant.bandwidth_scale.html b/devel/covariance/generated/generated/arch.covariance.kernel.Gallant.bandwidth_scale.html
index 4b7ec2bcc4..f34afa5a1a 100644
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@@ -15,7 +15,7 @@
     
   
       
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+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
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-    arch v6.2.1 (+21)
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diff --git a/devel/covariance/generated/generated/arch.covariance.kernel.Gallant.centered.html b/devel/covariance/generated/generated/arch.covariance.kernel.Gallant.centered.html
index 2a8b9e56f1..7644873a7d 100644
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@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.covariance.kernel.Gallant.centered - arch v6.2.1 (+21)</title>
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@@ -47,7 +47,7 @@
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@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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@@ -189,10 +189,10 @@
 
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-    arch v6.2.1 (+21)
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diff --git a/devel/covariance/generated/generated/arch.covariance.kernel.Gallant.cov.html b/devel/covariance/generated/generated/arch.covariance.kernel.Gallant.cov.html
index ba66e27fde..7f12314697 100644
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+++ b/devel/covariance/generated/generated/arch.covariance.kernel.Gallant.cov.html
@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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@@ -189,10 +189,10 @@
 
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-            arch v6.2.1 (+21)
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index ac5dbc1867..0477eec119 100644
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diff --git a/devel/index.html b/devel/index.html
index 81f8f87c03..85c3829ebe 100644
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+    arch v6.2.1 (+23)
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-    <a href="../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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diff --git a/devel/multiple-comparison/generated/arch.bootstrap.MCS.html b/devel/multiple-comparison/generated/arch.bootstrap.MCS.html
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@@ -92,7 +92,7 @@
 
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@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
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           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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@@ -189,10 +189,10 @@
 
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@@ -942,12 +942,12 @@
 <h1 id="arch-bootstrap-mcs">arch.bootstrap.MCS<a class="headerlink" href="#arch-bootstrap-mcs" title="Link to this heading">¶</a></h1>
 <dl class="py class objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.bootstrap.MCS">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.bootstrap.</span></span><span class="sig-name descname"><span class="pre">MCS</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.MCS.__init__.losses" title="arch.bootstrap.MCS.__init__.losses (Python parameter) — T by k array containing losses from a set of models"><span class="n"><span class="pre">losses</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.MCS.__init__.size" title="arch.bootstrap.MCS.__init__.size (Python parameter) — Value in (0,1) to use as the test size when implementing the mcs. Default value is 0.05."><span class="n"><span class="pre">size</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.MCS.__init__.reps" title="arch.bootstrap.MCS.__init__.reps (Python parameter) — Number of bootstrap replications to uses.  Default is 1000."><span class="n"><span class="pre">reps</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.MCS.__init__.block_size" title="arch.bootstrap.MCS.__init__.block_size (Python parameter) — Length of window to use in the bootstrap.  If not provided, sqrt(T) is used.  In general, this should be provided and chosen to be appropriate for the data."><span class="n"><span class="pre">block_size</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.MCS.__init__.method" title="arch.bootstrap.MCS.__init__.method (Python parameter) — MCS test and elimination implementation method, either 'max' or 'R'. Default is 'R'."><span class="n"><span class="pre">method</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'R'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'max'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;R&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.MCS.__init__.bootstrap" title="arch.bootstrap.MCS.__init__.bootstrap (Python parameter) — Bootstrap to use.  Options are 'stationary' or 'sb': Stationary bootstrap (Default) 'circular' or 'cbb': Circular block bootstrap 'moving block' or 'mbb': Moving block bootstrap"><span class="n"><span class="pre">bootstrap</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'stationary'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'sb'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'circular'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'cbb'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'moving</span> <span class="pre">block'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'mbb'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;stationary&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.MCS.__init__.seed" title="arch.bootstrap.MCS.__init__.seed (Python parameter) — Seed value to use when creating the bootstrap used in the comparison. If an integer or None, the NumPy default_rng is used with the seed value.  If a Generator or a RandomState, the argument is used."><span class="n"><span class="pre">seed</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.26)"><span class="pre">Generator</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.26)"><span class="pre">RandomState</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/bootstrap/multiple_comparison.html#MCS"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.bootstrap.MCS" title="Link to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.bootstrap.</span></span><span class="sig-name descname"><span class="pre">MCS</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.MCS.__init__.losses" title="arch.bootstrap.MCS.__init__.losses (Python parameter) — T by k array containing losses from a set of models"><span class="n"><span class="pre">losses</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><span class="pre">DataFrame</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.MCS.__init__.size" title="arch.bootstrap.MCS.__init__.size (Python parameter) — Value in (0,1) to use as the test size when implementing the mcs. Default value is 0.05."><span class="n"><span class="pre">size</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.MCS.__init__.reps" title="arch.bootstrap.MCS.__init__.reps (Python parameter) — Number of bootstrap replications to uses.  Default is 1000."><span class="n"><span class="pre">reps</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.MCS.__init__.block_size" title="arch.bootstrap.MCS.__init__.block_size (Python parameter) — Length of window to use in the bootstrap.  If not provided, sqrt(T) is used.  In general, this should be provided and chosen to be appropriate for the data."><span class="n"><span class="pre">block_size</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.MCS.__init__.method" title="arch.bootstrap.MCS.__init__.method (Python parameter) — MCS test and elimination implementation method, either 'max' or 'R'. Default is 'R'."><span class="n"><span class="pre">method</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'R'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'max'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;R&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.MCS.__init__.bootstrap" title="arch.bootstrap.MCS.__init__.bootstrap (Python parameter) — Bootstrap to use.  Options are 'stationary' or 'sb': Stationary bootstrap (Default) 'circular' or 'cbb': Circular block bootstrap 'moving block' or 'mbb': Moving block bootstrap"><span class="n"><span class="pre">bootstrap</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'stationary'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'sb'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'circular'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'cbb'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'moving</span> <span class="pre">block'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'mbb'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;stationary&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.MCS.__init__.seed" title="arch.bootstrap.MCS.__init__.seed (Python parameter) — Seed value to use when creating the bootstrap used in the comparison. If an integer or None, the NumPy default_rng is used with the seed value.  If a Generator or a RandomState, the argument is used."><span class="n"><span class="pre">seed</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.26)"><span class="pre">Generator</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.26)"><span class="pre">RandomState</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/bootstrap/multiple_comparison.html#MCS"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.bootstrap.MCS" title="Link to this definition">¶</a></dt>
 <dd><p>Model Confidence Set (MCS) of Hansen, Lunde and Nason.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.bootstrap.MCS-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.MCS-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.bootstrap.MCS.__init__.losses"><span class="n sig-name">losses</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a></span></span><a class="headerlink" href="#arch.bootstrap.MCS.__init__.losses" title="Permalink to this definition">¶</a></dt><dd><p>T by k array containing losses from a set of models</p>
+<dt id="arch.bootstrap.MCS.__init__.losses"><span class="n sig-name">losses</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)">DataFrame</a></span></span><a class="headerlink" href="#arch.bootstrap.MCS.__init__.losses" title="Permalink to this definition">¶</a></dt><dd><p>T by k array containing losses from a set of models</p>
 </dd>
 <dt id="arch.bootstrap.MCS.__init__.size"><span class="n sig-name">size</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a></span></span><a class="headerlink" href="#arch.bootstrap.MCS.__init__.size" title="Permalink to this definition">¶</a></dt><dd><p>Value in (0,1) to use as the test size when implementing the
 mcs. Default value is 0.05.</p>
@@ -1127,7 +1127,7 @@ <h1 id="arch-bootstrap-mcs">arch.bootstrap.MCS<a class="headerlink" href="#arch-
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-        <script src="../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
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   </body>
diff --git a/devel/multiple-comparison/generated/arch.bootstrap.SPA.html b/devel/multiple-comparison/generated/arch.bootstrap.SPA.html
index ef6e07eecb..0bd5695c5d 100644
--- a/devel/multiple-comparison/generated/arch.bootstrap.SPA.html
+++ b/devel/multiple-comparison/generated/arch.bootstrap.SPA.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.SPA - arch v6.2.1 (+21)</title>
+        <title>arch.bootstrap.SPA - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
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 <h1 id="arch-bootstrap-spa">arch.bootstrap.SPA<a class="headerlink" href="#arch-bootstrap-spa" title="Link to this heading">¶</a></h1>
 <dl class="py class objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.bootstrap.SPA">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.bootstrap.</span></span><span class="sig-name descname"><span class="pre">SPA</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.SPA.__init__.benchmark" title="arch.bootstrap.SPA.__init__.benchmark (Python parameter) — T element array of benchmark model losses"><span class="n"><span class="pre">benchmark</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.SPA.__init__.models" title="arch.bootstrap.SPA.__init__.models (Python parameter) — T  by k element array of alternative model losses"><span class="n"><span class="pre">models</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.SPA.__init__.block_size" title="arch.bootstrap.SPA.__init__.block_size (Python parameter) — Length of window to use in the bootstrap.  If not provided, sqrt(T) is used.  In general, this should be provided and chosen to be appropriate for the data."><span class="n"><span class="pre">block_size</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.SPA.__init__.reps" title="arch.bootstrap.SPA.__init__.reps (Python parameter) — Number of bootstrap replications to uses.  Default is 1000."><span class="n"><span class="pre">reps</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.SPA.__init__.bootstrap" title="arch.bootstrap.SPA.__init__.bootstrap (Python parameter) — Bootstrap to use.  Options are 'stationary' or 'sb': Stationary bootstrap (Default) 'circular' or 'cbb': Circular block bootstrap 'moving block' or 'mbb': Moving block bootstrap"><span class="n"><span class="pre">bootstrap</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'stationary'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'sb'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'circular'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'cbb'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'moving</span> <span class="pre">block'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'mbb'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;stationary&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.SPA.__init__.studentize" title="arch.bootstrap.SPA.__init__.studentize (Python parameter) — Flag indicating to studentize loss differentials. Default is True"><span class="n"><span class="pre">studentize</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.SPA.__init__.nested" title="arch.bootstrap.SPA.__init__.nested (Python parameter) — Flag indicating to use a nested bootstrap to compute variances for studentization.  Default is False.  Note that this can be slow since the procedure requires k extra bootstraps."><span class="n"><span class="pre">nested</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.SPA.__init__.seed" title="arch.bootstrap.SPA.__init__.seed (Python parameter) — Seed value to use when creating the bootstrap used in the comparison. If an integer or None, the NumPy default_rng is used with the seed value.  If a Generator or a RandomState, the argument is used."><span class="n"><span class="pre">seed</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.26)"><span class="pre">Generator</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.26)"><span class="pre">RandomState</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/bootstrap/multiple_comparison.html#SPA"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.bootstrap.SPA" title="Link to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.bootstrap.</span></span><span class="sig-name descname"><span class="pre">SPA</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.SPA.__init__.benchmark" title="arch.bootstrap.SPA.__init__.benchmark (Python parameter) — T element array of benchmark model losses"><span class="n"><span class="pre">benchmark</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.SPA.__init__.models" title="arch.bootstrap.SPA.__init__.models (Python parameter) — T  by k element array of alternative model losses"><span class="n"><span class="pre">models</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.SPA.__init__.block_size" title="arch.bootstrap.SPA.__init__.block_size (Python parameter) — Length of window to use in the bootstrap.  If not provided, sqrt(T) is used.  In general, this should be provided and chosen to be appropriate for the data."><span class="n"><span class="pre">block_size</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.SPA.__init__.reps" title="arch.bootstrap.SPA.__init__.reps (Python parameter) — Number of bootstrap replications to uses.  Default is 1000."><span class="n"><span class="pre">reps</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.SPA.__init__.bootstrap" title="arch.bootstrap.SPA.__init__.bootstrap (Python parameter) — Bootstrap to use.  Options are 'stationary' or 'sb': Stationary bootstrap (Default) 'circular' or 'cbb': Circular block bootstrap 'moving block' or 'mbb': Moving block bootstrap"><span class="n"><span class="pre">bootstrap</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'stationary'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'sb'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'circular'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'cbb'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'moving</span> <span class="pre">block'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'mbb'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;stationary&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.SPA.__init__.studentize" title="arch.bootstrap.SPA.__init__.studentize (Python parameter) — Flag indicating to studentize loss differentials. Default is True"><span class="n"><span class="pre">studentize</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.SPA.__init__.nested" title="arch.bootstrap.SPA.__init__.nested (Python parameter) — Flag indicating to use a nested bootstrap to compute variances for studentization.  Default is False.  Note that this can be slow since the procedure requires k extra bootstraps."><span class="n"><span class="pre">nested</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.SPA.__init__.seed" title="arch.bootstrap.SPA.__init__.seed (Python parameter) — Seed value to use when creating the bootstrap used in the comparison. If an integer or None, the NumPy default_rng is used with the seed value.  If a Generator or a RandomState, the argument is used."><span class="n"><span class="pre">seed</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.26)"><span class="pre">Generator</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.26)"><span class="pre">RandomState</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/bootstrap/multiple_comparison.html#SPA"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.bootstrap.SPA" title="Link to this definition">¶</a></dt>
 <dd><p>Test of Superior Predictive Ability (SPA) of White and Hansen.</p>
 <p>The SPA is also known as the Reality Check or Bootstrap Data Snooper.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.bootstrap.SPA-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.SPA-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.bootstrap.SPA.__init__.benchmark"><span class="n sig-name">benchmark</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.bootstrap.SPA.__init__.benchmark" title="Permalink to this definition">¶</a></dt><dd><p>T element array of benchmark model <em>losses</em></p>
+<dt id="arch.bootstrap.SPA.__init__.benchmark"><span class="n sig-name">benchmark</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a></span></span><a class="headerlink" href="#arch.bootstrap.SPA.__init__.benchmark" title="Permalink to this definition">¶</a></dt><dd><p>T element array of benchmark model <em>losses</em></p>
 </dd>
-<dt id="arch.bootstrap.SPA.__init__.models"><span class="n sig-name">models</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.bootstrap.SPA.__init__.models" title="Permalink to this definition">¶</a></dt><dd><p>T  by k element array of alternative model <em>losses</em></p>
+<dt id="arch.bootstrap.SPA.__init__.models"><span class="n sig-name">models</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a></span></span><a class="headerlink" href="#arch.bootstrap.SPA.__init__.models" title="Permalink to this definition">¶</a></dt><dd><p>T  by k element array of alternative model <em>losses</em></p>
 </dd>
 <dt id="arch.bootstrap.SPA.__init__.block_size"><span class="n sig-name">block_size</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.bootstrap.SPA.__init__.block_size" title="Permalink to this definition">¶</a></dt><dd><p>Length of window to use in the bootstrap.  If not provided, sqrt(T)
 is used.  In general, this should be provided and chosen to be
@@ -1186,7 +1186,7 @@ <h1 id="arch-bootstrap-spa">arch.bootstrap.SPA<a class="headerlink" href="#arch-
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diff --git a/devel/multiple-comparison/generated/arch.bootstrap.StepM.html b/devel/multiple-comparison/generated/arch.bootstrap.StepM.html
index c317138fa9..870726064f 100644
--- a/devel/multiple-comparison/generated/arch.bootstrap.StepM.html
+++ b/devel/multiple-comparison/generated/arch.bootstrap.StepM.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.StepM - arch v6.2.1 (+21)</title>
+        <title>arch.bootstrap.StepM - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
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@@ -102,7 +102,7 @@
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-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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@@ -189,10 +189,10 @@
 
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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@@ -926,14 +926,14 @@
 <h1 id="arch-bootstrap-stepm">arch.bootstrap.StepM<a class="headerlink" href="#arch-bootstrap-stepm" title="Link to this heading">¶</a></h1>
 <dl class="py class objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.bootstrap.StepM">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.bootstrap.</span></span><span class="sig-name descname"><span class="pre">StepM</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.StepM.__init__.benchmark" title="arch.bootstrap.StepM.__init__.benchmark (Python parameter) — T element array of benchmark model losses"><span class="n"><span class="pre">benchmark</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.StepM.__init__.models" title="arch.bootstrap.StepM.__init__.models (Python parameter) — T by k element array of alternative model losses"><span class="n"><span class="pre">models</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.StepM.__init__.size" title="arch.bootstrap.StepM.__init__.size (Python parameter) — Value in (0,1) to use as the test size when implementing the comparison. Default value is 0.05."><span class="n"><span class="pre">size</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mf">0.05</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.StepM.__init__.block_size" title="arch.bootstrap.StepM.__init__.block_size (Python parameter) — Length of window to use in the bootstrap.  If not provided, sqrt(T) is used.  In general, this should be provided and chosen to be appropriate for the data."><span class="n"><span class="pre">block_size</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.StepM.__init__.reps" title="arch.bootstrap.StepM.__init__.reps (Python parameter) — Number of bootstrap replications to uses.  Default is 1000."><span class="n"><span class="pre">reps</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.StepM.__init__.bootstrap" title="arch.bootstrap.StepM.__init__.bootstrap (Python parameter) — Bootstrap to use.  Options are 'stationary' or 'sb': Stationary bootstrap (Default) 'circular' or 'cbb': Circular block bootstrap 'moving block' or 'mbb': Moving block bootstrap"><span class="n"><span class="pre">bootstrap</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'stationary'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'sb'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'circular'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'cbb'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'moving</span> <span class="pre">block'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'mbb'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;stationary&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.StepM.__init__.studentize" title="arch.bootstrap.StepM.__init__.studentize (Python parameter) — Flag indicating to studentize loss differentials. Default is True"><span class="n"><span class="pre">studentize</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.StepM.__init__.nested" title="arch.bootstrap.StepM.__init__.nested (Python parameter) — Flag indicating to use a nested bootstrap to compute variances for studentization.  Default is False.  Note that this can be slow since the procedure requires k extra bootstraps."><span class="n"><span class="pre">nested</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.StepM.__init__.seed" title="arch.bootstrap.StepM.__init__.seed (Python parameter) — Seed value to use when creating the bootstrap used in the comparison. If an integer or None, the NumPy default_rng is used with the seed value.  If a Generator or a RandomState, the argument is used."><span class="n"><span class="pre">seed</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.26)"><span class="pre">Generator</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.26)"><span class="pre">RandomState</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/bootstrap/multiple_comparison.html#StepM"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.bootstrap.StepM" title="Link to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.bootstrap.</span></span><span class="sig-name descname"><span class="pre">StepM</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.StepM.__init__.benchmark" title="arch.bootstrap.StepM.__init__.benchmark (Python parameter) — T element array of benchmark model losses"><span class="n"><span class="pre">benchmark</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.StepM.__init__.models" title="arch.bootstrap.StepM.__init__.models (Python parameter) — T by k element array of alternative model losses"><span class="n"><span class="pre">models</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.StepM.__init__.size" title="arch.bootstrap.StepM.__init__.size (Python parameter) — Value in (0,1) to use as the test size when implementing the comparison. Default value is 0.05."><span class="n"><span class="pre">size</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mf">0.05</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.StepM.__init__.block_size" title="arch.bootstrap.StepM.__init__.block_size (Python parameter) — Length of window to use in the bootstrap.  If not provided, sqrt(T) is used.  In general, this should be provided and chosen to be appropriate for the data."><span class="n"><span class="pre">block_size</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.StepM.__init__.reps" title="arch.bootstrap.StepM.__init__.reps (Python parameter) — Number of bootstrap replications to uses.  Default is 1000."><span class="n"><span class="pre">reps</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.StepM.__init__.bootstrap" title="arch.bootstrap.StepM.__init__.bootstrap (Python parameter) — Bootstrap to use.  Options are 'stationary' or 'sb': Stationary bootstrap (Default) 'circular' or 'cbb': Circular block bootstrap 'moving block' or 'mbb': Moving block bootstrap"><span class="n"><span class="pre">bootstrap</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'stationary'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'sb'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'circular'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'cbb'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'moving</span> <span class="pre">block'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'mbb'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;stationary&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.StepM.__init__.studentize" title="arch.bootstrap.StepM.__init__.studentize (Python parameter) — Flag indicating to studentize loss differentials. Default is True"><span class="n"><span class="pre">studentize</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.StepM.__init__.nested" title="arch.bootstrap.StepM.__init__.nested (Python parameter) — Flag indicating to use a nested bootstrap to compute variances for studentization.  Default is False.  Note that this can be slow since the procedure requires k extra bootstraps."><span class="n"><span class="pre">nested</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.StepM.__init__.seed" title="arch.bootstrap.StepM.__init__.seed (Python parameter) — Seed value to use when creating the bootstrap used in the comparison. If an integer or None, the NumPy default_rng is used with the seed value.  If a Generator or a RandomState, the argument is used."><span class="n"><span class="pre">seed</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.26)"><span class="pre">Generator</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.26)"><span class="pre">RandomState</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/bootstrap/multiple_comparison.html#StepM"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.bootstrap.StepM" title="Link to this definition">¶</a></dt>
 <dd><p>StepM multiple comparison procedure of Romano and Wolf.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.bootstrap.StepM-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.StepM-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.bootstrap.StepM.__init__.benchmark"><span class="n sig-name">benchmark</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.bootstrap.StepM.__init__.benchmark" title="Permalink to this definition">¶</a></dt><dd><p>T element array of benchmark model <em>losses</em></p>
+<dt id="arch.bootstrap.StepM.__init__.benchmark"><span class="n sig-name">benchmark</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a></span></span><a class="headerlink" href="#arch.bootstrap.StepM.__init__.benchmark" title="Permalink to this definition">¶</a></dt><dd><p>T element array of benchmark model <em>losses</em></p>
 </dd>
-<dt id="arch.bootstrap.StepM.__init__.models"><span class="n sig-name">models</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.bootstrap.StepM.__init__.models" title="Permalink to this definition">¶</a></dt><dd><p>T by k element array of alternative model <em>losses</em></p>
+<dt id="arch.bootstrap.StepM.__init__.models"><span class="n sig-name">models</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a></span></span><a class="headerlink" href="#arch.bootstrap.StepM.__init__.models" title="Permalink to this definition">¶</a></dt><dd><p>T by k element array of alternative model <em>losses</em></p>
 </dd>
 <dt id="arch.bootstrap.StepM.__init__.size"><span class="n sig-name">size</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mf">0.05</span></code><a class="headerlink" href="#arch.bootstrap.StepM.__init__.size" title="Permalink to this definition">¶</a></dt><dd><p>Value in (0,1) to use as the test size when implementing the
 comparison. Default value is 0.05.</p>
@@ -1117,7 +1117,7 @@ <h1 id="arch-bootstrap-stepm">arch.bootstrap.StepM<a class="headerlink" href="#a
     <script id="__config" type="application/json">{"base": "../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/devel/multiple-comparison/generated/generated/arch.bootstrap.MCS.compute.html b/devel/multiple-comparison/generated/generated/arch.bootstrap.MCS.compute.html
index 4227e0a834..b7122ad5ac 100644
--- a/devel/multiple-comparison/generated/generated/arch.bootstrap.MCS.compute.html
+++ b/devel/multiple-comparison/generated/generated/arch.bootstrap.MCS.compute.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.MCS.compute - arch v6.2.1 (+21)</title>
+        <title>arch.bootstrap.MCS.compute - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -879,7 +879,7 @@ <h1 id="arch-bootstrap-mcs-compute">arch.bootstrap.MCS.compute<a class="headerli
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/devel/multiple-comparison/generated/generated/arch.bootstrap.MCS.excluded.html b/devel/multiple-comparison/generated/generated/arch.bootstrap.MCS.excluded.html
index db8cd1bbca..3db29105f2 100644
--- a/devel/multiple-comparison/generated/generated/arch.bootstrap.MCS.excluded.html
+++ b/devel/multiple-comparison/generated/generated/arch.bootstrap.MCS.excluded.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.MCS.excluded - arch v6.2.1 (+21)</title>
+        <title>arch.bootstrap.MCS.excluded - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -927,7 +927,7 @@ <h1 id="arch-bootstrap-mcs-excluded">arch.bootstrap.MCS.excluded<a class="header
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/devel/multiple-comparison/generated/generated/arch.bootstrap.MCS.included.html b/devel/multiple-comparison/generated/generated/arch.bootstrap.MCS.included.html
index 53205e5ae6..1b9be85131 100644
--- a/devel/multiple-comparison/generated/generated/arch.bootstrap.MCS.included.html
+++ b/devel/multiple-comparison/generated/generated/arch.bootstrap.MCS.included.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.MCS.included - arch v6.2.1 (+21)</title>
+        <title>arch.bootstrap.MCS.included - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -927,7 +927,7 @@ <h1 id="arch-bootstrap-mcs-included">arch.bootstrap.MCS.included<a class="header
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
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   </body>
diff --git a/devel/multiple-comparison/generated/generated/arch.bootstrap.MCS.pvalues.html b/devel/multiple-comparison/generated/generated/arch.bootstrap.MCS.pvalues.html
index 387beb8554..010e60e425 100644
--- a/devel/multiple-comparison/generated/generated/arch.bootstrap.MCS.pvalues.html
+++ b/devel/multiple-comparison/generated/generated/arch.bootstrap.MCS.pvalues.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.MCS.pvalues - arch v6.2.1 (+21)</title>
+        <title>arch.bootstrap.MCS.pvalues - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -801,7 +801,7 @@
 <h1 id="arch-bootstrap-mcs-pvalues">arch.bootstrap.MCS.pvalues<a class="headerlink" href="#arch-bootstrap-mcs-pvalues" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.bootstrap.MCS.pvalues">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">MCS.</span></span><span class="sig-name descname"><span class="pre">pvalues</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a></span><a class="headerlink" href="#arch.bootstrap.MCS.pvalues" title="Link to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">MCS.</span></span><span class="sig-name descname"><span class="pre">pvalues</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><span class="pre">DataFrame</span></a></span><a class="headerlink" href="#arch.bootstrap.MCS.pvalues" title="Link to this definition">¶</a></dt>
 <dd><p>Model p-values for inclusion in the MCS</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.bootstrap.MCS.pvalues-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.MCS.pvalues-returns" title="Permalink to this headline">¶</a></dt>
@@ -809,7 +809,7 @@ <h1 id="arch-bootstrap-mcs-pvalues">arch.bootstrap.MCS.pvalues<a class="headerli
 containing the smallest size where the model is in the MCS.</p>
 </dd>
 <dt class="field-even" id="arch.bootstrap.MCS.pvalues-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.MCS.pvalues-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><code class="xref py py-class docutils literal notranslate"><span class="pre">pandas.DataFrame</span></code></a></p>
+<dd class="field-even"><p><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><code class="xref py py-class docutils literal notranslate"><span class="pre">pandas.DataFrame</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -928,7 +928,7 @@ <h1 id="arch-bootstrap-mcs-pvalues">arch.bootstrap.MCS.pvalues<a class="headerli
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/devel/multiple-comparison/generated/generated/arch.bootstrap.MCS.reset.html b/devel/multiple-comparison/generated/generated/arch.bootstrap.MCS.reset.html
index e1a3dfbf7d..b09264988d 100644
--- a/devel/multiple-comparison/generated/generated/arch.bootstrap.MCS.reset.html
+++ b/devel/multiple-comparison/generated/generated/arch.bootstrap.MCS.reset.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.MCS.reset - arch v6.2.1 (+21)</title>
+        <title>arch.bootstrap.MCS.reset - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -879,7 +879,7 @@ <h1 id="arch-bootstrap-mcs-reset">arch.bootstrap.MCS.reset<a class="headerlink"
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/devel/multiple-comparison/generated/generated/arch.bootstrap.MCS.seed.html b/devel/multiple-comparison/generated/generated/arch.bootstrap.MCS.seed.html
index ddb8d19980..ee309ee12f 100644
--- a/devel/multiple-comparison/generated/generated/arch.bootstrap.MCS.seed.html
+++ b/devel/multiple-comparison/generated/generated/arch.bootstrap.MCS.seed.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.MCS.seed - arch v6.2.1 (+21)</title>
+        <title>arch.bootstrap.MCS.seed - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -939,7 +939,7 @@ <h1 id="arch-bootstrap-mcs-seed">arch.bootstrap.MCS.seed<a class="headerlink" hr
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/devel/multiple-comparison/generated/generated/arch.bootstrap.SPA.better_models.html b/devel/multiple-comparison/generated/generated/arch.bootstrap.SPA.better_models.html
index 63cdd600dd..c80ce3d360 100644
--- a/devel/multiple-comparison/generated/generated/arch.bootstrap.SPA.better_models.html
+++ b/devel/multiple-comparison/generated/generated/arch.bootstrap.SPA.better_models.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.SPA.better_models - arch v6.2.1 (+21)</title>
+        <title>arch.bootstrap.SPA.better_models - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -1010,7 +1010,7 @@ <h1 id="arch-bootstrap-spa-better-models">arch.bootstrap.SPA.better_models<a cla
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/devel/multiple-comparison/generated/generated/arch.bootstrap.SPA.compute.html b/devel/multiple-comparison/generated/generated/arch.bootstrap.SPA.compute.html
index d696406d7e..373bc2df1b 100644
--- a/devel/multiple-comparison/generated/generated/arch.bootstrap.SPA.compute.html
+++ b/devel/multiple-comparison/generated/generated/arch.bootstrap.SPA.compute.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.SPA.compute - arch v6.2.1 (+21)</title>
+        <title>arch.bootstrap.SPA.compute - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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     <div class="md-nav__source">
@@ -896,7 +896,7 @@ <h1 id="arch-bootstrap-spa-compute">arch.bootstrap.SPA.compute<a class="headerli
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-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/devel/multiple-comparison/generated/generated/arch.bootstrap.SPA.critical_values.html b/devel/multiple-comparison/generated/generated/arch.bootstrap.SPA.critical_values.html
index 7000b327e0..6b1179a8ef 100644
--- a/devel/multiple-comparison/generated/generated/arch.bootstrap.SPA.critical_values.html
+++ b/devel/multiple-comparison/generated/generated/arch.bootstrap.SPA.critical_values.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.SPA.critical_values - arch v6.2.1 (+21)</title>
+        <title>arch.bootstrap.SPA.critical_values - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -856,7 +856,7 @@
 <h1 id="arch-bootstrap-spa-critical-values">arch.bootstrap.SPA.critical_values<a class="headerlink" href="#arch-bootstrap-spa-critical-values" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight py" id="arch.bootstrap.SPA.critical_values">
-<span class="sig-prename descclassname"><span class="pre">SPA.</span></span><span class="sig-name descname"><span class="pre">critical_values</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.SPA.critical_values.pvalue" title="arch.bootstrap.SPA.critical_values.pvalue (Python parameter) — P-value in (0,1) to use when computing the critical values."><span class="n"><span class="pre">pvalue</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mf">0.05</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/bootstrap/multiple_comparison.html#SPA.critical_values"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.bootstrap.SPA.critical_values" title="Link to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">SPA.</span></span><span class="sig-name descname"><span class="pre">critical_values</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.bootstrap.SPA.critical_values.pvalue" title="arch.bootstrap.SPA.critical_values.pvalue (Python parameter) — P-value in (0,1) to use when computing the critical values."><span class="n"><span class="pre">pvalue</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mf">0.05</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/bootstrap/multiple_comparison.html#SPA.critical_values"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.bootstrap.SPA.critical_values" title="Link to this definition">¶</a></dt>
 <dd><p>Returns data-dependent critical values</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.bootstrap.SPA.critical_values-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.SPA.critical_values-parameters" title="Permalink to this headline">¶</a></dt>
@@ -870,7 +870,7 @@ <h1 id="arch-bootstrap-spa-critical-values">arch.bootstrap.SPA.critical_values<a
 upper methodologies</p>
 </dd>
 <dt class="field-odd" id="arch.bootstrap.SPA.critical_values-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.SPA.critical_values-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><code class="xref py py-class docutils literal notranslate"><span class="pre">pandas.Series</span></code></a></p>
+<dd class="field-odd"><p><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><code class="xref py py-class docutils literal notranslate"><span class="pre">pandas.Series</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -989,7 +989,7 @@ <h1 id="arch-bootstrap-spa-critical-values">arch.bootstrap.SPA.critical_values<a
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
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   </body>
diff --git a/devel/multiple-comparison/generated/generated/arch.bootstrap.SPA.pvalues.html b/devel/multiple-comparison/generated/generated/arch.bootstrap.SPA.pvalues.html
index cf23d6c9c8..7584e089f5 100644
--- a/devel/multiple-comparison/generated/generated/arch.bootstrap.SPA.pvalues.html
+++ b/devel/multiple-comparison/generated/generated/arch.bootstrap.SPA.pvalues.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.SPA.pvalues - arch v6.2.1 (+21)</title>
+        <title>arch.bootstrap.SPA.pvalues - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -816,7 +816,7 @@
 <h1 id="arch-bootstrap-spa-pvalues">arch.bootstrap.SPA.pvalues<a class="headerlink" href="#arch-bootstrap-spa-pvalues" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.bootstrap.SPA.pvalues">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">SPA.</span></span><span class="sig-name descname"><span class="pre">pvalues</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span><a class="headerlink" href="#arch.bootstrap.SPA.pvalues" title="Link to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">SPA.</span></span><span class="sig-name descname"><span class="pre">pvalues</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a></span><a class="headerlink" href="#arch.bootstrap.SPA.pvalues" title="Link to this definition">¶</a></dt>
 <dd><p>P-values corresponding to the lower, consistent and
 upper p-values.</p>
 <dl class="field-list simple">
@@ -825,7 +825,7 @@ <h1 id="arch-bootstrap-spa-pvalues">arch.bootstrap.SPA.pvalues<a class="headerli
 estimator, and the upper bound.</p>
 </dd>
 <dt class="field-even" id="arch.bootstrap.SPA.pvalues-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.bootstrap.SPA.pvalues-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><code class="xref py py-class docutils literal notranslate"><span class="pre">pandas.Series</span></code></a></p>
+<dd class="field-even"><p><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><code class="xref py py-class docutils literal notranslate"><span class="pre">pandas.Series</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -944,7 +944,7 @@ <h1 id="arch-bootstrap-spa-pvalues">arch.bootstrap.SPA.pvalues<a class="headerli
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
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   </body>
diff --git a/devel/multiple-comparison/generated/generated/arch.bootstrap.SPA.reset.html b/devel/multiple-comparison/generated/generated/arch.bootstrap.SPA.reset.html
index 57ad1b7ac0..437a632783 100644
--- a/devel/multiple-comparison/generated/generated/arch.bootstrap.SPA.reset.html
+++ b/devel/multiple-comparison/generated/generated/arch.bootstrap.SPA.reset.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.SPA.reset - arch v6.2.1 (+21)</title>
+        <title>arch.bootstrap.SPA.reset - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -894,7 +894,7 @@ <h1 id="arch-bootstrap-spa-reset">arch.bootstrap.SPA.reset<a class="headerlink"
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/devel/multiple-comparison/generated/generated/arch.bootstrap.SPA.seed.html b/devel/multiple-comparison/generated/generated/arch.bootstrap.SPA.seed.html
index 849f4c1b60..ae87dbea1c 100644
--- a/devel/multiple-comparison/generated/generated/arch.bootstrap.SPA.seed.html
+++ b/devel/multiple-comparison/generated/generated/arch.bootstrap.SPA.seed.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.SPA.seed - arch v6.2.1 (+21)</title>
+        <title>arch.bootstrap.SPA.seed - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -954,7 +954,7 @@ <h1 id="arch-bootstrap-spa-seed">arch.bootstrap.SPA.seed<a class="headerlink" hr
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/devel/multiple-comparison/generated/generated/arch.bootstrap.SPA.subset.html b/devel/multiple-comparison/generated/generated/arch.bootstrap.SPA.subset.html
index 7c11a9b1f9..e4b50b9d4f 100644
--- a/devel/multiple-comparison/generated/generated/arch.bootstrap.SPA.subset.html
+++ b/devel/multiple-comparison/generated/generated/arch.bootstrap.SPA.subset.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.SPA.subset - arch v6.2.1 (+21)</title>
+        <title>arch.bootstrap.SPA.subset - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -956,7 +956,7 @@ <h1 id="arch-bootstrap-spa-subset">arch.bootstrap.SPA.subset<a class="headerlink
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/devel/multiple-comparison/generated/generated/arch.bootstrap.StepM.compute.html b/devel/multiple-comparison/generated/generated/arch.bootstrap.StepM.compute.html
index 4563ebd926..598bf5993e 100644
--- a/devel/multiple-comparison/generated/generated/arch.bootstrap.StepM.compute.html
+++ b/devel/multiple-comparison/generated/generated/arch.bootstrap.StepM.compute.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.StepM.compute - arch v6.2.1 (+21)</title>
+        <title>arch.bootstrap.StepM.compute - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -849,7 +849,7 @@ <h1 id="arch-bootstrap-stepm-compute">arch.bootstrap.StepM.compute<a class="head
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/devel/multiple-comparison/generated/generated/arch.bootstrap.StepM.reset.html b/devel/multiple-comparison/generated/generated/arch.bootstrap.StepM.reset.html
index 0a9eb9182a..4ae7770901 100644
--- a/devel/multiple-comparison/generated/generated/arch.bootstrap.StepM.reset.html
+++ b/devel/multiple-comparison/generated/generated/arch.bootstrap.StepM.reset.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.StepM.reset - arch v6.2.1 (+21)</title>
+        <title>arch.bootstrap.StepM.reset - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -849,7 +849,7 @@ <h1 id="arch-bootstrap-stepm-reset">arch.bootstrap.StepM.reset<a class="headerli
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/devel/multiple-comparison/generated/generated/arch.bootstrap.StepM.seed.html b/devel/multiple-comparison/generated/generated/arch.bootstrap.StepM.seed.html
index 004e28cbef..d1643c7e7f 100644
--- a/devel/multiple-comparison/generated/generated/arch.bootstrap.StepM.seed.html
+++ b/devel/multiple-comparison/generated/generated/arch.bootstrap.StepM.seed.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.StepM.seed - arch v6.2.1 (+21)</title>
+        <title>arch.bootstrap.StepM.seed - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -909,7 +909,7 @@ <h1 id="arch-bootstrap-stepm-seed">arch.bootstrap.StepM.seed<a class="headerlink
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/devel/multiple-comparison/generated/generated/arch.bootstrap.StepM.superior_models.html b/devel/multiple-comparison/generated/generated/arch.bootstrap.StepM.superior_models.html
index af23835343..4c22ab7426 100644
--- a/devel/multiple-comparison/generated/generated/arch.bootstrap.StepM.superior_models.html
+++ b/devel/multiple-comparison/generated/generated/arch.bootstrap.StepM.superior_models.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.bootstrap.StepM.superior_models - arch v6.2.1 (+21)</title>
+        <title>arch.bootstrap.StepM.superior_models - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -898,7 +898,7 @@ <h1 id="arch-bootstrap-stepm-superior-models">arch.bootstrap.StepM.superior_mode
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/devel/multiple-comparison/multiple-comparison-reference.html b/devel/multiple-comparison/multiple-comparison-reference.html
index aaf0e8f2bb..f8521c914c 100644
--- a/devel/multiple-comparison/multiple-comparison-reference.html
+++ b/devel/multiple-comparison/multiple-comparison-reference.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>Module Reference - arch v6.2.1 (+21)</title>
+        <title>Module Reference - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -914,7 +914,7 @@ <h2 id="model-confidence-set-mcs">Model Confidence Set (MCS)<a class="headerlink
     <script id="__config" type="application/json">{"base": "..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
         <script>window.MathJax = {"tex": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true}, "options": {"ignoreHtmlClass": "tex2jax_ignore|mathjax_ignore|document", "processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
         <script id="MathJax-script" src="../_static/mathjax/tex-mml-chtml.js?v=cadf963e"></script>
diff --git a/devel/multiple-comparison/multiple-comparison_examples.html b/devel/multiple-comparison/multiple-comparison_examples.html
index 6c67d4a935..da90a9dfa5 100644
--- a/devel/multiple-comparison/multiple-comparison_examples.html
+++ b/devel/multiple-comparison/multiple-comparison_examples.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>Multiple Comparisons - arch v6.2.1 (+21)</title>
+        <title>Multiple Comparisons - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../_static/nbsphinx-code-cells.css?v=2aa19091" />
         <link rel="stylesheet" type="text/css" href="../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
@@ -93,7 +93,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -103,7 +103,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -190,10 +190,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -1054,7 +1054,7 @@ <h2 id="The-Model-Confidence-Set">The Model Confidence Set<a class="headerlink"
     <script id="__config" type="application/json">{"base": "..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
         <script>window.MathJax = {"tex": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true}, "options": {"ignoreHtmlClass": "tex2jax_ignore|mathjax_ignore|document", "processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
         <script id="MathJax-script" src="../_static/mathjax/tex-mml-chtml.js?v=cadf963e"></script>
diff --git a/devel/multiple-comparison/multiple-comparisons.html b/devel/multiple-comparison/multiple-comparisons.html
index 764b80ebd1..04643dce01 100644
--- a/devel/multiple-comparison/multiple-comparisons.html
+++ b/devel/multiple-comparison/multiple-comparisons.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>Multiple Comparison Procedures - arch v6.2.1 (+21)</title>
+        <title>Multiple Comparison Procedures - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
@@ -87,7 +87,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -97,7 +97,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -184,10 +184,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
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@@ -618,7 +618,7 @@ <h1 id="multiple-comparison-procedures">Multiple Comparison Procedures<a class="
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diff --git a/devel/searchindex.js b/devel/searchindex.js
index 9420a9e6ee..1d9ee7ccaa 100644
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768, 769, 771, 772, 773, 775, 776, 786, 787, 788, 793, 794, 795, 797, 798, 799, 802, 803, 804, 805, 809, 812, 813, 819, 822, 836, 857, 859, 860, 861, 863, 864, 866, 867, 868, 870, 871, 872, 873, 874, 875, 876, 877, 878, 879, 882, 883, 885, 887, 889, 891, 900], "length": [5, 7, 8, 9, 10, 90, 91, 95, 96, 97, 238, 239, 240, 262, 263, 265, 267, 271, 276, 281, 294, 392, 420, 421, 424, 427, 428, 429, 437, 438, 439, 440, 442, 445, 496, 502, 513, 518, 529, 534, 678, 683, 694, 699, 783, 788, 799, 812, 840, 891, 897], "wrap": [5, 8, 9, 10, 90], "around": [5, 8, 9, 10, 90, 258, 420], "posit": [5, 6, 7, 8, 9, 12, 13, 27, 28, 42, 43, 48, 57, 58, 72, 73, 87, 118, 128, 142, 152, 162, 172, 182, 192, 202, 212, 222, 232, 259, 265, 266, 421, 424, 433, 435, 496, 513, 529, 678, 694, 783, 799, 811, 812, 835, 840, 895], "ensur": [5, 6, 7, 8, 9, 89, 277, 436, 441, 443, 444, 471, 488, 554, 571, 588, 606, 623, 640, 670, 719, 749, 889], "reproduc": [5, 6, 7, 8, 9, 89, 95, 259, 436, 441, 443, 444, 898], "If": [5, 6, 7, 8, 9, 20, 23, 35, 38, 50, 53, 65, 68, 80, 83, 95, 96, 100, 101, 104, 105, 106, 107, 108, 109, 110, 111, 238, 239, 240, 262, 263, 264, 265, 266, 267, 271, 277, 354, 362, 368, 377, 381, 402, 424, 425, 427, 428, 429, 434, 436, 438, 439, 441, 443, 444, 445, 450, 461, 472, 478, 489, 494, 496, 502, 511, 513, 518, 527, 529, 544, 555, 561, 572, 578, 589, 595, 607, 613, 624, 630, 641, 660, 671, 676, 678, 683, 692, 694, 699, 709, 720, 739, 750, 781, 783, 797, 799, 809, 812, 813, 819, 833, 840, 841, 849, 854, 879, 890, 891, 895, 897], "fresh": [5, 6, 8, 9, 13, 28, 43, 58, 73], "system": [5, 6, 8, 9, 271, 436, 441, 443, 444], "deprec": [5, 6, 8, 9, 92, 93, 96, 436, 441, 443, 444, 652, 731, 761, 774, 869], "ha": [5, 6, 7, 8, 9, 20, 35, 50, 65, 80, 95, 96, 259, 267, 419, 421, 424, 430, 432, 442, 725, 812, 840, 873, 895, 897, 898, 899], "been": [5, 6, 7, 8, 9, 20, 35, 50, 65, 80, 95, 96, 267, 421, 437, 812, 840, 854, 873, 895, 899], "tupl": [5, 6, 7, 8, 9, 12, 17, 18, 22, 23, 27, 32, 33, 37, 38, 39, 42, 47, 48, 52, 53, 54, 57, 62, 63, 67, 68, 72, 77, 78, 82, 83, 87, 456, 459, 465, 475, 477, 478, 482, 490, 492, 496, 507, 509, 513, 523, 525, 529, 541, 543, 544, 548, 558, 560, 561, 565, 575, 577, 578, 582, 592, 594, 595, 599, 610, 612, 613, 617, 627, 629, 630, 634, 642, 644, 653, 657, 659, 660, 664, 672, 674, 678, 688, 690, 694, 706, 708, 709, 713, 721, 723, 732, 736, 738, 739, 743, 751, 753, 762, 764, 766, 775, 777, 779, 783, 793, 795, 799, 812, 840, 859, 861, 870, 876, 878, 879, 883], "pos_data": [5, 6, 7, 8, 9], "kw_data": [5, 6, 7, 8, 9], "type": [5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 19, 23, 25, 26, 27, 28, 29, 30, 31, 32, 34, 38, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 53, 55, 56, 57, 58, 59, 60, 61, 62, 64, 68, 70, 71, 72, 73, 74, 75, 76, 77, 79, 83, 85, 86, 95, 96, 112, 113, 114, 115, 117, 118, 119, 120, 121, 122, 123, 124, 125, 127, 128, 129, 130, 131, 136, 137, 138, 139, 141, 142, 143, 144, 145, 146, 147, 148, 149, 151, 152, 153, 154, 155, 156, 157, 158, 159, 161, 162, 163, 164, 165, 166, 167, 168, 169, 171, 172, 173, 174, 175, 176, 177, 178, 179, 181, 182, 183, 184, 185, 186, 187, 188, 189, 191, 192, 193, 194, 195, 196, 197, 198, 199, 201, 202, 203, 204, 205, 206, 207, 208, 209, 211, 212, 213, 214, 215, 216, 217, 218, 219, 221, 222, 223, 224, 225, 226, 227, 228, 229, 231, 232, 233, 234, 235, 242, 243, 244, 247, 249, 250, 257, 268, 272, 276, 277, 280, 293, 306, 317, 324, 331, 345, 354, 362, 366, 368, 371, 377, 381, 385, 389, 395, 398, 402, 406, 411, 421, 424, 454, 455, 456, 461, 464, 467, 469, 471, 472, 473, 474, 475, 478, 481, 484, 486, 488, 489, 490, 494, 495, 496, 500, 501, 502, 503, 507, 511, 512, 513, 516, 517, 518, 519, 523, 527, 528, 529, 532, 533, 534, 535, 539, 540, 541, 544, 547, 550, 552, 554, 555, 556, 557, 558, 561, 564, 567, 569, 571, 572, 573, 574, 575, 578, 581, 584, 586, 588, 589, 590, 591, 592, 595, 598, 601, 604, 606, 607, 608, 609, 610, 613, 616, 619, 621, 623, 624, 625, 626, 627, 630, 633, 636, 638, 640, 641, 642, 643, 646, 647, 649, 650, 651, 653, 654, 655, 656, 657, 660, 663, 666, 668, 670, 671, 672, 676, 677, 678, 681, 682, 683, 684, 688, 692, 693, 694, 697, 698, 699, 700, 704, 705, 706, 709, 712, 715, 717, 719, 720, 721, 722, 725, 726, 728, 729, 730, 732, 733, 734, 735, 736, 739, 742, 745, 747, 749, 750, 751, 752, 755, 756, 758, 759, 760, 762, 763, 764, 765, 768, 769, 771, 772, 773, 775, 776, 777, 781, 782, 783, 786, 787, 788, 789, 793, 797, 798, 799, 802, 803, 805, 809, 811, 812, 813, 819, 822, 825, 833, 835, 836, 840, 841, 846, 849, 857, 859, 860, 864, 866, 867, 868, 870, 871, 874, 875, 876, 879, 882, 885, 887, 889, 890, 891, 897, 899, 900], "enter": [5, 6, 7, 8, 9, 12, 27, 42, 57, 72, 892], "dict": [5, 6, 7, 8, 9, 11, 12, 14, 15, 17, 18, 22, 23, 25, 26, 27, 29, 30, 32, 33, 37, 38, 39, 40, 41, 42, 44, 45, 47, 48, 52, 53, 54, 55, 56, 57, 59, 60, 62, 63, 67, 68, 70, 71, 72, 74, 75, 77, 78, 82, 83, 85, 279, 292, 305, 316, 329, 344, 494, 496, 511, 513, 527, 529, 676, 678, 692, 694, 781, 783, 797, 799, 812, 840, 900], "note": [5, 6, 7, 8, 9, 10, 11, 14, 26, 29, 41, 44, 56, 59, 71, 74, 100, 101, 104, 105, 106, 107, 108, 109, 110, 111, 120, 130, 144, 154, 164, 174, 184, 194, 204, 214, 224, 234, 238, 239, 240, 247, 248, 258, 262, 263, 264, 265, 266, 267, 269, 271, 274, 276, 277, 362, 368, 377, 381, 398, 421, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 437, 438, 439, 440, 442, 443, 445, 452, 459, 461, 477, 478, 492, 494, 495, 496, 509, 511, 512, 513, 525, 527, 528, 529, 543, 544, 560, 561, 577, 578, 594, 595, 612, 613, 629, 630, 644, 646, 650, 654, 659, 660, 674, 676, 677, 678, 690, 692, 693, 694, 708, 709, 723, 725, 729, 733, 738, 739, 753, 755, 759, 763, 766, 768, 772, 776, 779, 781, 782, 783, 795, 797, 798, 799, 812, 840, 861, 863, 867, 871, 872, 873, 878, 879, 891, 895, 897, 899], "support": [5, 6, 7, 8, 9, 91, 95, 96, 97, 277, 288, 301, 312, 325, 339, 351, 366, 385, 417, 424, 461, 470, 478, 487, 494, 496, 511, 513, 527, 529, 544, 553, 561, 570, 578, 587, 595, 605, 613, 622, 630, 639, 660, 669, 676, 678, 692, 694, 709, 718, 739, 748, 781, 783, 797, 799, 812, 813, 819, 840, 841, 849, 879, 888, 891, 892, 895, 899], "date": [5, 6, 7, 8, 9, 91, 97, 421, 424, 496, 513, 529, 678, 694, 783, 799, 812, 813, 840, 841, 891, 895, 896, 897, 898, 899], "accessibli": [5, 6, 7, 8, 9], "attribut": [5, 6, 7, 8, 9, 424, 897, 899], "To": [5, 6, 7, 8, 9, 420, 422], "you": [5, 6, 7, 8, 9, 895, 899], "after": [5, 6, 7, 8, 9, 95, 96, 424, 496, 513, 529, 678, 694, 783, 799, 897], "reserv": [5, 6, 7, 8, 9, 898], "instanc": [5, 7, 8, 9, 13, 16, 19, 28, 31, 34, 43, 46, 49, 58, 61, 64, 73, 76, 79, 87, 89, 95, 96, 115, 125, 139, 149, 159, 169, 179, 189, 199, 209, 219, 229, 272, 273, 354, 366, 385, 395, 402, 411, 436, 441, 443, 444, 461, 478, 496, 513, 529, 544, 561, 578, 595, 613, 630, 660, 678, 694, 709, 739, 783, 799, 812, 815, 822, 840, 843, 857, 879, 898], "optim": [5, 8, 9, 10, 95, 100, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 117, 120, 121, 122, 123, 127, 130, 131, 136, 137, 141, 144, 145, 146, 147, 151, 154, 155, 156, 157, 161, 164, 165, 166, 167, 171, 174, 175, 176, 177, 181, 184, 185, 186, 187, 191, 194, 195, 196, 197, 201, 204, 205, 206, 207, 211, 214, 215, 216, 217, 221, 224, 225, 226, 227, 231, 234, 235, 268, 271, 277, 354, 368, 402, 450, 490, 492, 494, 507, 509, 511, 523, 525, 527, 642, 644, 654, 672, 674, 676, 688, 690, 692, 721, 723, 733, 751, 753, 763, 764, 766, 776, 777, 779, 781, 793, 795, 797, 837, 846, 859, 861, 871, 891, 896, 897, 898, 899], "exp": [5, 8, 10], "access": [5, 6, 7, 8, 9, 248, 259, 421, 898], "wai": [5, 6, 7, 8, 9, 898], "retain": [5, 6, 7, 8, 9], "wa": [5, 6, 7, 8, 9, 91, 94, 95, 96, 97, 421, 447, 450, 496, 513, 529, 678, 694, 783, 799, 895], "avail": [5, 6, 7, 8, 9, 259, 262, 265, 421, 424, 452, 461, 478, 496, 513, 529, 544, 561, 578, 595, 613, 630, 660, 678, 694, 709, 739, 783, 799, 812, 840, 879, 897, 898, 899], "syntax": [5, 6, 7, 8, 9, 496, 513, 529, 678, 694, 783, 799, 812, 840], "z": [5, 6, 7, 8, 9, 87, 96, 100, 104, 105, 106, 107, 108, 109, 110, 111, 263, 277, 368, 421, 650, 729, 759, 772, 867], "bs_x": [5, 6, 7, 8, 9], "bs_y": [5, 6, 7, 8, 9], "bs_z": [5, 6, 7, 8, 9], "1234": [5, 6, 7, 8, 9, 438], "properti": [5, 6, 7, 8, 9, 16, 18, 19, 23, 31, 33, 34, 38, 46, 48, 49, 53, 61, 63, 64, 68, 76, 78, 79, 83, 91, 96, 97, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 238, 239, 240, 242, 243, 244, 250, 257, 262, 263, 264, 265, 266, 267, 270, 272, 273, 275, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 312, 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 324, 325, 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 351, 352, 353, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 365, 367, 369, 370, 371, 372, 373, 374, 375, 376, 377, 378, 379, 380, 381, 382, 383, 384, 386, 387, 388, 389, 390, 391, 392, 393, 394, 396, 397, 398, 399, 401, 403, 404, 405, 406, 407, 408, 409, 410, 412, 413, 414, 416, 420, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446, 447, 448, 449, 450, 451, 453, 457, 460, 462, 463, 466, 468, 470, 472, 479, 480, 483, 485, 487, 489, 493, 496, 497, 498, 499, 504, 505, 506, 510, 513, 514, 515, 520, 521, 522, 526, 529, 530, 531, 536, 537, 538, 545, 546, 549, 551, 553, 555, 562, 563, 566, 568, 570, 572, 579, 580, 583, 585, 587, 589, 596, 597, 600, 602, 603, 605, 607, 614, 615, 618, 620, 622, 624, 631, 632, 635, 637, 639, 641, 645, 648, 652, 661, 662, 665, 667, 669, 671, 675, 678, 679, 680, 685, 686, 687, 691, 694, 695, 696, 701, 702, 703, 710, 711, 714, 716, 718, 720, 724, 727, 731, 740, 741, 744, 746, 748, 750, 754, 757, 761, 767, 770, 774, 780, 783, 784, 785, 790, 791, 792, 796, 799, 800, 801, 806, 807, 808, 810, 811, 812, 814, 815, 816, 817, 818, 820, 821, 823, 824, 825, 826, 827, 828, 829, 830, 831, 832, 834, 835, 837, 838, 839, 840, 842, 843, 844, 845, 846, 847, 848, 850, 851, 852, 853, 854, 855, 856, 858, 862, 865, 869, 880, 881, 884, 886, 888, 890, 892, 893, 898, 900], "uniform": 6, "integ": [6, 94, 96, 100, 104, 105, 106, 107, 108, 109, 110, 111, 116, 126, 140, 150, 160, 170, 180, 190, 200, 210, 220, 230, 238, 239, 240, 246, 252, 256, 259, 277, 354, 368, 402, 421, 424, 450, 465, 482, 496, 513, 529, 548, 565, 582, 599, 617, 634, 664, 678, 694, 713, 743, 783, 799, 812, 813, 840, 841, 883, 891], "equival": [6, 259, 368, 427, 438, 496, 513, 529, 678, 694, 783, 799, 812, 840, 891, 899], "structur": [7, 89, 267, 417, 420, 421, 424, 427, 428, 438, 895], "unequ": 7, "cross": [7, 273, 276, 277, 368, 420], "section": [7, 273, 276, 277, 368, 420], "relat": [7, 95, 96, 258, 433, 435, 891, 898], "inappropri": 7, "without": [8, 14, 29, 44, 59, 74, 89, 427, 428, 429, 438, 439, 445, 891], "circularblockbootstrap": [8, 9, 10, 58, 73, 90], "expon": 9, "arraylike1d": [10, 461], "arraylike2d": 10, "dimension": [10, 15, 25, 30, 40, 45, 55, 60, 70, 75, 85, 89, 895, 896], "oper": [10, 433], "b_sb": 10, "b_cb": 10, "algorithm": [10, 262, 267, 901], "its": [10, 20, 35, 50, 65, 80, 251, 258, 259, 368, 420, 421, 898], "correct": [10, 14, 29, 44, 59, 74, 259, 495, 512, 528, 677, 693, 782, 798], "tune": 10, "k_n": 10, "consecut": 10, "insid": [10, 895], "conserv": 10, "band": 10, "pm": 10, "log_": 10, "maximum": [10, 262, 263, 267, 271, 276, 281, 294, 392, 424, 440, 891, 895, 896, 898, 899], "lceil": 10, "rceil": 10, "_i": 10, "2g": 10, "d_": [10, 269, 274], "sum_": [10, 269, 271, 274, 277, 398, 424, 425, 426, 427, 428, 431, 433, 435, 437, 438, 440], "k": [10, 238, 239, 240, 259, 264, 265, 267, 271, 354, 368, 402, 425, 427, 428, 431, 435, 438, 439, 502, 518, 683, 699, 899], "h": [10, 95, 100, 104, 105, 106, 107, 108, 109, 110, 111, 237, 239, 262, 263, 264, 269, 271, 274, 424, 496, 513, 529, 678, 694, 783, 799, 812, 840, 895, 897, 899], "gamma_": [10, 425, 431, 435], "c_": 10, "x_k": 10, "bar": [10, 421, 438], "x_": [10, 95, 271, 895], "remain": [10, 895], "constant": [10, 95, 117, 120, 127, 130, 141, 144, 151, 154, 161, 164, 171, 174, 181, 184, 191, 194, 201, 204, 211, 214, 221, 224, 231, 234, 262, 263, 264, 265, 267, 269, 271, 274, 276, 277, 417, 420, 421, 424, 427, 428, 429, 430, 438, 439, 496, 502, 513, 518, 529, 534, 678, 683, 694, 699, 755, 783, 788, 799, 813, 841, 891, 896, 899], "c_i": 10, "taken": 10, "andrew": [10, 95, 108, 264, 266, 267, 268, 419], "patton": 10, "matlab": 10, "program": 10, "do": [10, 12, 27, 42, 57, 72, 269, 274, 424, 438, 496, 513, 529, 678, 694, 783, 799, 812, 813, 840, 841, 872, 897, 900], "autocovari": [10, 277], "rather": [10, 96, 258, 259, 263, 267, 424, 897, 898], "dimitri": 10, "halbert": 10, "white": [10, 237, 239, 258, 277, 354, 368, 402, 421, 896, 898], "2004": [10, 264, 267], "automat": [10, 91, 95, 96, 97, 262, 263, 264, 265, 267, 268, 276, 281, 294, 417, 419, 421, 427, 428, 429, 438, 439, 445, 891], "select": [10, 95, 96, 121, 131, 145, 155, 165, 175, 185, 195, 205, 215, 225, 235, 240, 262, 263, 264, 267, 268, 271, 273, 276, 281, 294, 392, 417, 419, 421, 466, 468, 483, 485, 549, 551, 566, 568, 583, 585, 600, 602, 618, 620, 635, 637, 665, 667, 714, 716, 744, 746, 884, 886], "review": [10, 264, 443], "23": [10, 237, 239, 421, 896], "53": [10, 420, 896], "70": [10, 259], "doi": [10, 236], "1081": 10, "etc": [10, 424, 898], "120028836": 10, "2009": [10, 420, 424, 897], "28": [10, 421, 424, 891, 895, 896, 897, 898], "372": 10, "375": 10, "1080": 10, "07474930802459016": 10, "func": [11, 14, 15, 25, 26, 29, 30, 40, 41, 44, 45, 55, 56, 59, 60, 70, 71, 74, 75, 85, 891, 896, 897, 898], "callabl": [11, 14, 15, 25, 26, 29, 30, 40, 41, 44, 45, 55, 56, 59, 60, 70, 71, 74, 75, 85, 95, 258, 452, 461, 465, 478, 482, 496, 513, 529, 544, 548, 561, 565, 578, 582, 595, 599, 613, 617, 630, 634, 653, 660, 664, 678, 694, 709, 713, 732, 739, 743, 762, 775, 783, 799, 812, 840, 870, 879, 883], "ndarrai": [11, 12, 14, 15, 17, 18, 21, 22, 23, 24, 25, 26, 27, 29, 30, 32, 33, 36, 37, 38, 39, 40, 41, 42, 44, 45, 47, 48, 51, 52, 53, 54, 55, 56, 57, 59, 60, 62, 63, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 77, 78, 81, 82, 83, 84, 85, 95, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 118, 128, 134, 135, 142, 152, 162, 172, 182, 192, 202, 212, 222, 232, 238, 239, 240, 246, 247, 252, 253, 256, 262, 263, 264, 265, 266, 267, 268, 269, 271, 274, 276, 277, 290, 303, 314, 327, 342, 353, 427, 428, 429, 434, 438, 439, 445, 446, 447, 450, 454, 455, 456, 458, 459, 465, 467, 469, 471, 473, 474, 475, 476, 477, 478, 482, 484, 486, 488, 491, 492, 494, 495, 496, 501, 502, 503, 505, 506, 508, 509, 511, 512, 513, 517, 518, 519, 521, 522, 524, 525, 527, 528, 529, 533, 534, 535, 537, 538, 539, 540, 541, 542, 543, 544, 548, 550, 552, 554, 556, 557, 558, 559, 560, 561, 565, 567, 569, 571, 573, 574, 575, 576, 577, 578, 582, 584, 586, 588, 590, 591, 592, 593, 594, 595, 599, 601, 604, 606, 608, 609, 610, 611, 612, 613, 617, 619, 621, 623, 625, 626, 627, 628, 629, 630, 634, 636, 638, 640, 642, 643, 644, 646, 647, 650, 651, 653, 654, 655, 656, 657, 658, 659, 660, 664, 666, 668, 670, 673, 674, 676, 677, 678, 682, 683, 684, 686, 687, 689, 690, 692, 693, 694, 698, 699, 700, 702, 703, 704, 705, 706, 707, 708, 709, 713, 715, 717, 719, 721, 722, 723, 725, 726, 729, 730, 732, 733, 734, 735, 736, 737, 738, 739, 743, 745, 747, 749, 751, 752, 753, 755, 756, 759, 760, 762, 763, 764, 765, 766, 768, 769, 772, 773, 775, 776, 778, 779, 781, 782, 783, 787, 788, 789, 791, 792, 794, 795, 797, 798, 799, 803, 804, 805, 807, 811, 812, 813, 820, 821, 828, 829, 830, 831, 835, 840, 841, 851, 856, 859, 860, 861, 863, 864, 867, 868, 870, 871, 872, 873, 874, 875, 876, 877, 878, 879, 883, 885, 887, 889, 891, 895], "rep": [11, 12, 14, 15, 25, 26, 27, 29, 30, 40, 41, 42, 44, 45, 55, 56, 57, 59, 60, 70, 71, 72, 74, 75, 85, 238, 239, 240, 259], "str": [11, 12, 14, 15, 17, 18, 22, 23, 25, 26, 27, 29, 30, 32, 33, 37, 38, 39, 40, 41, 42, 44, 45, 47, 48, 52, 53, 54, 55, 56, 57, 59, 60, 62, 63, 67, 68, 70, 71, 72, 74, 75, 77, 78, 82, 83, 85, 101, 119, 129, 143, 153, 163, 173, 183, 193, 203, 213, 223, 233, 259, 270, 272, 273, 275, 277, 278, 279, 283, 288, 289, 291, 292, 296, 302, 304, 305, 308, 312, 313, 315, 316, 319, 324, 325, 326, 328, 329, 333, 339, 340, 343, 344, 347, 351, 352, 354, 357, 368, 371, 374, 387, 393, 394, 395, 401, 402, 403, 408, 409, 410, 411, 416, 447, 450, 462, 464, 479, 481, 494, 495, 496, 498, 500, 511, 512, 513, 514, 516, 527, 528, 529, 530, 532, 545, 547, 562, 564, 579, 581, 596, 598, 614, 616, 631, 633, 648, 649, 661, 663, 676, 677, 678, 679, 681, 692, 693, 694, 695, 697, 710, 712, 727, 728, 740, 742, 757, 758, 770, 771, 781, 782, 783, 784, 786, 797, 798, 799, 800, 802, 812, 813, 819, 840, 841, 849, 865, 866, 880, 882, 900], "extra": [11, 14, 15, 25, 26, 29, 30, 40, 41, 44, 45, 55, 56, 59, 60, 70, 71, 74, 75, 85, 239, 240], "conflict": [11, 26, 41, 56, 71], "nparam": [11, 26, 41, 56, 71], "row": [11, 14, 26, 29, 41, 44, 56, 59, 71, 74, 424, 496, 513, 529, 678, 694, 783, 799, 812, 836, 840, 895], "correspond": [11, 14, 26, 29, 41, 44, 56, 59, 71, 74, 239, 250, 259, 471, 488, 494, 496, 511, 513, 527, 529, 554, 571, 588, 606, 623, 640, 670, 676, 678, 692, 694, 719, 749, 781, 783, 797, 799, 812, 840, 889, 897, 898], "iter": [11, 12, 24, 26, 27, 39, 41, 42, 54, 56, 57, 69, 71, 72, 84, 92, 452, 494, 511, 527, 676, 692, 781, 797, 891, 896, 897, 898], "up": [11, 14, 26, 29, 41, 44, 56, 59, 71, 74, 267, 421, 424, 437, 496, 513, 529, 678, 694, 783, 799, 812, 840, 895, 897, 899], "append": [11, 14, 26, 29, 41, 44, 56, 59, 71, 74, 259, 895, 897], "befor": [11, 14, 26, 29, 41, 44, 56, 59, 71, 74, 100, 104, 105, 106, 107, 108, 109, 110, 111, 248, 263, 421, 494, 511, 527, 676, 692, 781, 797, 854, 899], "randn": [11, 12, 14, 15, 25, 26, 27, 29, 30, 40, 41, 42, 44, 45, 55, 56, 57, 59, 60, 70, 71, 72, 74, 75, 85, 87, 89, 259, 428, 429, 438, 439, 445, 502, 518, 683, 699], "over": [12, 27, 42, 57, 72, 94, 899], "calcul": [12, 27, 42, 57, 72, 95, 113, 117, 118, 123, 127, 128, 137, 141, 142, 147, 151, 152, 157, 161, 162, 167, 171, 172, 177, 181, 182, 187, 191, 192, 197, 201, 202, 207, 211, 212, 217, 221, 222, 227, 231, 232, 264, 267, 646, 647, 725, 726, 755, 756, 768, 769, 864], "kei": [12, 27, 42, 57, 72, 389, 406, 417, 496, 513, 529, 678, 694, 783, 799, 812, 840, 895], "problem": [12, 27, 42, 57, 72, 89, 96, 421], "doe": [12, 27, 42, 57, 72, 90, 259, 277, 362, 381, 421, 471, 488, 494, 511, 527, 554, 571, 588, 606, 623, 640, 670, 676, 692, 719, 749, 781, 797, 873, 889, 891, 897], "output": [12, 27, 42, 57, 72, 88, 259, 494, 511, 527, 653, 676, 692, 732, 762, 775, 781, 797, 870, 891, 897, 898], "arang": [12, 27, 42, 57, 72, 89, 895, 897, 899], "posdata": [12, 27, 42, 57, 72], "kwdata": [12, 27, 42, 57, 72], "someth": [12, 27, 42, 57, 72], "prng": [13, 16, 28, 31, 43, 46, 58, 61, 73, 76], "norm": [14, 29, 44, 59, 74], "bc": [14, 29, 44, 59, 74, 755], "bca": [14, 29, 44, 59, 74, 95], "float": [14, 15, 17, 22, 23, 25, 29, 30, 32, 37, 38, 40, 44, 45, 47, 52, 53, 55, 59, 60, 62, 67, 68, 70, 74, 75, 77, 82, 83, 85, 99, 100, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 117, 120, 121, 122, 123, 127, 130, 131, 136, 137, 141, 144, 145, 146, 147, 151, 154, 155, 156, 157, 161, 164, 165, 166, 167, 171, 174, 175, 176, 177, 181, 184, 185, 186, 187, 191, 194, 195, 196, 197, 201, 204, 205, 206, 207, 211, 214, 215, 216, 217, 221, 224, 225, 226, 227, 231, 234, 235, 238, 240, 247, 249, 267, 268, 270, 273, 275, 279, 284, 286, 292, 297, 299, 305, 309, 310, 316, 320, 322, 329, 335, 337, 341, 344, 348, 349, 354, 355, 358, 362, 363, 364, 369, 377, 381, 382, 383, 396, 398, 399, 402, 404, 412, 414, 425, 427, 432, 433, 435, 442, 447, 450, 454, 455, 456, 458, 460, 461, 465, 469, 471, 473, 474, 475, 476, 478, 482, 486, 488, 490, 491, 494, 495, 497, 502, 507, 508, 511, 512, 518, 523, 524, 527, 528, 534, 539, 540, 541, 542, 544, 548, 552, 554, 556, 557, 558, 559, 561, 565, 569, 571, 573, 574, 575, 576, 578, 582, 586, 588, 590, 591, 592, 593, 595, 599, 604, 606, 608, 609, 610, 611, 613, 617, 621, 623, 625, 626, 627, 628, 630, 634, 638, 640, 642, 643, 646, 647, 650, 651, 653, 655, 656, 657, 658, 660, 664, 668, 670, 672, 673, 676, 677, 683, 688, 689, 692, 693, 699, 704, 705, 706, 707, 709, 713, 717, 719, 721, 722, 725, 726, 729, 730, 732, 734, 735, 736, 737, 739, 743, 747, 749, 751, 752, 755, 756, 759, 760, 762, 764, 765, 768, 769, 772, 773, 775, 777, 778, 781, 782, 788, 793, 794, 797, 798, 804, 808, 810, 814, 819, 832, 834, 836, 842, 849, 852, 853, 854, 859, 860, 863, 864, 867, 868, 870, 872, 874, 875, 876, 877, 879, 883, 887, 889, 891, 895, 900], "tail": [14, 29, 44, 59, 74, 424, 443, 654, 895, 898], "bool": [14, 15, 20, 25, 29, 30, 35, 40, 44, 45, 50, 55, 59, 60, 65, 70, 74, 75, 80, 85, 99, 100, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 114, 116, 124, 126, 138, 140, 148, 150, 158, 160, 168, 170, 178, 180, 188, 190, 198, 200, 208, 210, 218, 220, 228, 230, 239, 240, 262, 263, 266, 270, 271, 272, 277, 330, 334, 336, 354, 368, 385, 402, 425, 427, 428, 429, 434, 438, 439, 440, 445, 446, 447, 448, 450, 457, 470, 487, 491, 494, 496, 508, 511, 513, 524, 527, 529, 553, 570, 587, 605, 622, 639, 646, 669, 673, 676, 678, 689, 692, 694, 718, 725, 748, 755, 768, 778, 781, 783, 794, 797, 799, 809, 812, 833, 840, 863, 888, 891], "fals": [14, 15, 20, 25, 29, 30, 35, 40, 44, 45, 50, 55, 59, 60, 65, 70, 74, 75, 80, 85, 88, 91, 95, 97, 99, 100, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 239, 240, 266, 271, 277, 354, 368, 385, 402, 421, 425, 427, 428, 429, 434, 438, 439, 440, 445, 448, 491, 494, 508, 511, 513, 524, 527, 529, 646, 673, 676, 678, 689, 692, 694, 725, 755, 768, 778, 781, 783, 794, 797, 799, 809, 812, 833, 840, 863, 891, 895, 897, 899], "nonparametr": [14, 29, 44, 59, 74, 89], "semi": [14, 29, 44, 59, 74, 89], "studentize_rep": [14, 29, 44, 59, 74], "One": [14, 29, 44, 59, 74, 421, 424], "debias": [14, 29, 44, 59, 74, 266, 277], "bia": [14, 29, 44, 59, 74, 274, 420], "coverag": [14, 29, 44, 59, 74], "flag": [14, 20, 29, 35, 44, 50, 59, 65, 74, 80, 100, 104, 105, 106, 107, 108, 109, 110, 111, 114, 116, 124, 126, 138, 140, 148, 150, 158, 160, 168, 170, 178, 180, 188, 190, 198, 200, 208, 210, 218, 220, 228, 230, 239, 240, 262, 271, 385, 427, 428, 429, 434, 438, 439, 440, 445, 470, 487, 491, 494, 508, 511, 524, 527, 553, 570, 587, 605, 622, 639, 646, 669, 673, 676, 689, 692, 718, 725, 748, 755, 768, 778, 781, 794, 797, 809, 833, 837, 863, 888, 891], "indic": [14, 20, 24, 29, 35, 39, 44, 48, 50, 54, 59, 65, 69, 74, 80, 84, 100, 104, 105, 106, 107, 108, 109, 110, 111, 114, 116, 124, 126, 138, 140, 148, 150, 158, 160, 168, 170, 178, 180, 188, 190, 198, 200, 208, 210, 218, 220, 228, 230, 239, 240, 242, 243, 247, 253, 257, 259, 262, 266, 271, 330, 334, 336, 385, 420, 421, 424, 427, 428, 429, 438, 439, 440, 445, 470, 487, 491, 494, 496, 502, 508, 511, 513, 518, 524, 527, 529, 553, 570, 587, 605, 622, 639, 646, 669, 673, 676, 678, 683, 689, 692, 694, 699, 718, 725, 748, 755, 768, 778, 781, 783, 794, 797, 799, 809, 812, 813, 819, 833, 840, 841, 849, 863, 888, 891, 898], "whether": [14, 15, 20, 25, 29, 30, 35, 40, 44, 45, 50, 55, 59, 60, 65, 70, 74, 75, 80, 85, 100, 104, 105, 106, 107, 108, 109, 110, 111, 114, 116, 124, 126, 138, 140, 148, 150, 158, 160, 168, 170, 178, 180, 188, 190, 198, 200, 208, 210, 218, 220, 228, 230, 258, 259, 262, 265, 266, 272, 277, 354, 368, 385, 402, 417, 419, 421, 427, 428, 429, 434, 438, 439, 440, 445, 447, 450, 491, 494, 496, 508, 511, 513, 524, 527, 529, 646, 673, 676, 678, 689, 692, 694, 725, 755, 768, 778, 781, 783, 794, 797, 799, 812, 840, 863, 891, 898], "previous": [14, 29, 44, 59, 74, 94, 896, 898, 899], "rerun": [14, 29, 44, 59, 74, 420], "ignor": [14, 29, 44, 59, 74, 421, 819, 849, 891, 895], "chang": [14, 29, 44, 59, 74, 93, 94, 95, 97, 259, 277, 420, 421, 452, 496, 513, 529, 678, 694, 783, 799, 812, 840, 872, 896, 897, 898, 899], "about": [14, 29, 44, 59, 74, 496, 513, 529, 678, 694, 783, 799, 846, 897, 898], "elimin": [14, 29, 44, 59, 74, 238], "need": [14, 29, 44, 59, 74, 89, 277, 420, 895, 897, 899], "bound": [14, 29, 44, 59, 74, 239, 250, 458, 461, 469, 471, 476, 478, 486, 488, 492, 509, 525, 542, 544, 552, 554, 559, 561, 569, 571, 576, 578, 586, 588, 593, 595, 604, 606, 611, 613, 621, 623, 628, 630, 638, 640, 650, 658, 660, 668, 670, 674, 690, 707, 709, 717, 719, 729, 737, 739, 747, 749, 759, 772, 779, 795, 867, 873, 877, 879, 887, 889], "inf": [14, 29, 44, 59, 74, 650, 729, 759, 772, 867], "similarli": [14, 29, 44, 59, 74], "between": [14, 29, 44, 59, 74, 95, 258, 259, 417, 421, 427, 428, 438, 763, 776, 813, 841, 898, 899], "direct": [14, 29, 44, 59, 74, 440, 898], "normal": [14, 29, 44, 59, 74, 89, 96, 259, 424, 440, 654, 891, 895, 896, 897, 898, 899], "approxim": [14, 29, 44, 59, 74, 262, 265, 267, 271, 454, 455, 456, 467, 471, 473, 474, 475, 484, 488, 539, 540, 541, 550, 554, 556, 557, 558, 567, 571, 573, 574, 575, 584, 588, 590, 591, 592, 601, 603, 606, 608, 609, 610, 619, 623, 625, 626, 627, 636, 640, 655, 656, 657, 666, 670, 704, 705, 706, 715, 719, 734, 735, 736, 745, 749, 874, 875, 876, 885, 889], "scale": [14, 29, 44, 59, 74, 95, 259, 427, 428, 429, 434, 438, 439, 442, 445, 455, 474, 540, 557, 574, 591, 609, 626, 656, 705, 735, 819, 849, 875, 891, 896, 898, 899], "acceler": [14, 29, 44, 59, 74], "ad": [14, 29, 44, 59, 74, 91, 92, 93, 94, 95, 96, 97, 898, 901], "recent": [15, 25, 30, 40, 45, 55, 60, 70, 75, 85, 88, 236, 239, 277], "interest": [15, 25, 30, 40, 45, 55, 60, 70, 75, 85, 898], "center": [15, 25, 30, 40, 45, 55, 60, 70, 75, 85, 88, 95, 99, 100, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 239, 259, 265, 898], "signatur": [15, 25, 30, 40, 45, 55, 60, 70, 75, 85], "axi": [15, 25, 30, 40, 45, 55, 60, 70, 75, 85, 496, 513, 529, 678, 694, 783, 799, 812, 840, 898, 899], "addit": [15, 25, 30, 40, 45, 55, 60, 70, 75, 85, 236, 265, 421, 465, 482, 548, 565, 582, 599, 617, 634, 664, 713, 743, 883, 897, 898], "elif": [15, 25, 30, 40, 45, 55, 60, 70, 75, 85, 897], "get": [16, 17, 19, 23, 31, 32, 34, 38, 46, 47, 49, 53, 61, 62, 64, 68, 76, 77, 79, 83, 280, 281, 288, 293, 294, 301, 306, 312, 317, 324, 325, 330, 331, 334, 336, 339, 345, 351, 393, 409, 417, 472, 489, 493, 504, 505, 510, 520, 521, 526, 536, 537, 555, 572, 589, 607, 624, 641, 671, 675, 685, 686, 691, 701, 702, 720, 750, 780, 790, 791, 796, 806, 890], "option": [16, 31, 46, 61, 76, 89, 95, 238, 239, 240, 259, 262, 277, 421, 428, 438, 446, 494, 496, 511, 513, 527, 529, 676, 678, 692, 694, 781, 783, 797, 799, 891, 892, 898, 901], "map": [17, 23, 32, 38, 47, 53, 62, 68, 77, 83], "current": [18, 33, 48, 63, 78, 95, 265, 873, 891, 896, 897, 898], "use_se": [20, 35, 50, 65, 80], "last": [20, 35, 50, 65, 80, 266, 269, 274, 424, 450, 494, 495, 511, 512, 527, 528, 676, 677, 692, 693, 781, 782, 797, 798, 895], "rese": [21, 36, 51, 66, 81], "otherwis": [23, 38, 53, 68, 83, 854], "updat": [24, 39, 54, 69, 84, 91, 94, 95, 97, 262, 265, 427, 452, 472, 489, 494, 511, 527, 555, 572, 589, 607, 624, 641, 671, 676, 692, 720, 750, 781, 797, 872, 890, 892], "overridden": [24, 39, 54, 69, 84, 421, 446], "new": [24, 39, 54, 69, 84, 95, 262, 427, 428, 429, 438, 439, 445, 496, 513, 529, 678, 694, 783, 799, 812, 840, 891], "least": [86, 266, 269, 274, 368, 421, 439], "total": 86, "aris": 86, "experiment": 86, 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See Notes for requirements"], [11, 2, 2, 0, "reps", "Number of bootstrap replications"]], "arch.bootstrap.CircularBlockBootstrap.bootstrap": [[12, 2, 2, 0, "reps", "Number of bootstrap replications"]], "arch.bootstrap.CircularBlockBootstrap.clone": [[13, 2, 2, 0, "args", "Positional arguments to bootstrap"], [13, 2, 2, 0, "kwargs", "Keyword arguments to bootstrap"], [13, 2, 2, 0, "seed", "The seed value to pass to the closed generator"]], "arch.bootstrap.CircularBlockBootstrap.conf_int": [[14, 2, 2, 0, "extra_kwargs", "Extra keyword arguments to use when calling func and std_err_func, when appropriate"], [14, 2, 2, 0, "func", "Function the computes parameter values.  See Notes for requirements"], [14, 2, 2, 0, "method", "One of 'basic', 'percentile', 'studentized', 'norm' (identical to 'var', 'cov'), 'bc' (identical to 'debiased', 'bias-corrected'), or 'bca'"], [14, 2, 2, 0, "reps", "Number of bootstrap replications"], [14, 2, 2, 0, "reuse", "Flag indicating whether to reuse previously computed bootstrap results.  This allows alternative methods to be compared without rerunning the bootstrap simulation.  Reuse is ignored if reps is not the same across multiple runs, func changes across calls, or method is 'studentized'."], [14, 2, 2, 0, "sampling", "Type of sampling to use: 'nonparametric', 'semi-parametric' (or 'semi') or 'parametric'.  The default is 'nonparametric'.  See notes about the changes to func required when using 'semi' or 'parametric'."], [14, 2, 2, 0, "size", "Coverage of confidence interval"], [14, 2, 2, 0, "std_err_func", "Function to use when standardizing estimated parameters when using the studentized bootstrap.  Providing an analytical function eliminates the need for a nested bootstrap"], [14, 2, 2, 0, "studentize_reps", "Number of bootstraps to use in the inner bootstrap when using the studentized bootstrap.  Ignored when std_err_func is provided"], [14, 2, 2, 0, "tail", "One of 'two', 'upper' or 'lower'."]], "arch.bootstrap.CircularBlockBootstrap.cov": [[15, 2, 2, 0, "extra_kwargs", "Dictionary of extra keyword arguments to pass to func"], [15, 2, 2, 0, "func", "Callable function that returns the statistic of interest as a 1-d array"], [15, 2, 2, 0, "recenter", "Whether to center the bootstrap variance estimator on the average of the bootstrap samples (True) or to center on the original sample estimate (False).  Default is True."], [15, 2, 2, 0, "reps", "Number of bootstrap replications"]], "arch.bootstrap.CircularBlockBootstrap.reset": [[20, 2, 2, 0, "use_seed", "Flag indicating whether to use the last seed if provided.  If False or if no seed has been set, the bootstrap will be reset to the initial state.  Default is True"]], "arch.bootstrap.CircularBlockBootstrap.seed": [[21, 2, 2, 0, "value", "Value to use as the seed."]], "arch.bootstrap.CircularBlockBootstrap.set_state": [[22, 2, 2, 0, "state", "Dictionary or tuple containing the state."]], "arch.bootstrap.CircularBlockBootstrap.var": [[25, 2, 2, 0, "extra_kwargs", "Dictionary of extra keyword arguments to pass to func"], [25, 2, 2, 0, "func", "Callable function that returns the statistic of interest as a 1-d array"], [25, 2, 2, 0, "recenter", "Whether to center the bootstrap variance estimator on the average of the bootstrap samples (True) or to center on the original sample estimate (False).  Default is True."], [25, 2, 2, 0, "reps", "Number of bootstrap replications"]], "arch.bootstrap.IIDBootstrap.__init__": [[6, 2, 2, 0, "args", "Positional arguments to bootstrap"], [6, 2, 2, 0, "kwargs", "Keyword arguments to bootstrap"], [6, 2, 2, 0, "random_state", "RandomState to use to ensure reproducable results. Cannot be used with seed"], [6, 2, 2, 0, "seed", "Seed to use to ensure reproducable results. If an int, passes the value to value to np.random.default_rng. If None, a fresh Generator is constructed with system-provided entropy."]], "arch.bootstrap.IIDBootstrap": [[26, 3, 1, 0, "apply", "Applies a function to bootstrap replicated data"], [27, 3, 1, 0, "bootstrap", "Iterator for use when bootstrapping"], [28, 3, 1, 0, "clone", "Clones the bootstrap using different data with a fresh prng."], [29, 3, 1, 0, "conf_int", "Function the computes parameter values.  See Notes for requirements"], [30, 3, 1, 0, "cov", "Compute parameter covariance using bootstrap"], [6, 4, 1, 0, "data", "Two-element tuple with the pos_data in the first position and kw_data in the second (pos_data, kw_data)"], [31, 5, 1, 0, "generator", "Set or get the instance PRNG"], [32, 3, 1, 0, "get_state", "Gets the state of the bootstrap's random number generator"], [33, 5, 1, 0, "index", "The current index of the bootstrap"], [6, 4, 1, 0, "kw_data", "Dictionary containing the keyword arguments"], [6, 4, 1, 0, "pos_data", "Tuple containing the positional arguments (in the order entered)"], [34, 5, 1, 0, "random_state", "Set or get the instance random state"], [35, 3, 1, 0, "reset", "Resets the bootstrap to either its initial state or the last seed."], [36, 3, 1, 0, "seed", "Reseeds the bootstrap's random number generator"], [37, 3, 1, 0, "set_state", "Sets the state of the bootstrap's random number generator"], [38, 5, 1, 0, "state", "Set or get the generator's state"], [39, 3, 1, 0, "update_indices", "Update indices for the next iteration of the bootstrap.  This must be overridden when creating new bootstraps."], [40, 3, 1, 0, "var", "Compute parameter variance using bootstrap"]], "arch.bootstrap.IIDBootstrap.apply": [[26, 2, 2, 0, "extra_kwargs", "Extra keyword arguments to use when calling func.  Must not conflict with keyword arguments used to initialize bootstrap"], [26, 2, 2, 0, "func", "Function the computes parameter values.  See Notes for requirements"], [26, 2, 2, 0, "reps", "Number of bootstrap replications"]], "arch.bootstrap.IIDBootstrap.bootstrap": [[27, 2, 2, 0, "reps", "Number of bootstrap replications"]], "arch.bootstrap.IIDBootstrap.clone": [[28, 2, 2, 0, "args", "Positional arguments to bootstrap"], [28, 2, 2, 0, "kwargs", "Keyword arguments to bootstrap"], [28, 2, 2, 0, "seed", "The seed value to pass to the closed generator"]], "arch.bootstrap.IIDBootstrap.conf_int": [[29, 2, 2, 0, "extra_kwargs", "Extra keyword arguments to use when calling func and std_err_func, when appropriate"], [29, 2, 2, 0, "func", "Function the computes parameter values.  See Notes for requirements"], [29, 2, 2, 0, "method", "One of 'basic', 'percentile', 'studentized', 'norm' (identical to 'var', 'cov'), 'bc' (identical to 'debiased', 'bias-corrected'), or 'bca'"], [29, 2, 2, 0, "reps", "Number of bootstrap replications"], [29, 2, 2, 0, "reuse", "Flag indicating whether to reuse previously computed bootstrap results.  This allows alternative methods to be compared without rerunning the bootstrap simulation.  Reuse is ignored if reps is not the same across multiple runs, func changes across calls, or method is 'studentized'."], [29, 2, 2, 0, "sampling", "Type of sampling to use: 'nonparametric', 'semi-parametric' (or 'semi') or 'parametric'.  The default is 'nonparametric'.  See notes about the changes to func required when using 'semi' or 'parametric'."], [29, 2, 2, 0, "size", "Coverage of confidence interval"], [29, 2, 2, 0, "std_err_func", "Function to use when standardizing estimated parameters when using the studentized bootstrap.  Providing an analytical function eliminates the need for a nested bootstrap"], [29, 2, 2, 0, "studentize_reps", "Number of bootstraps to use in the inner bootstrap when using the studentized bootstrap.  Ignored when std_err_func is provided"], [29, 2, 2, 0, "tail", "One of 'two', 'upper' or 'lower'."]], "arch.bootstrap.IIDBootstrap.cov": [[30, 2, 2, 0, "extra_kwargs", "Dictionary of extra keyword arguments to pass to func"], [30, 2, 2, 0, "func", "Callable function that returns the statistic of interest as a 1-d array"], [30, 2, 2, 0, "recenter", "Whether to center the bootstrap variance estimator on the average of the bootstrap samples (True) or to center on the original sample estimate (False).  Default is True."], [30, 2, 2, 0, "reps", "Number of bootstrap replications"]], "arch.bootstrap.IIDBootstrap.reset": [[35, 2, 2, 0, "use_seed", "Flag indicating whether to use the last seed if provided.  If False or if no seed has been set, the bootstrap will be reset to the initial state.  Default is True"]], "arch.bootstrap.IIDBootstrap.seed": [[36, 2, 2, 0, "value", "Value to use as the seed."]], "arch.bootstrap.IIDBootstrap.set_state": [[37, 2, 2, 0, "state", "Dictionary or tuple containing the state."]], "arch.bootstrap.IIDBootstrap.var": [[40, 2, 2, 0, "extra_kwargs", "Dictionary of extra keyword arguments to pass to func"], [40, 2, 2, 0, "func", "Callable function that returns the statistic of interest as a 1-d array"], [40, 2, 2, 0, "recenter", "Whether to center the bootstrap variance estimator on the average of the bootstrap samples (True) or to center on the original sample estimate (False).  Default is True."], [40, 2, 2, 0, "reps", "Number of bootstrap replications"]], "arch.bootstrap.IndependentSamplesBootstrap.__init__": [[7, 2, 2, 0, "args", "Positional arguments to bootstrap"], [7, 2, 2, 0, "kwargs", "Keyword arguments to bootstrap"]], "arch.bootstrap.IndependentSamplesBootstrap": [[41, 3, 1, 0, "apply", "Applies a function to bootstrap replicated data"], [42, 3, 1, 0, "bootstrap", "Iterator for use when bootstrapping"], [43, 3, 1, 0, "clone", "Clones the bootstrap using different data with a fresh prng."], [44, 3, 1, 0, "conf_int", "Function the computes parameter values.  See Notes for requirements"], [45, 3, 1, 0, "cov", "Compute parameter covariance using bootstrap"], [7, 4, 1, 0, "data", "Two-element tuple with the pos_data in the first position and kw_data in the second (pos_data, kw_data)"], [46, 5, 1, 0, "generator", "Set or get the instance PRNG"], [47, 3, 1, 0, "get_state", "Gets the state of the bootstrap's random number generator"], [48, 5, 1, 0, "index", "Returns the current index of the bootstrap"], [7, 4, 1, 0, "kw_data", "Dictionary containing the keyword arguments"], [7, 4, 1, 0, "pos_data", "Tuple containing the positional arguments (in the order entered)"], [49, 5, 1, 0, "random_state", "Set or get the instance random state"], [50, 3, 1, 0, "reset", "Resets the bootstrap to either its initial state or the last seed."], [51, 3, 1, 0, "seed", "Reseeds the bootstrap's random number generator"], [52, 3, 1, 0, "set_state", "Sets the state of the bootstrap's random number generator"], [53, 5, 1, 0, "state", "Set or get the generator's state"], [54, 3, 1, 0, "update_indices", "Update indices for the next iteration of the bootstrap.  This must be overridden when creating new bootstraps."], [55, 3, 1, 0, "var", "Compute parameter variance using bootstrap"]], "arch.bootstrap.IndependentSamplesBootstrap.apply": [[41, 2, 2, 0, "extra_kwargs", "Extra keyword arguments to use when calling func.  Must not conflict with keyword arguments used to initialize bootstrap"], [41, 2, 2, 0, "func", "Function the computes parameter values.  See Notes for requirements"], [41, 2, 2, 0, "reps", "Number of bootstrap replications"]], "arch.bootstrap.IndependentSamplesBootstrap.bootstrap": [[42, 2, 2, 0, "reps", "Number of bootstrap replications"]], "arch.bootstrap.IndependentSamplesBootstrap.clone": [[43, 2, 2, 0, "args", "Positional arguments to bootstrap"], [43, 2, 2, 0, "kwargs", "Keyword arguments to bootstrap"], [43, 2, 2, 0, "seed", "The seed value to pass to the closed generator"]], "arch.bootstrap.IndependentSamplesBootstrap.conf_int": [[44, 2, 2, 0, "extra_kwargs", "Extra keyword arguments to use when calling func and std_err_func, when appropriate"], [44, 2, 2, 0, "func", "Function the computes parameter values.  See Notes for requirements"], [44, 2, 2, 0, "method", "One of 'basic', 'percentile', 'studentized', 'norm' (identical to 'var', 'cov'), 'bc' (identical to 'debiased', 'bias-corrected'), or 'bca'"], [44, 2, 2, 0, "reps", "Number of bootstrap replications"], [44, 2, 2, 0, "reuse", "Flag indicating whether to reuse previously computed bootstrap results.  This allows alternative methods to be compared without rerunning the bootstrap simulation.  Reuse is ignored if reps is not the same across multiple runs, func changes across calls, or method is 'studentized'."], [44, 2, 2, 0, "sampling", "Type of sampling to use: 'nonparametric', 'semi-parametric' (or 'semi') or 'parametric'.  The default is 'nonparametric'.  See notes about the changes to func required when using 'semi' or 'parametric'."], [44, 2, 2, 0, "size", "Coverage of confidence interval"], [44, 2, 2, 0, "std_err_func", "Function to use when standardizing estimated parameters when using the studentized bootstrap.  Providing an analytical function eliminates the need for a nested bootstrap"], [44, 2, 2, 0, "studentize_reps", "Number of bootstraps to use in the inner bootstrap when using the studentized bootstrap.  Ignored when std_err_func is provided"], [44, 2, 2, 0, "tail", "One of 'two', 'upper' or 'lower'."]], "arch.bootstrap.IndependentSamplesBootstrap.cov": [[45, 2, 2, 0, "extra_kwargs", "Dictionary of extra keyword arguments to pass to func"], [45, 2, 2, 0, "func", "Callable function that returns the statistic of interest as a 1-d array"], [45, 2, 2, 0, "recenter", "Whether to center the bootstrap variance estimator on the average of the bootstrap samples (True) or to center on the original sample estimate (False).  Default is True."], [45, 2, 2, 0, "reps", "Number of bootstrap replications"]], "arch.bootstrap.IndependentSamplesBootstrap.reset": [[50, 2, 2, 0, "use_seed", "Flag indicating whether to use the last seed if provided.  If False or if no seed has been set, the bootstrap will be reset to the initial state.  Default is True"]], "arch.bootstrap.IndependentSamplesBootstrap.seed": [[51, 2, 2, 0, "value", "Value to use as the seed."]], "arch.bootstrap.IndependentSamplesBootstrap.set_state": [[52, 2, 2, 0, "state", "Dictionary or tuple containing the state."]], "arch.bootstrap.IndependentSamplesBootstrap.var": [[55, 2, 2, 0, "extra_kwargs", "Dictionary of extra keyword arguments to pass to func"], [55, 2, 2, 0, "func", "Callable function that returns the statistic of interest as a 1-d array"], [55, 2, 2, 0, "recenter", "Whether to center the bootstrap variance estimator on the average of the bootstrap samples (True) or to center on the original sample estimate (False).  Default is True."], [55, 2, 2, 0, "reps", "Number of bootstrap replications"]], "arch.bootstrap.MCS.__init__": [[238, 2, 2, 0, "block_size", "Length of window to use in the bootstrap.  If not provided, sqrt(T) is used.  In general, this should be provided and chosen to be appropriate for the data."], [238, 2, 2, 0, "bootstrap", "Bootstrap to use.  Options are 'stationary' or 'sb': Stationary bootstrap (Default) 'circular' or 'cbb': Circular block bootstrap 'moving block' or 'mbb': Moving block bootstrap"], [238, 2, 2, 0, "losses", "T by k array containing losses from a set of models"], [238, 2, 2, 0, "method", "MCS test and elimination implementation method, either 'max' or 'R'. Default is 'R'."], [238, 2, 2, 0, "reps", "Number of bootstrap replications to uses.  Default is 1000."], [238, 2, 2, 0, "seed", "Seed value to use when creating the bootstrap used in the comparison. If an integer or None, the NumPy default_rng is used with the seed value.  If a Generator or a RandomState, the argument is used."], [238, 2, 2, 0, "size", "Value in (0,1) to use as the test size when implementing the mcs. Default value is 0.05."]], "arch.bootstrap.MCS": [[241, 3, 1, 0, "compute", "Compute the set of models in the confidence set."], [242, 5, 1, 0, "excluded", "List of model indices that are excluded from the MCS"], [243, 5, 1, 0, "included", "List of model indices that are included in the MCS"], [244, 5, 1, 0, "pvalues", "Model p-values for inclusion in the MCS"], [245, 3, 1, 0, "reset", "Reset the bootstrap to it's initial state."], [246, 3, 1, 0, "seed", "Seed the bootstrap's random number generator"]], "arch.bootstrap.MCS.seed": [[246, 2, 2, 0, "value", "Integer to use as the seed"]], "arch.bootstrap.MovingBlockBootstrap.__init__": [[8, 2, 2, 0, "args", "Positional arguments to bootstrap"], [8, 2, 2, 0, "block_size", "Size of block to use"], [8, 2, 2, 0, "kwargs", "Keyword arguments to bootstrap"], [8, 2, 2, 0, "random_state", "RandomState to use to ensure reproducable results. Cannot be used with seed"], [8, 2, 2, 0, "seed", "Seed to use to ensure reproducable results. If an int, passes the value to value to np.random.default_rng. If None, a fresh Generator is constructed with system-provided entropy."]], "arch.bootstrap.MovingBlockBootstrap": [[56, 3, 1, 0, "apply", "Applies a function to bootstrap replicated data"], [57, 3, 1, 0, "bootstrap", "Iterator for use when bootstrapping"], [58, 3, 1, 0, "clone", "Clones the bootstrap using different data with a fresh prng."], [59, 3, 1, 0, "conf_int", "Function the computes parameter values.  See Notes for requirements"], [60, 3, 1, 0, "cov", "Compute parameter covariance using bootstrap"], [8, 4, 1, 0, "data", "Two-element tuple with the pos_data in the first position and kw_data in the second (pos_data, kw_data)"], [61, 5, 1, 0, "generator", "Set or get the instance PRNG"], [62, 3, 1, 0, "get_state", "Gets the state of the bootstrap's random number generator"], [63, 5, 1, 0, "index", "The current index of the bootstrap"], [8, 4, 1, 0, "kw_data", "Dictionary containing the keyword arguments"], [8, 4, 1, 0, "pos_data", "Tuple containing the positional arguments (in the order entered)"], [64, 5, 1, 0, "random_state", "Set or get the instance random state"], [65, 3, 1, 0, "reset", "Resets the bootstrap to either its initial state or the last seed."], [66, 3, 1, 0, "seed", "Reseeds the bootstrap's random number generator"], [67, 3, 1, 0, "set_state", "Sets the state of the bootstrap's random number generator"], [68, 5, 1, 0, "state", "Set or get the generator's state"], [69, 3, 1, 0, "update_indices", "Update indices for the next iteration of the bootstrap.  This must be overridden when creating new bootstraps."], [70, 3, 1, 0, "var", "Compute parameter variance using bootstrap"]], "arch.bootstrap.MovingBlockBootstrap.apply": [[56, 2, 2, 0, "extra_kwargs", "Extra keyword arguments to use when calling func.  Must not conflict with keyword arguments used to initialize bootstrap"], [56, 2, 2, 0, "func", "Function the computes parameter values.  See Notes for requirements"], [56, 2, 2, 0, "reps", "Number of bootstrap replications"]], "arch.bootstrap.MovingBlockBootstrap.bootstrap": [[57, 2, 2, 0, "reps", "Number of bootstrap replications"]], "arch.bootstrap.MovingBlockBootstrap.clone": [[58, 2, 2, 0, "args", "Positional arguments to bootstrap"], [58, 2, 2, 0, "kwargs", "Keyword arguments to bootstrap"], [58, 2, 2, 0, "seed", "The seed value to pass to the closed generator"]], "arch.bootstrap.MovingBlockBootstrap.conf_int": [[59, 2, 2, 0, "extra_kwargs", "Extra keyword arguments to use when calling func and std_err_func, when appropriate"], [59, 2, 2, 0, "func", "Function the computes parameter values.  See Notes for requirements"], [59, 2, 2, 0, "method", "One of 'basic', 'percentile', 'studentized', 'norm' (identical to 'var', 'cov'), 'bc' (identical to 'debiased', 'bias-corrected'), or 'bca'"], [59, 2, 2, 0, "reps", "Number of bootstrap replications"], [59, 2, 2, 0, "reuse", "Flag indicating whether to reuse previously computed bootstrap results.  This allows alternative methods to be compared without rerunning the bootstrap simulation.  Reuse is ignored if reps is not the same across multiple runs, func changes across calls, or method is 'studentized'."], [59, 2, 2, 0, "sampling", "Type of sampling to use: 'nonparametric', 'semi-parametric' (or 'semi') or 'parametric'.  The default is 'nonparametric'.  See notes about the changes to func required when using 'semi' or 'parametric'."], [59, 2, 2, 0, "size", "Coverage of confidence interval"], [59, 2, 2, 0, "std_err_func", "Function to use when standardizing estimated parameters when using the studentized bootstrap.  Providing an analytical function eliminates the need for a nested bootstrap"], [59, 2, 2, 0, "studentize_reps", "Number of bootstraps to use in the inner bootstrap when using the studentized bootstrap.  Ignored when std_err_func is provided"], [59, 2, 2, 0, "tail", "One of 'two', 'upper' or 'lower'."]], "arch.bootstrap.MovingBlockBootstrap.cov": [[60, 2, 2, 0, "extra_kwargs", "Dictionary of extra keyword arguments to pass to func"], [60, 2, 2, 0, "func", "Callable function that returns the statistic of interest as a 1-d array"], [60, 2, 2, 0, "recenter", "Whether to center the bootstrap variance estimator on the average of the bootstrap samples (True) or to center on the original sample estimate (False).  Default is True."], [60, 2, 2, 0, "reps", "Number of bootstrap replications"]], "arch.bootstrap.MovingBlockBootstrap.reset": [[65, 2, 2, 0, "use_seed", "Flag indicating whether to use the last seed if provided.  If False or if no seed has been set, the bootstrap will be reset to the initial state.  Default is True"]], "arch.bootstrap.MovingBlockBootstrap.seed": [[66, 2, 2, 0, "value", "Value to use as the seed."]], "arch.bootstrap.MovingBlockBootstrap.set_state": [[67, 2, 2, 0, "state", "Dictionary or tuple containing the state."]], "arch.bootstrap.MovingBlockBootstrap.var": [[70, 2, 2, 0, "extra_kwargs", "Dictionary of extra keyword arguments to pass to func"], [70, 2, 2, 0, "func", "Callable function that returns the statistic of interest as a 1-d array"], [70, 2, 2, 0, "recenter", "Whether to center the bootstrap variance estimator on the average of the bootstrap samples (True) or to center on the original sample estimate (False).  Default is True."], [70, 2, 2, 0, "reps", "Number of bootstrap replications"]], "arch.bootstrap.SPA.__init__": [[239, 2, 2, 0, "benchmark", "T element array of benchmark model losses"], [239, 2, 2, 0, "block_size", "Length of window to use in the bootstrap.  If not provided, sqrt(T) is used.  In general, this should be provided and chosen to be appropriate for the data."], [239, 2, 2, 0, "bootstrap", "Bootstrap to use.  Options are 'stationary' or 'sb': Stationary bootstrap (Default) 'circular' or 'cbb': Circular block bootstrap 'moving block' or 'mbb': Moving block bootstrap"], [239, 2, 2, 0, "models", "T  by k element array of alternative model losses"], [239, 2, 2, 0, "nested", "Flag indicating to use a nested bootstrap to compute variances for studentization.  Default is False.  Note that this can be slow since the procedure requires k extra bootstraps."], [239, 2, 2, 0, "reps", "Number of bootstrap replications to uses.  Default is 1000."], [239, 2, 2, 0, "seed", "Seed value to use when creating the bootstrap used in the comparison. If an integer or None, the NumPy default_rng is used with the seed value.  If a Generator or a RandomState, the argument is used."], [239, 2, 2, 0, "studentize", "Flag indicating to studentize loss differentials. Default is True"]], "arch.bootstrap.SPA": [[247, 3, 1, 0, "better_models", "Returns set of models rejected as being equal-or-worse than the benchmark"], [248, 3, 1, 0, "compute", "Compute the bootstrap pvalue."], [249, 3, 1, 0, "critical_values", "Returns data-dependent critical values"], [250, 5, 1, 0, "pvalues", "P-values corresponding to the lower, consistent and upper p-values."], [251, 3, 1, 0, "reset", "Reset the bootstrap to its initial state."], [252, 3, 1, 0, "seed", "Seed the bootstrap's random number generator"], [253, 3, 1, 0, "subset", "Sets a list of active models to run the SPA on.  Primarily for internal use."]], "arch.bootstrap.SPA.better_models": [[247, 2, 2, 0, "pvalue", "P-value in (0,1) to use when computing superior models"], [247, 2, 2, 0, "pvalue_type", "String in 'lower', 'consistent', or 'upper' indicating which critical value to use."]], "arch.bootstrap.SPA.critical_values": [[249, 2, 2, 0, "pvalue", "P-value in (0,1) to use when computing the critical values."]], "arch.bootstrap.SPA.seed": [[252, 2, 2, 0, "value", "Integer to use as the seed"]], "arch.bootstrap.SPA.subset": [[253, 2, 2, 0, "selector", "Boolean array indicating which columns to use when computing the p-values.  This is primarily for use by StepM."]], "arch.bootstrap.StationaryBootstrap.__init__": [[9, 2, 2, 0, "args", "Positional arguments to bootstrap"], [9, 2, 2, 0, "block_size", "Average size of block to use"], [9, 2, 2, 0, "kwargs", "Keyword arguments to bootstrap"], [9, 2, 2, 0, "random_state", "RandomState to use to ensure reproducable results. Cannot be used with seed"], [9, 2, 2, 0, "seed", "Seed to use to ensure reproducable results. If an int, passes the value to value to np.random.default_rng. If None, a fresh Generator is constructed with system-provided entropy."]], "arch.bootstrap.StationaryBootstrap": [[71, 3, 1, 0, "apply", "Applies a function to bootstrap replicated data"], [72, 3, 1, 0, "bootstrap", "Iterator for use when bootstrapping"], [73, 3, 1, 0, "clone", "Clones the bootstrap using different data with a fresh prng."], [74, 3, 1, 0, "conf_int", "Function the computes parameter values.  See Notes for requirements"], [75, 3, 1, 0, "cov", "Compute parameter covariance using bootstrap"], [9, 4, 1, 0, "data", "Two-element tuple with the pos_data in the first position and kw_data in the second (pos_data, kw_data)"], [76, 5, 1, 0, "generator", "Set or get the instance PRNG"], [77, 3, 1, 0, "get_state", "Gets the state of the bootstrap's random number generator"], [78, 5, 1, 0, "index", "The current index of the bootstrap"], [9, 4, 1, 0, "kw_data", "Dictionary containing the keyword arguments"], [9, 4, 1, 0, "pos_data", "Tuple containing the positional arguments (in the order entered)"], [79, 5, 1, 0, "random_state", "Set or get the instance random state"], [80, 3, 1, 0, "reset", "Resets the bootstrap to either its initial state or the last seed."], [81, 3, 1, 0, "seed", "Reseeds the bootstrap's random number generator"], [82, 3, 1, 0, "set_state", "Sets the state of the bootstrap's random number generator"], [83, 5, 1, 0, "state", "Set or get the generator's state"], [84, 3, 1, 0, "update_indices", "Update indices for the next iteration of the bootstrap.  This must be overridden when creating new bootstraps."], [85, 3, 1, 0, "var", "Compute parameter variance using bootstrap"]], "arch.bootstrap.StationaryBootstrap.apply": [[71, 2, 2, 0, "extra_kwargs", "Extra keyword arguments to use when calling func.  Must not conflict with keyword arguments used to initialize bootstrap"], [71, 2, 2, 0, "func", "Function the computes parameter values.  See Notes for requirements"], [71, 2, 2, 0, "reps", "Number of bootstrap replications"]], "arch.bootstrap.StationaryBootstrap.bootstrap": [[72, 2, 2, 0, "reps", "Number of bootstrap replications"]], "arch.bootstrap.StationaryBootstrap.clone": [[73, 2, 2, 0, "args", "Positional arguments to bootstrap"], [73, 2, 2, 0, "kwargs", "Keyword arguments to bootstrap"], [73, 2, 2, 0, "seed", "The seed value to pass to the closed generator"]], "arch.bootstrap.StationaryBootstrap.conf_int": [[74, 2, 2, 0, "extra_kwargs", "Extra keyword arguments to use when calling func and std_err_func, when appropriate"], [74, 2, 2, 0, "func", "Function the computes parameter values.  See Notes for requirements"], [74, 2, 2, 0, "method", "One of 'basic', 'percentile', 'studentized', 'norm' (identical to 'var', 'cov'), 'bc' (identical to 'debiased', 'bias-corrected'), or 'bca'"], [74, 2, 2, 0, "reps", "Number of bootstrap replications"], [74, 2, 2, 0, "reuse", "Flag indicating whether to reuse previously computed bootstrap results.  This allows alternative methods to be compared without rerunning the bootstrap simulation.  Reuse is ignored if reps is not the same across multiple runs, func changes across calls, or method is 'studentized'."], [74, 2, 2, 0, "sampling", "Type of sampling to use: 'nonparametric', 'semi-parametric' (or 'semi') or 'parametric'.  The default is 'nonparametric'.  See notes about the changes to func required when using 'semi' or 'parametric'."], [74, 2, 2, 0, "size", "Coverage of confidence interval"], [74, 2, 2, 0, "std_err_func", "Function to use when standardizing estimated parameters when using the studentized bootstrap.  Providing an analytical function eliminates the need for a nested bootstrap"], [74, 2, 2, 0, "studentize_reps", "Number of bootstraps to use in the inner bootstrap when using the studentized bootstrap.  Ignored when std_err_func is provided"], [74, 2, 2, 0, "tail", "One of 'two', 'upper' or 'lower'."]], "arch.bootstrap.StationaryBootstrap.cov": [[75, 2, 2, 0, "extra_kwargs", "Dictionary of extra keyword arguments to pass to func"], [75, 2, 2, 0, "func", "Callable function that returns the statistic of interest as a 1-d array"], [75, 2, 2, 0, "recenter", "Whether to center the bootstrap variance estimator on the average of the bootstrap samples (True) or to center on the original sample estimate (False).  Default is True."], [75, 2, 2, 0, "reps", "Number of bootstrap replications"]], "arch.bootstrap.StationaryBootstrap.reset": [[80, 2, 2, 0, "use_seed", "Flag indicating whether to use the last seed if provided.  If False or if no seed has been set, the bootstrap will be reset to the initial state.  Default is True"]], "arch.bootstrap.StationaryBootstrap.seed": [[81, 2, 2, 0, "value", "Value to use as the seed."]], "arch.bootstrap.StationaryBootstrap.set_state": [[82, 2, 2, 0, "state", "Dictionary or tuple containing the state."]], "arch.bootstrap.StationaryBootstrap.var": [[85, 2, 2, 0, "extra_kwargs", "Dictionary of extra keyword arguments to pass to func"], [85, 2, 2, 0, "func", "Callable function that returns the statistic of interest as a 1-d array"], [85, 2, 2, 0, "recenter", "Whether to center the bootstrap variance estimator on the average of the bootstrap samples (True) or to center on the original sample estimate (False).  Default is True."], [85, 2, 2, 0, "reps", "Number of bootstrap replications"]], "arch.bootstrap.StepM.__init__": [[240, 2, 2, 0, "benchmark", "T element array of benchmark model losses"], [240, 2, 2, 0, "block_size", "Length of window to use in the bootstrap.  If not provided, sqrt(T) is used.  In general, this should be provided and chosen to be appropriate for the data."], [240, 2, 2, 0, "bootstrap", "Bootstrap to use.  Options are 'stationary' or 'sb': Stationary bootstrap (Default) 'circular' or 'cbb': Circular block bootstrap 'moving block' or 'mbb': Moving block bootstrap"], [240, 2, 2, 0, "models", "T by k element array of alternative model losses"], [240, 2, 2, 0, "nested", "Flag indicating to use a nested bootstrap to compute variances for studentization.  Default is False.  Note that this can be slow since the procedure requires k extra bootstraps."], [240, 2, 2, 0, "reps", "Number of bootstrap replications to uses.  Default is 1000."], [240, 2, 2, 0, "seed", "Seed value to use when creating the bootstrap used in the comparison. If an integer or None, the NumPy default_rng is used with the seed value.  If a Generator or a RandomState, the argument is used."], [240, 2, 2, 0, "size", "Value in (0,1) to use as the test size when implementing the comparison. Default value is 0.05."], [240, 2, 2, 0, "studentize", "Flag indicating to studentize loss differentials. Default is True"]], "arch.bootstrap.StepM": [[254, 3, 1, 0, "compute", "Compute the set of superior models."], [255, 3, 1, 0, "reset", "Reset the bootstrap to it's initial state."], [256, 3, 1, 0, "seed", "Seed the bootstrap's random number generator"], [257, 5, 1, 0, "superior_models", "List of the indices or column names of the superior models"]], "arch.bootstrap.StepM.seed": [[256, 2, 2, 0, "value", "Integer to use as the seed"]], "arch.bootstrap.optimal_block_length": [[10, 2, 2, 0, "x", "A one-dimensional or two-dimensional array-like.  Operates columns by column if 2-dimensional."]], "arch.covariance": [[98, 0, 0, 1, "kernel", ""]], "arch.covariance.kernel": [[99, 1, 1, 0, "Andrews", "Alternative name of the QuadraticSpectral covariance estimator."], [100, 1, 1, 0, "Bartlett", "Bartlett's (Newey-West) kernel covariance estimation."], [101, 1, 1, 0, "CovarianceEstimate", "Covariance estimate using a long-run covariance estimator"], [102, 1, 1, 0, "Gallant", "Alternative name for Parzen covariance estimator."], [103, 1, 1, 0, "NeweyWest", "Alternative name for Bartlett covariance estimator."], [104, 1, 1, 0, "Parzen", "Parzen's kernel covariance estimation."], [105, 1, 1, 0, "ParzenCauchy", "Parzen's Cauchy kernel covariance estimation."], [106, 1, 1, 0, "ParzenGeometric", "Parzen's Geometric kernel covariance estimation."], [107, 1, 1, 0, "ParzenRiesz", "Parzen-Reisz kernel covariance estimation."], [108, 1, 1, 0, "QuadraticSpectral", "Quadratic-Spectral (Andrews') kernel covariance estimation."], [109, 1, 1, 0, "TukeyHamming", "Tukey-Hamming kernel covariance estimation."], [110, 1, 1, 0, "TukeyHanning", "Tukey-Hanning kernel covariance estimation."], [111, 1, 1, 0, "TukeyParzen", "Tukey-Parzen kernel covariance estimation."]], "arch.covariance.kernel.Andrews": [[112, 5, 1, 0, "bandwidth", "The bandwidth used by the covariance estimator."], [113, 5, 1, 0, "bandwidth_scale", "The power used in optimal bandwidth calculation."], [114, 5, 1, 0, "centered", "Flag indicating whether the data are centered (demeaned)."], [115, 5, 1, 0, "cov", "The estimated covariances."], [116, 5, 1, 0, "force_int", "Flag indicating whether the bandwidth is restricted to be an integer."], [117, 5, 1, 0, "kernel_const", "The constant used in optimal bandwidth calculation."], [118, 5, 1, 0, "kernel_weights", "Weights used in covariance calculation."], [119, 5, 1, 0, "name", "The covariance estimator's name."], [120, 5, 1, 0, "opt_bandwidth", "Estimate optimal bandwidth."], [121, 5, 1, 0, "rate", "The optimal rate used in bandwidth selection."]], "arch.covariance.kernel.Bartlett.__init__": [[100, 2, 2, 0, "bandwidth", "The kernel's bandwidth.  If None, optimal bandwidth is estimated."], [100, 2, 2, 0, "center", "A flag indicating whether x should be demeaned before estimating the covariance."], [100, 2, 2, 0, "df_adjust", "Degrees of freedom to remove when adjusting the covariance. Uses the number of observations in x minus df_adjust when dividing inner-products."], [100, 2, 2, 0, "force_int", "Force bandwidth to be an integer."], [100, 2, 2, 0, "weights", "An array of weights used to combine when estimating optimal bandwidth. If not provided, a vector of 1s is used. Must have nvar elements."], [100, 2, 2, 0, "x", "The data to use in covariance estimation."]], "arch.covariance.kernel.Bartlett": [[122, 5, 1, 0, "bandwidth", "The bandwidth used by the covariance estimator."], [123, 5, 1, 0, "bandwidth_scale", "The power used in optimal bandwidth calculation."], [124, 5, 1, 0, "centered", "Flag indicating whether the data are centered (demeaned)."], [125, 5, 1, 0, "cov", "The estimated covariances."], [126, 5, 1, 0, "force_int", "Flag indicating whether the bandwidth is restricted to be an integer."], [127, 5, 1, 0, "kernel_const", "The constant used in optimal bandwidth calculation."], [128, 5, 1, 0, "kernel_weights", "Weights used in covariance calculation."], [129, 5, 1, 0, "name", "The covariance estimator's name."], [130, 5, 1, 0, "opt_bandwidth", "Estimate optimal bandwidth."], [131, 5, 1, 0, "rate", "The optimal rate used in bandwidth selection."]], "arch.covariance.kernel.CovarianceEstimate.__init__": [[101, 2, 2, 0, "columns", "Column labels to use if covariance estimates are returned as DataFrames."], [101, 2, 2, 0, "long_run", "The long-run covariance estimate. If not provided, computed from short_run and one_sided_strict."], [101, 2, 2, 0, "one_sided_strict", "The one-sided-strict covariance estimate. If not provided, computed from short_run and one_sided_strict."], [101, 2, 2, 0, "short_run", "The short-run covariance estimate."]], "arch.covariance.kernel.CovarianceEstimate": [[132, 5, 1, 0, "long_run", "The long-run covariance estimate."], [133, 5, 1, 0, "one_sided", "The one-sided covariance estimate."], [134, 5, 1, 0, "one_sided_strict", "The one-sided strict covariance estimate."], [135, 5, 1, 0, "short_run", "The short-run covariance estimate."]], "arch.covariance.kernel.Gallant": [[136, 5, 1, 0, "bandwidth", "The bandwidth used by the covariance estimator."], [137, 5, 1, 0, "bandwidth_scale", "The power used in optimal bandwidth calculation."], [138, 5, 1, 0, "centered", "Flag indicating whether the data are centered (demeaned)."], [139, 5, 1, 0, "cov", "The estimated covariances."], [140, 5, 1, 0, "force_int", "Flag indicating whether the bandwidth is restricted to be an integer."], [141, 5, 1, 0, "kernel_const", "The constant used in optimal bandwidth calculation."], [142, 5, 1, 0, "kernel_weights", "Weights used in covariance calculation."], [143, 5, 1, 0, "name", "The covariance estimator's name."], [144, 5, 1, 0, "opt_bandwidth", "Estimate optimal bandwidth."], [145, 5, 1, 0, "rate", "The optimal rate used in bandwidth selection."]], "arch.covariance.kernel.NeweyWest": [[146, 5, 1, 0, "bandwidth", "The bandwidth used by the covariance estimator."], [147, 5, 1, 0, "bandwidth_scale", "The power used in optimal bandwidth calculation."], [148, 5, 1, 0, "centered", "Flag indicating whether the data are centered (demeaned)."], [149, 5, 1, 0, "cov", "The estimated covariances."], [150, 5, 1, 0, "force_int", "Flag indicating whether the bandwidth is restricted to be an integer."], [151, 5, 1, 0, "kernel_const", "The constant used in optimal bandwidth calculation."], [152, 5, 1, 0, "kernel_weights", "Weights used in covariance calculation."], [153, 5, 1, 0, "name", "The covariance estimator's name."], [154, 5, 1, 0, "opt_bandwidth", "Estimate optimal bandwidth."], [155, 5, 1, 0, "rate", "The optimal rate used in bandwidth selection."]], "arch.covariance.kernel.Parzen.__init__": [[104, 2, 2, 0, "bandwidth", "The kernel's bandwidth.  If None, optimal bandwidth is estimated."], [104, 2, 2, 0, "center", "A flag indicating whether x should be demeaned before estimating the covariance."], [104, 2, 2, 0, "df_adjust", "Degrees of freedom to remove when adjusting the covariance. Uses the number of observations in x minus df_adjust when dividing inner-products."], [104, 2, 2, 0, "force_int", "Force bandwidth to be an integer."], [104, 2, 2, 0, "weights", "An array of weights used to combine when estimating optimal bandwidth. If not provided, a vector of 1s is used. Must have nvar elements."], [104, 2, 2, 0, "x", "The data to use in covariance estimation."]], "arch.covariance.kernel.Parzen": [[156, 5, 1, 0, "bandwidth", "The bandwidth used by the covariance estimator."], [157, 5, 1, 0, "bandwidth_scale", "The power used in optimal bandwidth calculation."], [158, 5, 1, 0, "centered", "Flag indicating whether the data are centered (demeaned)."], [159, 5, 1, 0, "cov", "The estimated covariances."], [160, 5, 1, 0, "force_int", "Flag indicating whether the bandwidth is restricted to be an integer."], [161, 5, 1, 0, "kernel_const", "The constant used in optimal bandwidth calculation."], [162, 5, 1, 0, "kernel_weights", "Weights used in covariance calculation."], [163, 5, 1, 0, "name", "The covariance estimator's name."], [164, 5, 1, 0, "opt_bandwidth", "Estimate optimal bandwidth."], [165, 5, 1, 0, "rate", "The optimal rate used in bandwidth selection."]], "arch.covariance.kernel.ParzenCauchy.__init__": [[105, 2, 2, 0, "bandwidth", "The kernel's bandwidth.  If None, optimal bandwidth is estimated."], [105, 2, 2, 0, "center", "A flag indicating whether x should be demeaned before estimating the covariance."], [105, 2, 2, 0, "df_adjust", "Degrees of freedom to remove when adjusting the covariance. Uses the number of observations in x minus df_adjust when dividing inner-products."], [105, 2, 2, 0, "force_int", "Force bandwidth to be an integer."], [105, 2, 2, 0, "weights", "An array of weights used to combine when estimating optimal bandwidth. If not provided, a vector of 1s is used. Must have nvar elements."], [105, 2, 2, 0, "x", "The data to use in covariance estimation."]], "arch.covariance.kernel.ParzenCauchy": [[166, 5, 1, 0, "bandwidth", "The bandwidth used by the covariance estimator."], [167, 5, 1, 0, "bandwidth_scale", "The power used in optimal bandwidth calculation."], [168, 5, 1, 0, "centered", "Flag indicating whether the data are centered (demeaned)."], [169, 5, 1, 0, "cov", "The estimated covariances."], [170, 5, 1, 0, "force_int", "Flag indicating whether the bandwidth is restricted to be an integer."], [171, 5, 1, 0, "kernel_const", "The constant used in optimal bandwidth calculation."], [172, 5, 1, 0, "kernel_weights", "Weights used in covariance calculation."], [173, 5, 1, 0, "name", "The covariance estimator's name."], [174, 5, 1, 0, "opt_bandwidth", "Estimate optimal bandwidth."], [175, 5, 1, 0, "rate", "The optimal rate used in bandwidth selection."]], "arch.covariance.kernel.ParzenGeometric.__init__": [[106, 2, 2, 0, "bandwidth", "The kernel's bandwidth.  If None, optimal bandwidth is estimated."], [106, 2, 2, 0, "center", "A flag indicating whether x should be demeaned before estimating the covariance."], [106, 2, 2, 0, "df_adjust", "Degrees of freedom to remove when adjusting the covariance. Uses the number of observations in x minus df_adjust when dividing inner-products."], [106, 2, 2, 0, "force_int", "Force bandwidth to be an integer."], [106, 2, 2, 0, "weights", "An array of weights used to combine when estimating optimal bandwidth. If not provided, a vector of 1s is used. Must have nvar elements."], [106, 2, 2, 0, "x", "The data to use in covariance estimation."]], "arch.covariance.kernel.ParzenGeometric": [[176, 5, 1, 0, "bandwidth", "The bandwidth used by the covariance estimator."], [177, 5, 1, 0, "bandwidth_scale", "The power used in optimal bandwidth calculation."], [178, 5, 1, 0, "centered", "Flag indicating whether the data are centered (demeaned)."], [179, 5, 1, 0, "cov", "The estimated covariances."], [180, 5, 1, 0, "force_int", "Flag indicating whether the bandwidth is restricted to be an integer."], [181, 5, 1, 0, "kernel_const", "The constant used in optimal bandwidth calculation."], [182, 5, 1, 0, "kernel_weights", "Weights used in covariance calculation."], [183, 5, 1, 0, "name", "The covariance estimator's name."], [184, 5, 1, 0, "opt_bandwidth", "Estimate optimal bandwidth."], [185, 5, 1, 0, "rate", "The optimal rate used in bandwidth selection."]], "arch.covariance.kernel.ParzenRiesz.__init__": [[107, 2, 2, 0, "bandwidth", "The kernel's bandwidth.  If None, optimal bandwidth is estimated."], [107, 2, 2, 0, "center", "A flag indicating whether x should be demeaned before estimating the covariance."], [107, 2, 2, 0, "df_adjust", "Degrees of freedom to remove when adjusting the covariance. Uses the number of observations in x minus df_adjust when dividing inner-products."], [107, 2, 2, 0, "force_int", "Force bandwidth to be an integer."], [107, 2, 2, 0, "weights", "An array of weights used to combine when estimating optimal bandwidth. If not provided, a vector of 1s is used. Must have nvar elements."], [107, 2, 2, 0, "x", "The data to use in covariance estimation."]], "arch.covariance.kernel.ParzenRiesz": [[186, 5, 1, 0, "bandwidth", "The bandwidth used by the covariance estimator."], [187, 5, 1, 0, "bandwidth_scale", "The power used in optimal bandwidth calculation."], [188, 5, 1, 0, "centered", "Flag indicating whether the data are centered (demeaned)."], [189, 5, 1, 0, "cov", "The estimated covariances."], [190, 5, 1, 0, "force_int", "Flag indicating whether the bandwidth is restricted to be an integer."], [191, 5, 1, 0, "kernel_const", "The constant used in optimal bandwidth calculation."], [192, 5, 1, 0, "kernel_weights", "Weights used in covariance calculation."], [193, 5, 1, 0, "name", "The covariance estimator's name."], [194, 5, 1, 0, "opt_bandwidth", "Estimate optimal bandwidth."], [195, 5, 1, 0, "rate", "The optimal rate used in bandwidth selection."]], "arch.covariance.kernel.QuadraticSpectral.__init__": [[108, 2, 2, 0, "bandwidth", "The kernel's bandwidth.  If None, optimal bandwidth is estimated."], [108, 2, 2, 0, "center", "A flag indicating whether x should be demeaned before estimating the covariance."], [108, 2, 2, 0, "df_adjust", "Degrees of freedom to remove when adjusting the covariance. Uses the number of observations in x minus df_adjust when dividing inner-products."], [108, 2, 2, 0, "force_int", "Force bandwidth to be an integer."], [108, 2, 2, 0, "weights", "An array of weights used to combine when estimating optimal bandwidth. If not provided, a vector of 1s is used. Must have nvar elements."], [108, 2, 2, 0, "x", "The data to use in covariance estimation."]], "arch.covariance.kernel.QuadraticSpectral": [[196, 5, 1, 0, "bandwidth", "The bandwidth used by the covariance estimator."], [197, 5, 1, 0, "bandwidth_scale", "The power used in optimal bandwidth calculation."], [198, 5, 1, 0, "centered", "Flag indicating whether the data are centered (demeaned)."], [199, 5, 1, 0, "cov", "The estimated covariances."], [200, 5, 1, 0, "force_int", "Flag indicating whether the bandwidth is restricted to be an integer."], [201, 5, 1, 0, "kernel_const", "The constant used in optimal bandwidth calculation."], [202, 5, 1, 0, "kernel_weights", "Weights used in covariance calculation."], [203, 5, 1, 0, "name", "The covariance estimator's name."], [204, 5, 1, 0, "opt_bandwidth", "Estimate optimal bandwidth."], [205, 5, 1, 0, "rate", "The optimal rate used in bandwidth selection."]], "arch.covariance.kernel.TukeyHamming.__init__": [[109, 2, 2, 0, "bandwidth", "The kernel's bandwidth.  If None, optimal bandwidth is estimated."], [109, 2, 2, 0, "center", "A flag indicating whether x should be demeaned before estimating the covariance."], [109, 2, 2, 0, "df_adjust", "Degrees of freedom to remove when adjusting the covariance. Uses the number of observations in x minus df_adjust when dividing inner-products."], [109, 2, 2, 0, "force_int", "Force bandwidth to be an integer."], [109, 2, 2, 0, "weights", "An array of weights used to combine when estimating optimal bandwidth. If not provided, a vector of 1s is used. Must have nvar elements."], [109, 2, 2, 0, "x", "The data to use in covariance estimation."]], "arch.covariance.kernel.TukeyHamming": [[206, 5, 1, 0, "bandwidth", "The bandwidth used by the covariance estimator."], [207, 5, 1, 0, "bandwidth_scale", "The power used in optimal bandwidth calculation."], [208, 5, 1, 0, "centered", "Flag indicating whether the data are centered (demeaned)."], [209, 5, 1, 0, "cov", "The estimated covariances."], [210, 5, 1, 0, "force_int", "Flag indicating whether the bandwidth is restricted to be an integer."], [211, 5, 1, 0, "kernel_const", "The constant used in optimal bandwidth calculation."], [212, 5, 1, 0, "kernel_weights", "Weights used in covariance calculation."], [213, 5, 1, 0, "name", "The covariance estimator's name."], [214, 5, 1, 0, "opt_bandwidth", "Estimate optimal bandwidth."], [215, 5, 1, 0, "rate", "The optimal rate used in bandwidth selection."]], "arch.covariance.kernel.TukeyHanning.__init__": [[110, 2, 2, 0, "bandwidth", "The kernel's bandwidth.  If None, optimal bandwidth is estimated."], [110, 2, 2, 0, "center", "A flag indicating whether x should be demeaned before estimating the covariance."], [110, 2, 2, 0, "df_adjust", "Degrees of freedom to remove when adjusting the covariance. Uses the number of observations in x minus df_adjust when dividing inner-products."], [110, 2, 2, 0, "force_int", "Force bandwidth to be an integer."], [110, 2, 2, 0, "weights", "An array of weights used to combine when estimating optimal bandwidth. If not provided, a vector of 1s is used. Must have nvar elements."], [110, 2, 2, 0, "x", "The data to use in covariance estimation."]], "arch.covariance.kernel.TukeyHanning": [[216, 5, 1, 0, "bandwidth", "The bandwidth used by the covariance estimator."], [217, 5, 1, 0, "bandwidth_scale", "The power used in optimal bandwidth calculation."], [218, 5, 1, 0, "centered", "Flag indicating whether the data are centered (demeaned)."], [219, 5, 1, 0, "cov", "The estimated covariances."], [220, 5, 1, 0, "force_int", "Flag indicating whether the bandwidth is restricted to be an integer."], [221, 5, 1, 0, "kernel_const", "The constant used in optimal bandwidth calculation."], [222, 5, 1, 0, "kernel_weights", "Weights used in covariance calculation."], [223, 5, 1, 0, "name", "The covariance estimator's name."], [224, 5, 1, 0, "opt_bandwidth", "Estimate optimal bandwidth."], [225, 5, 1, 0, "rate", "The optimal rate used in bandwidth selection."]], "arch.covariance.kernel.TukeyParzen.__init__": [[111, 2, 2, 0, "bandwidth", "The kernel's bandwidth.  If None, optimal bandwidth is estimated."], [111, 2, 2, 0, "center", "A flag indicating whether x should be demeaned before estimating the covariance."], [111, 2, 2, 0, "df_adjust", "Degrees of freedom to remove when adjusting the covariance. Uses the number of observations in x minus df_adjust when dividing inner-products."], [111, 2, 2, 0, "force_int", "Force bandwidth to be an integer."], [111, 2, 2, 0, "weights", "An array of weights used to combine when estimating optimal bandwidth. If not provided, a vector of 1s is used. Must have nvar elements."], [111, 2, 2, 0, "x", "The data to use in covariance estimation."]], "arch.covariance.kernel.TukeyParzen": [[226, 5, 1, 0, "bandwidth", "The bandwidth used by the covariance estimator."], [227, 5, 1, 0, "bandwidth_scale", "The power used in optimal bandwidth calculation."], [228, 5, 1, 0, "centered", "Flag indicating whether the data are centered (demeaned)."], [229, 5, 1, 0, "cov", "The estimated covariances."], [230, 5, 1, 0, "force_int", "Flag indicating whether the bandwidth is restricted to be an integer."], [231, 5, 1, 0, "kernel_const", "The constant used in optimal bandwidth calculation."], [232, 5, 1, 0, "kernel_weights", "Weights used in covariance calculation."], [233, 5, 1, 0, "name", "The covariance estimator's name."], [234, 5, 1, 0, "opt_bandwidth", "Estimate optimal bandwidth."], [235, 5, 1, 0, "rate", "The optimal rate used in bandwidth selection."]], "arch.unitroot": [[262, 1, 1, 0, "ADF", "Augmented Dickey-Fuller unit root test"], [263, 1, 1, 0, "DFGLS", "Elliott, Rothenberg and Stock's (1) GLS detrended Dickey-Fuller"], [264, 1, 1, 0, "KPSS", "Kwiatkowski, Phillips, Schmidt and Shin (KPSS) stationarity test"], [265, 1, 1, 0, "PhillipsPerron", "Phillips-Perron unit root test"], [266, 1, 1, 0, "VarianceRatio", "Variance Ratio test of a random walk."], [267, 1, 1, 0, "ZivotAndrews", "Zivot-Andrews structural-break unit-root test"], [268, 6, 1, 0, "auto_bandwidth", "Automatic bandwidth selection of Andrews (1991) and Newey & West (1994)."], [261, 0, 0, 1, "cointegration", ""]], "arch.unitroot.ADF.__init__": [[262, 2, 2, 0, "lags", "The number of lags to use in the ADF regression.  If omitted or None, method is used to automatically select the lag length with no more than max_lags are included."], [262, 2, 2, 0, "low_memory", "Flag indicating whether to use a low memory implementation of the lag selection algorithm. The low memory algorithm is slower than the standard algorithm but will use 2-4% of the memory required for the standard algorithm. This options allows automatic lag selection to be used in very long time series. If None, use automatic selection of algorithm."], [262, 2, 2, 0, "max_lags", "The maximum number of lags to use when selecting lag length"], [262, 2, 2, 0, "method", "The method to use when selecting the lag length"], [262, 2, 2, 0, "trend", "The trend component to include in the test"], [262, 2, 2, 0, "y", "The data to test for a unit root"]], "arch.unitroot.ADF": [[278, 5, 1, 0, "alternative_hypothesis", "The alternative hypothesis"], [279, 5, 1, 0, "critical_values", "Dictionary containing critical values specific to the test, number of observations and included deterministic trend terms."], [280, 5, 1, 0, "lags", "Sets or gets the number of lags used in the model. When bootstrap use DF-type regressions, lags is the number of lags in the regression model.  When bootstrap use long-run variance estimators, lags is the number of lags used in the long-run variance estimator."], [281, 5, 1, 0, "max_lags", "Sets or gets the maximum lags used when automatically selecting lag length"], [282, 5, 1, 0, "nobs", "The number of observations used when computing the test statistic. Accounts for loss of data due to lags for regression-based bootstrap."], [283, 5, 1, 0, "null_hypothesis", "The null hypothesis"], [284, 5, 1, 0, "pvalue", "Returns the p-value for the test statistic"], [285, 5, 1, 0, "regression", "Returns the OLS regression results from the ADF model estimated"], [286, 5, 1, 0, "stat", "The test statistic for a unit root"], [287, 3, 1, 0, "summary", "Summary of test, containing statistic, p-value and critical values"], [288, 5, 1, 0, "trend", "Sets or gets the deterministic trend term used in the test. See valid_trends for a list of supported trends"], [289, 5, 1, 0, "valid_trends", "List of valid trend terms."], [290, 5, 1, 0, "y", "Returns the data used in the test statistic"]], "arch.unitroot.DFGLS.__init__": [[263, 2, 2, 0, "lags", "The number of lags to use in the ADF regression.  If omitted or None, method is used to automatically select the lag length with no more than max_lags are included."], [263, 2, 2, 0, "max_lags", "The maximum number of lags to use when selecting lag length. When using automatic lag length selection, the lag is selected using OLS detrending rather than GLS detrending (2)."], [263, 2, 2, 0, "method", "The method to use when selecting the lag length"], [263, 2, 2, 0, "trend", "The trend component to include in the test"], [263, 2, 2, 0, "y", "The data to test for a unit root"]], "arch.unitroot.DFGLS": [[291, 5, 1, 0, "alternative_hypothesis", "The alternative hypothesis"], [292, 5, 1, 0, "critical_values", "Dictionary containing critical values specific to the test, number of observations and included deterministic trend terms."], [293, 5, 1, 0, "lags", "Sets or gets the number of lags used in the model. When bootstrap use DF-type regressions, lags is the number of lags in the regression model.  When bootstrap use long-run variance estimators, lags is the number of lags used in the long-run variance estimator."], [294, 5, 1, 0, "max_lags", "Sets or gets the maximum lags used when automatically selecting lag length"], [295, 5, 1, 0, "nobs", "The number of observations used when computing the test statistic. Accounts for loss of data due to lags for regression-based bootstrap."], [296, 5, 1, 0, "null_hypothesis", "The null hypothesis"], [297, 5, 1, 0, "pvalue", "Returns the p-value for the test statistic"], [298, 5, 1, 0, "regression", "Returns the OLS regression results from the ADF model estimated"], [299, 5, 1, 0, "stat", "The test statistic for a unit root"], [300, 3, 1, 0, "summary", "Summary of test, containing statistic, p-value and critical values"], [301, 5, 1, 0, "trend", "Sets or gets the deterministic trend term used in the test. See valid_trends for a list of supported trends"], [302, 5, 1, 0, "valid_trends", "List of valid trend terms."], [303, 5, 1, 0, "y", "Returns the data used in the test statistic"]], "arch.unitroot.KPSS.__init__": [[264, 2, 2, 0, "lags", "The number of lags to use in the Newey-West estimator of the long-run covariance.  If omitted or None, the number of lags is calculated with the data-dependent method of Hobijn et al. (1998). See also Andrews (1991), Newey & West (1994), and Schwert (1989). Set lags=-1 to use the old method that only depends on the sample size, 12 * (nobs/100) ** (1/4)."], [264, 2, 2, 0, "trend", "\"c\" - Include a constant (Default) \"ct\" - Include a constant and linear time trend"], [264, 2, 2, 0, "y", "The data to test for stationarity"]], "arch.unitroot.KPSS": [[304, 5, 1, 0, "alternative_hypothesis", "The alternative hypothesis"], [305, 5, 1, 0, "critical_values", "Dictionary containing critical values specific to the test, number of observations and included deterministic trend terms."], [306, 5, 1, 0, "lags", "Sets or gets the number of lags used in the model. When bootstrap use DF-type regressions, lags is the number of lags in the regression model.  When bootstrap use long-run variance estimators, lags is the number of lags used in the long-run variance estimator."], [307, 5, 1, 0, "nobs", "The number of observations used when computing the test statistic. Accounts for loss of data due to lags for regression-based bootstrap."], [308, 5, 1, 0, "null_hypothesis", "The null hypothesis"], [309, 5, 1, 0, "pvalue", "Returns the p-value for the test statistic"], [310, 5, 1, 0, "stat", "The test statistic for a unit root"], [311, 3, 1, 0, "summary", "Summary of test, containing statistic, p-value and critical values"], [312, 5, 1, 0, "trend", "Sets or gets the deterministic trend term used in the test. See valid_trends for a list of supported trends"], [313, 5, 1, 0, "valid_trends", "List of valid trend terms."], [314, 5, 1, 0, "y", "Returns the data used in the test statistic"]], "arch.unitroot.PhillipsPerron.__init__": [[265, 2, 2, 0, "lags", "The number of lags to use in the Newey-West estimator of the long-run covariance.  If omitted or None, the lag length is set automatically to 12 * (nobs/100) ** (1/4)"], [265, 2, 2, 0, "test_type", "The test to use when computing the test statistic. \"tau\" is based on the t-stat and \"rho\" uses a test based on nobs times the re-centered regression coefficient"], [265, 2, 2, 0, "trend", "The trend component to include in the test"], [265, 2, 2, 0, "y", "The data to test for a unit root"]], "arch.unitroot.PhillipsPerron": [[315, 5, 1, 0, "alternative_hypothesis", "The alternative hypothesis"], [316, 5, 1, 0, "critical_values", "Dictionary containing critical values specific to the test, number of observations and included deterministic trend terms."], [317, 5, 1, 0, "lags", "Sets or gets the number of lags used in the model. When bootstrap use DF-type regressions, lags is the number of lags in the regression model.  When bootstrap use long-run variance estimators, lags is the number of lags used in the long-run variance estimator."], [318, 5, 1, 0, "nobs", "The number of observations used when computing the test statistic. Accounts for loss of data due to lags for regression-based bootstrap."], [319, 5, 1, 0, "null_hypothesis", "The null hypothesis"], [320, 5, 1, 0, "pvalue", "Returns the p-value for the test statistic"], [321, 5, 1, 0, "regression", "Returns OLS regression results for the specification used in the test"], [322, 5, 1, 0, "stat", "The test statistic for a unit root"], [323, 3, 1, 0, "summary", "Summary of test, containing statistic, p-value and critical values"], [324, 5, 1, 0, "test_type", "Gets or sets the test type returned by stat. Valid values are \"tau\" or \"rho\""], [325, 5, 1, 0, "trend", "Sets or gets the deterministic trend term used in the test. See valid_trends for a list of supported trends"], [326, 5, 1, 0, "valid_trends", "List of valid trend terms."], [327, 5, 1, 0, "y", "Returns the data used in the test statistic"]], "arch.unitroot.VarianceRatio.__init__": [[266, 2, 2, 0, "debiased", "Indicates whether to use a debiased version of the test. Default is True. Only applicable if overlap is True."], [266, 2, 2, 0, "lags", "The number of periods to used in the multi-period variance, which is the numerator of the test statistic.  Must be at least 2"], [266, 2, 2, 0, "overlap", "Indicates whether to use all overlapping blocks.  Default is True.  If False, the number of observations in y minus 1 must be an exact multiple of lags.  If this condition is not satisfied, some values at the end of y will be discarded."], [266, 2, 2, 0, "robust", "Indicates whether to use heteroskedasticity robust inference. Default is True."], [266, 2, 2, 0, "trend", "\"c\" allows for a non-zero drift in the random walk, while \"n\" requires that the increments to y are mean 0"], [266, 2, 2, 0, "y", "The data to test for a random walk"]], "arch.unitroot.VarianceRatio": [[328, 5, 1, 0, "alternative_hypothesis", "The alternative hypothesis"], [329, 5, 1, 0, "critical_values", "Dictionary containing critical values specific to the test, number of observations and included deterministic trend terms."], [330, 5, 1, 0, "debiased", "Sets of gets the indicator to use debiased variances in the ratio"], [331, 5, 1, 0, "lags", "Sets or gets the number of lags used in the model. When bootstrap use DF-type regressions, lags is the number of lags in the regression model.  When bootstrap use long-run variance estimators, lags is the number of lags used in the long-run variance estimator."], [332, 5, 1, 0, "nobs", "The number of observations used when computing the test statistic. Accounts for loss of data due to lags for regression-based bootstrap."], [333, 5, 1, 0, "null_hypothesis", "The null hypothesis"], [334, 5, 1, 0, "overlap", "Sets of gets the indicator to use overlapping returns in the long-period variance estimator"], [335, 5, 1, 0, "pvalue", "Returns the p-value for the test statistic"], [336, 5, 1, 0, "robust", "Sets of gets the indicator to use a heteroskedasticity robust variance estimator"], [337, 5, 1, 0, "stat", "The test statistic for a unit root"], [338, 3, 1, 0, "summary", "Summary of test, containing statistic, p-value and critical values"], [339, 5, 1, 0, "trend", "Sets or gets the deterministic trend term used in the test. See valid_trends for a list of supported trends"], [340, 5, 1, 0, "valid_trends", "List of valid trend terms."], [341, 5, 1, 0, "vr", "The ratio of the long block lags-period variance to the 1-period variance"], [342, 5, 1, 0, "y", "Returns the data used in the test statistic"]], "arch.unitroot.ZivotAndrews.__init__": [[267, 2, 2, 0, "lags", "The number of lags to use in the ADF regression.  If omitted or None, method is used to automatically select the lag length with no more than max_lags are included."], [267, 2, 2, 0, "max_lags", "The maximum number of lags to use when selecting lag length"], [267, 2, 2, 0, "method", "The method to use when selecting the lag length"], [267, 2, 2, 0, "trend", "The trend component to include in the test"], [267, 2, 2, 0, "trim", "percentage of series at begin/end to exclude from break-period calculation in range [0, 0.333] (default=0.15)"], [267, 2, 2, 0, "y", "data series"]], "arch.unitroot.ZivotAndrews": [[343, 5, 1, 0, "alternative_hypothesis", "The alternative hypothesis"], [344, 5, 1, 0, "critical_values", "Dictionary containing critical values specific to the test, number of observations and included deterministic trend terms."], [345, 5, 1, 0, "lags", "Sets or gets the number of lags used in the model. When bootstrap use DF-type regressions, lags is the number of lags in the regression model.  When bootstrap use long-run variance estimators, lags is the number of lags used in the long-run variance estimator."], [346, 5, 1, 0, "nobs", "The number of observations used when computing the test statistic. Accounts for loss of data due to lags for regression-based bootstrap."], [347, 5, 1, 0, "null_hypothesis", "The null hypothesis"], [348, 5, 1, 0, "pvalue", "Returns the p-value for the test statistic"], [349, 5, 1, 0, "stat", "The test statistic for a unit root"], [350, 3, 1, 0, "summary", "Summary of test, containing statistic, p-value and critical values"], [351, 5, 1, 0, "trend", "Sets or gets the deterministic trend term used in the test. See valid_trends for a list of supported trends"], [352, 5, 1, 0, "valid_trends", "List of valid trend terms."], [353, 5, 1, 0, "y", "Returns the data used in the test statistic"]], "arch.unitroot.auto_bandwidth": [[268, 2, 2, 0, "kernel", "The kernel function to use for selecting the bandwidth"], [268, 2, 2, 0, "y", "Data on which to apply the bandwidth selection"]], "arch.unitroot.cointegration": [[269, 1, 1, 0, "CanonicalCointegratingReg", "Canonical Cointegrating Regression cointegrating vector estimation."], [270, 1, 1, 0, "CointegrationAnalysisResults", "summary()"], [271, 1, 1, 0, "DynamicOLS", "Dynamic OLS (DOLS) cointegrating vector estimation"], [272, 1, 1, 0, "DynamicOLSResults", "Estimation results for Dynamic OLS models"], [273, 1, 1, 0, "EngleGrangerTestResults", "Results class for Engle-Granger cointegration tests."], [274, 1, 1, 0, "FullyModifiedOLS", "Fully Modified OLS cointegrating vector estimation."], [275, 1, 1, 0, "PhillipsOuliarisTestResults", "plot([axes, title])"], [276, 6, 1, 0, "engle_granger", "Test for cointegration within a set of time series."], [277, 6, 1, 0, "phillips_ouliaris", "Test for cointegration within a set of time series."]], "arch.unitroot.cointegration.CanonicalCointegratingReg.__init__": [[269, 2, 2, 0, "trend", "Trend to include in the cointegrating regression. Trends are:"], [269, 2, 2, 0, "x", "The right-hand-side variables in the cointegrating regression."], [269, 2, 2, 0, "x_trend", "Trends that affects affect the x-data but do not appear in the cointegrating regression. x_trend must be at least as large as trend, so that if trend is \"ct\", x_trend must be either \"ct\" or \"ctt\"."], [269, 2, 2, 0, "y", "The left-hand-side variable in the cointegrating regression."]], "arch.unitroot.cointegration.CanonicalCointegratingReg": [[354, 3, 1, 0, "fit", "Estimate the cointegrating vector."]], "arch.unitroot.cointegration.CanonicalCointegratingReg.fit": [[354, 2, 2, 0, "bandwidth", "The bandwidth to use. If not provided, the optimal bandwidth is estimated from the data. Setting the bandwidth to 0 and using \"unadjusted\" produces the classic OLS covariance estimator. Setting the bandwidth to 0 and using \"robust\" produces White's covariance estimator."], [354, 2, 2, 0, "df_adjust", "Whether the adjust the parameter covariance to account for the number of parameters estimated in the regression. If true, the parameter covariance estimator is multiplied by T/(T-k) where k is the number of regressors in the model."], [354, 2, 2, 0, "diff", "Use differenced data to estimate the residuals."], [354, 2, 2, 0, "force_int", "Whether the force the estimated optimal bandwidth to be an integer."], [354, 2, 2, 0, "kernel", "The string name of any of any known kernel-based long-run covariance estimators. Common choices are \"bartlett\" for the Bartlett kernel (Newey-West), \"parzen\" for the Parzen kernel and \"quadratic-spectral\" for the Quadratic Spectral kernel."]], "arch.unitroot.cointegration.CointegrationAnalysisResults": [[355, 5, 1, 0, "bandwidth", "The bandwidth used in the parameter covariance estimation"], [356, 5, 1, 0, "cov", "The estimated parameter covariance of the cointegrating vector"], [357, 5, 1, 0, "kernel", "The kernel used to estimate the covariance"], [358, 5, 1, 0, "long_run_variance", "Long-run variance estimate used in the parameter covariance estimator"], [359, 5, 1, 0, "params", "The estimated parameters of the cointegrating vector"], [360, 5, 1, 0, "pvalues", "P-value of the parameters in the cointegrating vector"], [361, 5, 1, 0, "resid", "The model residuals"], [362, 5, 1, 0, "residual_variance", "The variance of the regression residual."], [363, 5, 1, 0, "rsquared", "The model R\u00b2"], [364, 5, 1, 0, "rsquared_adj", "The degree-of-freedom adjusted R\u00b2"], [365, 5, 1, 0, "std_errors", "Standard errors  of the parameters in the cointegrating vector"], [366, 3, 1, 0, "summary", "Summary of the model, containing estimated parameters and std. errors"], [367, 5, 1, 0, "tvalues", "T-statistics of the parameters in the cointegrating vector"]], "arch.unitroot.cointegration.DynamicOLS.__init__": [[271, 2, 2, 0, "common", "Flag indicating that lags and leads should be restricted to the same value. When common is None, lags must equal leads and max_lag must equal max_lead."], [271, 2, 2, 0, "lags", "The number of lags to include in the model.  If None, the optimal number of lags is chosen using method."], [271, 2, 2, 0, "leads", "The number of leads to include in the model.  If None, the optimal number of leads is chosen using method."], [271, 2, 2, 0, "max_lag", "The maximum lag to consider. See Notes for value used when None."], [271, 2, 2, 0, "max_lead", "The maximum lead to consider. See Notes for value used when None."], [271, 2, 2, 0, "method", "The method used to select lag length when lags or leads is None."], [271, 2, 2, 0, "trend", "Trend to include in the cointegrating regression. Trends are:"], [271, 2, 2, 0, "x", "The right-hand-side variables in the cointegrating regression."], [271, 2, 2, 0, "y", "The left-hand-side variable in the cointegrating regression."]], "arch.unitroot.cointegration.DynamicOLS": [[368, 3, 1, 0, "fit", "Estimate the Dynamic OLS regression"]], "arch.unitroot.cointegration.DynamicOLS.fit": [[368, 2, 2, 0, "bandwidth", "The bandwidth to use. If not provided, the optimal bandwidth is estimated from the data. Setting the bandwidth to 0 and using \"unadjusted\" produces the classic OLS covariance estimator. Setting the bandwidth to 0 and using \"robust\" produces White's covariance estimator."], [368, 2, 2, 0, "cov_type", "Either \"unadjusted\" (or is equivalent \"homoskedastic\") or \"robust\" (or its equivalent \"kernel\")."], [368, 2, 2, 0, "df_adjust", "Whether the adjust the parameter covariance to account for the number of parameters estimated in the regression. If true, the parameter covariance estimator is multiplied by T/(T-k) where k is the number of regressors in the model."], [368, 2, 2, 0, "force_int", "Whether the force the estimated optimal bandwidth to be an integer."], [368, 2, 2, 0, "kernel", "The string name of any of any known kernel-based long-run covariance estimators. Common choices are \"bartlett\" for the Bartlett kernel (Newey-West), \"parzen\" for the Parzen kernel and \"quadratic-spectral\" for the Quadratic Spectral kernel."]], "arch.unitroot.cointegration.DynamicOLSResults.__init__": [[272, 2, 2, 0, "cov", "The estimated parameter covariance."], [272, 2, 2, 0, "cov_type", "The type of the parameter covariance estimator used."], [272, 2, 2, 0, "df_adjust", "Whether to degree of freedom adjust the estimator."], [272, 2, 2, 0, "kernel_est", "The covariance estimator instance used to estimate the parameter covariance."], [272, 2, 2, 0, "lags", "The number of lags included in the model."], [272, 2, 2, 0, "leads", "The number of leads included in the model."], [272, 2, 2, 0, "params", "The estimated model parameters."], [272, 2, 2, 0, "reg_results", "Regression results from fitting statsmodels OLS."], [272, 2, 2, 0, "resid", "The model residuals."]], "arch.unitroot.cointegration.DynamicOLSResults": [[369, 5, 1, 0, "bandwidth", "The bandwidth used in the parameter covariance estimation"], [370, 5, 1, 0, "cov", "The estimated parameter covariance of the cointegrating vector"], [371, 5, 1, 0, "cov_type", "The type of parameter covariance estimator used"], [372, 5, 1, 0, "full_cov", "Parameter covariance of the all model parameters, incl. leads and lags"], [373, 5, 1, 0, "full_params", "The complete set of parameters, including leads and lags"], [374, 5, 1, 0, "kernel", "The kernel used to estimate the covariance"], [375, 5, 1, 0, "lags", "The number of lags included in the model"], [376, 5, 1, 0, "leads", "The number of leads included in the model"], [377, 5, 1, 0, "long_run_variance", "The long-run variance of the regression residual."], [378, 5, 1, 0, "params", "The estimated parameters of the cointegrating vector"], [379, 5, 1, 0, "pvalues", "P-value of the parameters in the cointegrating vector"], [380, 5, 1, 0, "resid", "The model residuals"], [381, 5, 1, 0, "residual_variance", "The variance of the regression residual."], [382, 5, 1, 0, "rsquared", "The model R\u00b2"], [383, 5, 1, 0, "rsquared_adj", "The degree-of-freedom adjusted R\u00b2"], [384, 5, 1, 0, "std_errors", "Standard errors  of the parameters in the cointegrating vector"], [385, 3, 1, 0, "summary", "Summary of the model, containing estimated parameters and std. errors"], [386, 5, 1, 0, "tvalues", "T-statistics of the parameters in the cointegrating vector"]], "arch.unitroot.cointegration.DynamicOLSResults.summary": [[385, 2, 2, 0, "full", "Flag indicating whether to include all estimated parameters (True) or only the parameters of the cointegrating vector"]], "arch.unitroot.cointegration.EngleGrangerTestResults.__init__": [[273, 2, 2, 0, "adf", "The ADF instance used to perform the test and lag selection."], [273, 2, 2, 0, "alternative", "The alternative hypothesis."], [273, 2, 2, 0, "crit_vals", "The critical values of the Engle-Granger specific to the sample size and model dimension."], [273, 2, 2, 0, "null", "The null hypothesis."], [273, 2, 2, 0, "order", "The number of stochastic trends in the null distribution."], [273, 2, 2, 0, "pvalue", "The pvalue of the Engle-Granger test statistic."], [273, 2, 2, 0, "stat", "The Engle-Granger test statistic."], [273, 2, 2, 0, "trend", "The model's trend description."], [273, 2, 2, 0, "xsection", "The OLS results used in the cross-sectional regression."]], "arch.unitroot.cointegration.EngleGrangerTestResults": [[387, 5, 1, 0, "alternative_hypothesis", "The alternative hypothesis"], [388, 5, 1, 0, "cointegrating_vector", "The estimated cointegrating vector."], [389, 5, 1, 0, "critical_values", "Critical Values"], [390, 5, 1, 0, "distribution_order", "The number of stochastic trends under the null hypothesis."], [391, 5, 1, 0, "lags", "The number of lags used in the Augmented Dickey-Fuller regression."], [392, 5, 1, 0, "max_lags", "The maximum number of lags used in the lag-length selection."], [393, 5, 1, 0, "name", "Sets or gets the name of the cointegration test"], [394, 5, 1, 0, "null_hypothesis", "The null hypothesis"], [395, 3, 1, 0, "plot", "Plot the cointegration residuals."], [396, 5, 1, 0, "pvalue", "The p-value of the test statistic."], [397, 5, 1, 0, "resid", "The residual from the cointegrating regression."], [398, 5, 1, 0, "rho", "The estimated coefficient in the Dickey-Fuller Test"], [399, 5, 1, 0, "stat", "The test statistic."], [400, 3, 1, 0, "summary", "Summary of test, containing statistic, p-value and critical values"], [401, 5, 1, 0, "trend", "The trend used in the cointegrating regression"]], "arch.unitroot.cointegration.EngleGrangerTestResults.plot": [[395, 2, 2, 0, "axes", "matplotlib axes instance to hold the figure."], [395, 2, 2, 0, "title", "Title for the figure."]], "arch.unitroot.cointegration.FullyModifiedOLS.__init__": [[274, 2, 2, 0, "trend", "Trend to include in the cointegrating regression. Trends are:"], [274, 2, 2, 0, "x", "The right-hand-side variables in the cointegrating regression."], [274, 2, 2, 0, "x_trend", "Trends that affects affect the x-data but do not appear in the cointegrating regression. x_trend must be at least as large as trend, so that if trend is \"ct\", x_trend must be either \"ct\" or \"ctt\"."], [274, 2, 2, 0, "y", "The left-hand-side variable in the cointegrating regression."]], "arch.unitroot.cointegration.FullyModifiedOLS": [[402, 3, 1, 0, "fit", "Estimate the cointegrating vector."]], "arch.unitroot.cointegration.FullyModifiedOLS.fit": [[402, 2, 2, 0, "bandwidth", "The bandwidth to use. If not provided, the optimal bandwidth is estimated from the data. Setting the bandwidth to 0 and using \"unadjusted\" produces the classic OLS covariance estimator. Setting the bandwidth to 0 and using \"robust\" produces White's covariance estimator."], [402, 2, 2, 0, "df_adjust", "Whether the adjust the parameter covariance to account for the number of parameters estimated in the regression. If true, the parameter covariance estimator is multiplied by T/(T-k) where k is the number of regressors in the model."], [402, 2, 2, 0, "diff", "Use differenced data to estimate the residuals."], [402, 2, 2, 0, "force_int", "Whether the force the estimated optimal bandwidth to be an integer."], [402, 2, 2, 0, "kernel", "The string name of any of any known kernel-based long-run covariance estimators. Common choices are \"bartlett\" for the Bartlett kernel (Newey-West), \"parzen\" for the Parzen kernel and \"quadratic-spectral\" for the Quadratic Spectral kernel."]], "arch.unitroot.cointegration.PhillipsOuliarisTestResults": [[403, 5, 1, 0, "alternative_hypothesis", "The alternative hypothesis"], [404, 5, 1, 0, "bandwidth", "Bandwidth used by the long-run covariance estimator"], [405, 5, 1, 0, "cointegrating_vector", "The estimated cointegrating vector."], [406, 5, 1, 0, "critical_values", "Critical Values"], [407, 5, 1, 0, "distribution_order", "The number of stochastic trends under the null hypothesis."], [408, 5, 1, 0, "kernel", "Name of the long-run covariance estimator"], [409, 5, 1, 0, "name", "Sets or gets the name of the cointegration test"], [410, 5, 1, 0, "null_hypothesis", "The null hypothesis"], [411, 3, 1, 0, "plot", "Plot the cointegration residuals."], [412, 5, 1, 0, "pvalue", "The p-value of the test statistic."], [413, 5, 1, 0, "resid", "The residual from the cointegrating regression."], [414, 5, 1, 0, "stat", "The test statistic."], [415, 3, 1, 0, "summary", "Summary of test, containing statistic, p-value and critical values"], [416, 5, 1, 0, "trend", "The trend used in the cointegrating regression"]], "arch.unitroot.cointegration.PhillipsOuliarisTestResults.plot": [[411, 2, 2, 0, "axes", "matplotlib axes instance to hold the figure."], [411, 2, 2, 0, "title", "Title for the figure."]], "arch.unitroot.cointegration.engle_granger": [[276, 2, 2, 0, "lags", "The number of lagged differences to include in the Augmented Dickey-Fuller test used on the residuals of the"], [276, 2, 2, 0, "max_lags", "The maximum number of lags to consider when using automatic lag-length in the Augmented Dickey-Fuller regression."], [276, 2, 2, 0, "method", "The method used to select the number of lags included in the Augmented Dickey-Fuller regression."], [276, 2, 2, 0, "trend", "Trend to include in the cointegrating regression. Trends are:"], [276, 2, 2, 0, "x", "The right-hand-side variables in the cointegrating regression."], [276, 2, 2, 0, "y", "The left-hand-side variable in the cointegrating regression."]], "arch.unitroot.cointegration.phillips_ouliaris": [[277, 2, 2, 0, "bandwidth", "The bandwidth to use. If not provided, the optimal bandwidth is estimated from the data. Setting the bandwidth to 0 and using \"unadjusted\" produces the classic OLS covariance estimator. Setting the bandwidth to 0 and using \"robust\" produces White's covariance estimator."], [277, 2, 2, 0, "force_int", "Whether the force the estimated optimal bandwidth to be an integer."], [277, 2, 2, 0, "kernel", "The string name of any of any known kernel-based long-run covariance estimators. Common choices are \"bartlett\" for the Bartlett kernel (Newey-West), \"parzen\" for the Parzen kernel and \"quadratic-spectral\" for the Quadratic Spectral kernel."], [277, 2, 2, 0, "test_type", "The test statistic to compute. Supported options are:"], [277, 2, 2, 0, "trend", "Trend to include in the cointegrating regression. Trends are:"], [277, 2, 2, 0, "x", "The right-hand-side variables in the cointegrating regression."], [277, 2, 2, 0, "y", "The left-hand-side variable in the cointegrating regression."]], "arch.univariate": [[425, 1, 1, 0, "APARCH", "Asymmetric Power ARCH (APARCH) volatility process"], [426, 1, 1, 0, "ARCH", "ARCH process"], [427, 1, 1, 0, "ARCHInMean", "(G)ARCH-in-mean model and simulation"], [428, 1, 1, 0, "ARX", "Autoregressive model with optional exogenous regressors estimation and simulation"], [429, 1, 1, 0, "ConstantMean", "Constant mean model estimation and simulation."], [430, 1, 1, 0, "ConstantVariance", "Constant volatility process"], [431, 1, 1, 0, "EGARCH", "EGARCH model estimation"], [432, 1, 1, 0, "EWMAVariance", "Exponentially Weighted Moving-Average (RiskMetrics) Variance process"], [433, 1, 1, 0, "FIGARCH", "FIGARCH model"], [434, 1, 1, 0, "FixedVariance", "Fixed volatility process"], [435, 1, 1, 0, "GARCH", "GARCH and related model estimation"], [436, 1, 1, 0, "GeneralizedError", "Generalized Error distribution for use with ARCH models"], [437, 1, 1, 0, "HARCH", "Heterogeneous ARCH process"], [438, 1, 1, 0, "HARX", "Heterogeneous Autoregression (HAR), with optional exogenous regressors, model estimation and simulation"], [439, 1, 1, 0, "LS", "Least squares model estimation and simulation"], [440, 1, 1, 0, "MIDASHyperbolic", "MIDAS Hyperbolic ARCH process"], [441, 1, 1, 0, "Normal", "Standard normal distribution for use with ARCH models"], [442, 1, 1, 0, "RiskMetrics2006", "RiskMetrics 2006 Variance process"], [443, 1, 1, 0, "SkewStudent", "Standardized Skewed Student's distribution for use with ARCH models"], [444, 1, 1, 0, "StudentsT", "Standardized Student's distribution for use with ARCH models"], [445, 1, 1, 0, "ZeroMean", "Model with zero conditional mean estimation and simulation"], [891, 6, 1, 0, "arch_model", "Initialization of common ARCH model specifications"]], "arch.univariate.APARCH.__init__": [[425, 2, 2, 0, "common_asym", "Restrict all asymmetry terms to share the same asymmetry parameter. If False (default), then there are no restrictions on the o asymmetry parameters."], [425, 2, 2, 0, "delta", "Value to use for a fixed delta in the APARCH model. If not provided, the value of delta is jointly estimated with other model parameters. User provided delta is restricted to lie in (0.05, 4.0)."], [425, 2, 2, 0, "o", "Order of the asymmetric innovation. Must satisfy o<=p."], [425, 2, 2, 0, "p", "Order of the symmetric innovation. Must satisfy p>=o."], [425, 2, 2, 0, "q", "Order of the lagged (transformed) conditional variance"]], "arch.univariate.APARCH": [[454, 3, 1, 0, "backcast", "Construct values for backcasting to start the recursion"], [455, 3, 1, 0, "backcast_transform", "Transformation to apply to user-provided backcast values"], [456, 3, 1, 0, "bounds", "Returns bounds for parameters"], [457, 5, 1, 0, "common_asym", "The value of delta in the model. NaN is delta is estimated."], [458, 3, 1, 0, "compute_variance", "Compute the variance for the ARCH model"], [459, 3, 1, 0, "constraints", "Construct parameter constraints arrays for parameter estimation"], [460, 5, 1, 0, "delta", "The value of delta in the model. NaN is delta is estimated."], [461, 3, 1, 0, "forecast", "Forecast volatility from the model"], [462, 5, 1, 0, "name", "The name of the volatility process"], [463, 5, 1, 0, "num_params", "The number of parameters in the model"], [464, 3, 1, 0, "parameter_names", "Names of model parameters"], [465, 3, 1, 0, "simulate", "Simulate data from the model"], [466, 5, 1, 0, "start", "Index to use to start variance subarray selection"], [467, 3, 1, 0, "starting_values", "Returns starting values for the ARCH model"], [468, 5, 1, 0, "stop", "Index to use to stop variance subarray selection"], [469, 3, 1, 0, "update", "Compute the variance for a single observation"], [470, 5, 1, 0, "updateable", "Flag indicating that the volatility process supports update"], [471, 3, 1, 0, "variance_bounds", "Construct loose bounds for conditional variances."], [472, 5, 1, 0, "volatility_updater", "Get the volatility updater associated with the volatility process"]], "arch.univariate.APARCH.backcast": [[454, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.APARCH.backcast_transform": [[455, 2, 2, 0, "backcast", "User-provided backcast that approximates sigma2[0]."]], "arch.univariate.APARCH.bounds": [[456, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.APARCH.compute_variance": [[458, 2, 2, 0, "backcast", "Value to use when initializing ARCH recursion. Can be an ndarray when the model contains multiple components."], [458, 2, 2, 0, "parameters", "Model parameters"], [458, 2, 2, 0, "resids", "Vector of mean zero residuals"], [458, 2, 2, 0, "sigma2", "Array with same size as resids to store the conditional variance"], [458, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.APARCH.forecast": [[461, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [461, 2, 2, 0, "horizon", "Forecast horizon.  Must be 1 or larger.  Forecasts are produced for horizons in [1, horizon]."], [461, 2, 2, 0, "method", "Method to use when producing the forecast. The default is analytic."], [461, 2, 2, 0, "parameters", "Parameters required to forecast the volatility model"], [461, 2, 2, 0, "random_state", "NumPy RandomState instance to use when method is 'bootstrap'"], [461, 2, 2, 0, "resids", "Residuals to use in the recursion"], [461, 2, 2, 0, "rng", "Callable random number generator required if method is 'simulation'. Must take a single shape input and return random samples numbers with that shape."], [461, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [461, 2, 2, 0, "start", "Index of the first observation to use as the starting point for the forecast.  Default is len(resids)."], [461, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.APARCH.simulate": [[465, 2, 2, 0, "burn", "Number of additional observations to generate when initializing the simulation"], [465, 2, 2, 0, "initial_value", "Scalar or array of initial values to use when initializing the simulation"], [465, 2, 2, 0, "nobs", "Number of data points to simulate"], [465, 2, 2, 0, "parameters", "Parameters required to simulate the volatility model"], [465, 2, 2, 0, "rng", "Callable function that takes a single integer input and returns a vector of random numbers"]], "arch.univariate.APARCH.starting_values": [[467, 2, 2, 0, "resids", "Array of (approximate) residuals to use when computing starting values"]], "arch.univariate.APARCH.update": [[469, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [469, 2, 2, 0, "index", "The numerical index of the variance to compute"], [469, 2, 2, 0, "parameters", "The variance model parameters"], [469, 2, 2, 0, "resids", "The residual array. Only uses resids[:index] when computing sigma2[index]"], [469, 2, 2, 0, "sigma2", "The array containing the variances. Only uses sigma2[:index] when computing sigma2[index]. The computed value is stored in sigma2[index]."], [469, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.APARCH.variance_bounds": [[471, 2, 2, 0, "power", "Power used in the model. 2.0, the default corresponds to standard ARCH models that evolve in squares."], [471, 2, 2, 0, "resids", "Approximate residuals to use to compute the lower and upper bounds on the conditional variance"]], "arch.univariate.ARCH.__init__": [[426, 2, 2, 0, "p", "Order of the symmetric innovation"]], "arch.univariate.ARCH": [[473, 3, 1, 0, "backcast", "Construct values for backcasting to start the recursion"], [474, 3, 1, 0, "backcast_transform", "Transformation to apply to user-provided backcast values"], [475, 3, 1, 0, "bounds", "Returns bounds for parameters"], [476, 3, 1, 0, "compute_variance", "Compute the variance for the ARCH model"], [477, 3, 1, 0, "constraints", "Construct parameter constraints arrays for parameter estimation"], [478, 3, 1, 0, "forecast", "Forecast volatility from the model"], [479, 5, 1, 0, "name", "The name of the volatility process"], [480, 5, 1, 0, "num_params", "The number of parameters in the model"], [481, 3, 1, 0, "parameter_names", "Names of model parameters"], [482, 3, 1, 0, "simulate", "Simulate data from the model"], [483, 5, 1, 0, "start", "Index to use to start variance subarray selection"], [484, 3, 1, 0, "starting_values", "Returns starting values for the ARCH model"], [485, 5, 1, 0, "stop", "Index to use to stop variance subarray selection"], [486, 3, 1, 0, "update", "Compute the variance for a single observation"], [487, 5, 1, 0, "updateable", "Flag indicating that the volatility process supports update"], [488, 3, 1, 0, "variance_bounds", "Construct loose bounds for conditional variances."], [489, 5, 1, 0, "volatility_updater", "Get the volatility updater associated with the volatility process"]], "arch.univariate.ARCH.backcast": [[473, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.ARCH.backcast_transform": [[474, 2, 2, 0, "backcast", "User-provided backcast that approximates sigma2[0]."]], "arch.univariate.ARCH.bounds": [[475, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.ARCH.compute_variance": [[476, 2, 2, 0, "backcast", "Value to use when initializing ARCH recursion. Can be an ndarray when the model contains multiple components."], [476, 2, 2, 0, "parameters", "Model parameters"], [476, 2, 2, 0, "resids", "Vector of mean zero residuals"], [476, 2, 2, 0, "sigma2", "Array with same size as resids to store the conditional variance"], [476, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.ARCH.forecast": [[478, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [478, 2, 2, 0, "horizon", "Forecast horizon.  Must be 1 or larger.  Forecasts are produced for horizons in [1, horizon]."], [478, 2, 2, 0, "method", "Method to use when producing the forecast. The default is analytic."], [478, 2, 2, 0, "parameters", "Parameters required to forecast the volatility model"], [478, 2, 2, 0, "random_state", "NumPy RandomState instance to use when method is 'bootstrap'"], [478, 2, 2, 0, "resids", "Residuals to use in the recursion"], [478, 2, 2, 0, "rng", "Callable random number generator required if method is 'simulation'. Must take a single shape input and return random samples numbers with that shape."], [478, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [478, 2, 2, 0, "start", "Index of the first observation to use as the starting point for the forecast.  Default is len(resids)."], [478, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.ARCH.simulate": [[482, 2, 2, 0, "burn", "Number of additional observations to generate when initializing the simulation"], [482, 2, 2, 0, "initial_value", "Scalar or array of initial values to use when initializing the simulation"], [482, 2, 2, 0, "nobs", "Number of data points to simulate"], [482, 2, 2, 0, "parameters", "Parameters required to simulate the volatility model"], [482, 2, 2, 0, "rng", "Callable function that takes a single integer input and returns a vector of random numbers"]], "arch.univariate.ARCH.starting_values": [[484, 2, 2, 0, "resids", "Array of (approximate) residuals to use when computing starting values"]], "arch.univariate.ARCH.update": [[486, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [486, 2, 2, 0, "index", "The numerical index of the variance to compute"], [486, 2, 2, 0, "parameters", "The variance model parameters"], [486, 2, 2, 0, "resids", "The residual array. Only uses resids[:index] when computing sigma2[index]"], [486, 2, 2, 0, "sigma2", "The array containing the variances. Only uses sigma2[:index] when computing sigma2[index]. The computed value is stored in sigma2[index]."], [486, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.ARCH.variance_bounds": [[488, 2, 2, 0, "power", "Power used in the model. 2.0, the default corresponds to standard ARCH models that evolve in squares."], [488, 2, 2, 0, "resids", "Approximate residuals to use to compute the lower and upper bounds on the conditional variance"]], "arch.univariate.ARCHInMean.__init__": [[427, 2, 2, 0, "constant", "Flag whether the model should include a constant"], [427, 2, 2, 0, "distribution", "Error distribution to use in the model"], [427, 2, 2, 0, "form", "The form of the conditional variance that appears in the mean equation. The string names use the log of the conditional variance (\"log\"), the square-root of the conditional variance (\"vol\") or the conditional variance. When specified using a float, interpreted as \\sigma_t^{form} so that 1 is equivalent to \"vol\" and 2 is equivalent to \"var\". When using a number, must be different from 0."], [427, 2, 2, 0, "hold_back", "Number of observations at the start of the sample to exclude when estimating model parameters.  Used when comparing models with different lag lengths to estimate on the common sample."], [427, 2, 2, 0, "lags", "Description of lag structure of the HAR.  Scalar included all lags between 1 and the value.  A 1-d array includes the AR lags lags[0], lags[1], ..."], [427, 2, 2, 0, "rescale", "Flag indicating whether to automatically rescale data if the scale of the data is likely to produce convergence issues when estimating model parameters. If False, the model is estimated on the data without transformation.  If True, than y is rescaled and the new scale is reported in the estimation results."], [427, 2, 2, 0, "volatility", "Volatility process to use in the model. volatility.updateable must return True."], [427, 2, 2, 0, "x", "nobs by k element array containing exogenous regressors"], [427, 2, 2, 0, "y", "nobs element vector containing the dependent variable"]], "arch.univariate.ARCHInMean": [[490, 3, 1, 0, "bounds", "Construct bounds for parameters to use in non-linear optimization"], [491, 3, 1, 0, "compute_param_cov", "Computes parameter covariances using numerical derivatives."], [492, 3, 1, 0, "constraints", "Construct linear constraint arrays  for use in non-linear optimization"], [493, 5, 1, 0, "distribution", "Set or gets the error distribution"], [494, 3, 1, 0, "fit", "Estimate model parameters"], [495, 3, 1, 0, "fix", "Allows an ARCHModelFixedResult to be constructed from fixed parameters."], [496, 3, 1, 0, "forecast", "Construct forecasts from estimated model"], [497, 5, 1, 0, "form", "The form of the conditional variance in the mean"], [498, 5, 1, 0, "name", "The name of the model."], [499, 5, 1, 0, "num_params", "Returns the number of parameters"], [500, 3, 1, 0, "parameter_names", "List of parameters names"], [501, 3, 1, 0, "resids", "Compute model residuals"], [502, 3, 1, 0, "simulate", "Simulates data from a linear regression, AR or HAR models"], [503, 3, 1, 0, "starting_values", "Returns starting values for the mean model, often the same as the values returned from fit"], [504, 5, 1, 0, "volatility", "Set or gets the volatility process"], [505, 5, 1, 0, "x", "Gets the value of the exogenous regressors in the model"], [506, 5, 1, 0, "y", "Returns the dependent variable"]], "arch.univariate.ARCHInMean.compute_param_cov": [[491, 2, 2, 0, "backcast", "Value to use for pre-sample observations"], [491, 2, 2, 0, "params", "Model parameters"], [491, 2, 2, 0, "robust", "Flag indicating whether to use robust standard errors (True) or classic MLE (False)"]], "arch.univariate.ARCHInMean.fit": [[494, 2, 2, 0, "backcast", "Value to use as backcast. Should be measure \\sigma^2_0 since model-specific non-linear transformations are applied to value before computing the variance recursions."], [494, 2, 2, 0, "cov_type", "Estimation method of parameter covariance.  Supported options are 'robust', which does not assume the Information Matrix Equality holds and 'classic' which does.  In the ARCH literature, 'robust' corresponds to Bollerslev-Wooldridge covariance estimator."], [494, 2, 2, 0, "disp", "Either 'final' to print optimization result or 'off' to display nothing. If using a boolean, False is \"off\" and True is \"final\""], [494, 2, 2, 0, "first_obs", "First observation to use when estimating model"], [494, 2, 2, 0, "last_obs", "Last observation to use when estimating model"], [494, 2, 2, 0, "options", "Options to pass to scipy.optimize.minimize.  Valid entries include 'ftol', 'eps', 'disp', and 'maxiter'."], [494, 2, 2, 0, "show_warning", "Flag indicating whether convergence warnings should be shown."], [494, 2, 2, 0, "starting_values", "Array of starting values to use.  If not provided, starting values are constructed by the model components."], [494, 2, 2, 0, "tol", "Tolerance for termination."], [494, 2, 2, 0, "update_freq", "Frequency of iteration updates.  Output is generated every update_freq iterations. Set to 0 to disable iterative output."]], "arch.univariate.ARCHInMean.fix": [[495, 2, 2, 0, "first_obs", "First observation to use when fixing model"], [495, 2, 2, 0, "last_obs", "Last observation to use when fixing model"], [495, 2, 2, 0, "params", "User specified parameters to use when generating the result. Must have the correct number of parameters for a given choice of mean model, volatility model and distribution."]], "arch.univariate.ARCHInMean.forecast": [[496, 2, 2, 0, "align", "Either 'origin' or 'target'.  When set of 'origin', the t-th row of forecasts contains the forecasts for t+1, t+2, ..., t+h. When set to 'target', the t-th row contains the 1-step ahead forecast from time t-1, the 2 step from time t-2, ..., and the h-step from time t-h.  'target' simplified computing forecast errors since the realization and h-step forecast are aligned."], [496, 2, 2, 0, "horizon", "Number of steps to forecast"], [496, 2, 2, 0, "method", "Method to use when producing the forecast. The default is analytic. The method only affects the variance forecast generation.  Not all volatility models support all methods. In particular, volatility models that do not evolve in squares such as EGARCH or TARCH do not support the 'analytic' method for horizons > 1."], [496, 2, 2, 0, "params", "Parameters required to forecast. Must be identical in shape to the parameters computed by fitting the model."], [496, 2, 2, 0, "random_state", "NumPy RandomState instance to use when method is 'bootstrap'"], [496, 2, 2, 0, "reindex", "Whether to reindex the forecasts to have the same dimension as the series being forecast. Prior to 4.18 this was the default. As of 4.19 this is now optional. If not provided, a warning is raised about the future change in the default which will occur after September 2021."], [496, 2, 2, 0, "rng", "Custom random number generator to use in simulation-based forecasts. Must produce random samples using the syntax rng(size) where size the 2-element tuple (simulations, horizon)."], [496, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [496, 2, 2, 0, "start", "An integer, datetime or str indicating the first observation to produce the forecast for.  Datetimes can only be used with pandas inputs that have a datetime index. Strings must be convertible to a date time, such as in '1945-01-01'."], [496, 2, 2, 0, "x", "Values to use for exogenous regressors if any are included in the model. Three formats are accepted:"]], "arch.univariate.ARCHInMean.resids": [[501, 2, 2, 0, "params", "Model parameters"], [501, 2, 2, 0, "regressors", "Alternative regressor values to use when computing model residuals"], [501, 2, 2, 0, "y", "Alternative values to use when computing model residuals"]], "arch.univariate.ARCHInMean.simulate": [[502, 2, 2, 0, "burn", "Number of values to simulate to initialize the model and remove dependence on initial values."], [502, 2, 2, 0, "initial_value", "Either a scalar value or max(lags) array set of initial values to use when initializing the model.  If omitted, 0.0 is used."], [502, 2, 2, 0, "initial_value_vol", "An array or scalar to use when initializing the volatility process."], [502, 2, 2, 0, "nobs", "Length of series to simulate"], [502, 2, 2, 0, "params", "Parameters to use when simulating the model.  Parameter order is [mean volatility distribution] where the parameters of the mean model are ordered [constant lag[0] lag[1] ... lag[p] ex[0] ... ex[k-1]] where lag[j] indicates the coefficient on the jth lag in the model and ex[j] is the coefficient on the jth exogenous variable."], [502, 2, 2, 0, "x", "nobs + burn by k array of exogenous variables to include in the simulation."]], "arch.univariate.ARX.__init__": [[428, 2, 2, 0, "constant", "Flag whether the model should include a constant"], [428, 2, 2, 0, "distribution", "Error distribution to use in the model"], [428, 2, 2, 0, "hold_back", "Number of observations at the start of the sample to exclude when estimating model parameters.  Used when comparing models with different lag lengths to estimate on the common sample."], [428, 2, 2, 0, "lags", "Description of lag structure of the HAR.  Scalar included all lags between 1 and the value.  A 1-d array includes the AR lags lags[0], lags[1], ..."], [428, 2, 2, 0, "rescale", "Flag indicating whether to automatically rescale data if the scale of the data is likely to produce convergence issues when estimating model parameters. If False, the model is estimated on the data without transformation.  If True, than y is rescaled and the new scale is reported in the estimation results."], [428, 2, 2, 0, "volatility", "Volatility process to use in the model"], [428, 2, 2, 0, "x", "nobs by k element array containing exogenous regressors"], [428, 2, 2, 0, "y", "nobs element vector containing the dependent variable"]], "arch.univariate.ARX": [[507, 3, 1, 0, "bounds", "Construct bounds for parameters to use in non-linear optimization"], [508, 3, 1, 0, "compute_param_cov", "Computes parameter covariances using numerical derivatives."], [509, 3, 1, 0, "constraints", "Construct linear constraint arrays  for use in non-linear optimization"], [510, 5, 1, 0, "distribution", "Set or gets the error distribution"], [511, 3, 1, 0, "fit", "Estimate model parameters"], [512, 3, 1, 0, "fix", "Allows an ARCHModelFixedResult to be constructed from fixed parameters."], [513, 3, 1, 0, "forecast", "Construct forecasts from estimated model"], [514, 5, 1, 0, "name", "The name of the model."], [515, 5, 1, 0, "num_params", "Returns the number of parameters"], [516, 3, 1, 0, "parameter_names", "List of parameters names"], [517, 3, 1, 0, "resids", "Compute model residuals"], [518, 3, 1, 0, "simulate", "Simulates data from a linear regression, AR or HAR models"], [519, 3, 1, 0, "starting_values", "Returns starting values for the mean model, often the same as the values returned from fit"], [520, 5, 1, 0, "volatility", "Set or gets the volatility process"], [521, 5, 1, 0, "x", "Gets the value of the exogenous regressors in the model"], [522, 5, 1, 0, "y", "Returns the dependent variable"]], "arch.univariate.ARX.compute_param_cov": [[508, 2, 2, 0, "backcast", "Value to use for pre-sample observations"], [508, 2, 2, 0, "params", "Model parameters"], [508, 2, 2, 0, "robust", "Flag indicating whether to use robust standard errors (True) or classic MLE (False)"]], "arch.univariate.ARX.fit": [[511, 2, 2, 0, "backcast", "Value to use as backcast. Should be measure \\sigma^2_0 since model-specific non-linear transformations are applied to value before computing the variance recursions."], [511, 2, 2, 0, "cov_type", "Estimation method of parameter covariance.  Supported options are 'robust', which does not assume the Information Matrix Equality holds and 'classic' which does.  In the ARCH literature, 'robust' corresponds to Bollerslev-Wooldridge covariance estimator."], [511, 2, 2, 0, "disp", "Either 'final' to print optimization result or 'off' to display nothing. If using a boolean, False is \"off\" and True is \"final\""], [511, 2, 2, 0, "first_obs", "First observation to use when estimating model"], [511, 2, 2, 0, "last_obs", "Last observation to use when estimating model"], [511, 2, 2, 0, "options", "Options to pass to scipy.optimize.minimize.  Valid entries include 'ftol', 'eps', 'disp', and 'maxiter'."], [511, 2, 2, 0, "show_warning", "Flag indicating whether convergence warnings should be shown."], [511, 2, 2, 0, "starting_values", "Array of starting values to use.  If not provided, starting values are constructed by the model components."], [511, 2, 2, 0, "tol", "Tolerance for termination."], [511, 2, 2, 0, "update_freq", "Frequency of iteration updates.  Output is generated every update_freq iterations. Set to 0 to disable iterative output."]], "arch.univariate.ARX.fix": [[512, 2, 2, 0, "first_obs", "First observation to use when fixing model"], [512, 2, 2, 0, "last_obs", "Last observation to use when fixing model"], [512, 2, 2, 0, "params", "User specified parameters to use when generating the result. Must have the correct number of parameters for a given choice of mean model, volatility model and distribution."]], "arch.univariate.ARX.forecast": [[513, 2, 2, 0, "align", "Either 'origin' or 'target'.  When set of 'origin', the t-th row of forecasts contains the forecasts for t+1, t+2, ..., t+h. When set to 'target', the t-th row contains the 1-step ahead forecast from time t-1, the 2 step from time t-2, ..., and the h-step from time t-h.  'target' simplified computing forecast errors since the realization and h-step forecast are aligned."], [513, 2, 2, 0, "horizon", "Number of steps to forecast"], [513, 2, 2, 0, "method", "Method to use when producing the forecast. The default is analytic. The method only affects the variance forecast generation.  Not all volatility models support all methods. In particular, volatility models that do not evolve in squares such as EGARCH or TARCH do not support the 'analytic' method for horizons > 1."], [513, 2, 2, 0, "params", "Parameters required to forecast. Must be identical in shape to the parameters computed by fitting the model."], [513, 2, 2, 0, "random_state", "NumPy RandomState instance to use when method is 'bootstrap'"], [513, 2, 2, 0, "reindex", "Whether to reindex the forecasts to have the same dimension as the series being forecast. Prior to 4.18 this was the default. As of 4.19 this is now optional. If not provided, a warning is raised about the future change in the default which will occur after September 2021."], [513, 2, 2, 0, "rng", "Custom random number generator to use in simulation-based forecasts. Must produce random samples using the syntax rng(size) where size the 2-element tuple (simulations, horizon)."], [513, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [513, 2, 2, 0, "start", "An integer, datetime or str indicating the first observation to produce the forecast for.  Datetimes can only be used with pandas inputs that have a datetime index. Strings must be convertible to a date time, such as in '1945-01-01'."], [513, 2, 2, 0, "x", "Values to use for exogenous regressors if any are included in the model. Three formats are accepted:"]], "arch.univariate.ARX.resids": [[517, 2, 2, 0, "params", "Model parameters"], [517, 2, 2, 0, "regressors", "Alternative regressor values to use when computing model residuals"], [517, 2, 2, 0, "y", "Alternative values to use when computing model residuals"]], "arch.univariate.ARX.simulate": [[518, 2, 2, 0, "burn", "Number of values to simulate to initialize the model and remove dependence on initial values."], [518, 2, 2, 0, "initial_value", "Either a scalar value or max(lags) array set of initial values to use when initializing the model.  If omitted, 0.0 is used."], [518, 2, 2, 0, "initial_value_vol", "An array or scalar to use when initializing the volatility process."], [518, 2, 2, 0, "nobs", "Length of series to simulate"], [518, 2, 2, 0, "params", "Parameters to use when simulating the model.  Parameter order is [mean volatility distribution] where the parameters of the mean model are ordered [constant lag[0] lag[1] ... lag[p] ex[0] ... ex[k-1]] where lag[j] indicates the coefficient on the jth lag in the model and ex[j] is the coefficient on the jth exogenous variable."], [518, 2, 2, 0, "x", "nobs + burn by k array of exogenous variables to include in the simulation."]], "arch.univariate.ConstantMean.__init__": [[429, 2, 2, 0, "distribution", "Error distribution to use in the model"], [429, 2, 2, 0, "hold_back", "Number of observations at the start of the sample to exclude when estimating model parameters.  Used when comparing models with different lag lengths to estimate on the common sample."], [429, 2, 2, 0, "rescale", "Flag indicating whether to automatically rescale data if the scale of the data is likely to produce convergence issues when estimating model parameters. If False, the model is estimated on the data without transformation.  If True, than y is rescaled and the new scale is reported in the estimation results."], [429, 2, 2, 0, "volatility", "Volatility process to use in the model"], [429, 2, 2, 0, "y", "nobs element vector containing the dependent variable"]], "arch.univariate.ConstantMean": [[523, 3, 1, 0, "bounds", "Construct bounds for parameters to use in non-linear optimization"], [524, 3, 1, 0, "compute_param_cov", "Computes parameter covariances using numerical derivatives."], [525, 3, 1, 0, "constraints", "Construct linear constraint arrays  for use in non-linear optimization"], [526, 5, 1, 0, "distribution", "Set or gets the error distribution"], [527, 3, 1, 0, "fit", "Estimate model parameters"], [528, 3, 1, 0, "fix", "Allows an ARCHModelFixedResult to be constructed from fixed parameters."], [529, 3, 1, 0, "forecast", "Construct forecasts from estimated model"], [530, 5, 1, 0, "name", "The name of the model."], [531, 5, 1, 0, "num_params", "Returns the number of parameters"], [532, 3, 1, 0, "parameter_names", "List of parameters names"], [533, 3, 1, 0, "resids", "Compute model residuals"], [534, 3, 1, 0, "simulate", "Simulated data from a constant mean model"], [535, 3, 1, 0, "starting_values", "Returns starting values for the mean model, often the same as the values returned from fit"], [536, 5, 1, 0, "volatility", "Set or gets the volatility process"], [537, 5, 1, 0, "x", "Gets the value of the exogenous regressors in the model"], [538, 5, 1, 0, "y", "Returns the dependent variable"]], "arch.univariate.ConstantMean.compute_param_cov": [[524, 2, 2, 0, "backcast", "Value to use for pre-sample observations"], [524, 2, 2, 0, "params", "Model parameters"], [524, 2, 2, 0, "robust", "Flag indicating whether to use robust standard errors (True) or classic MLE (False)"]], "arch.univariate.ConstantMean.fit": [[527, 2, 2, 0, "backcast", "Value to use as backcast. Should be measure \\sigma^2_0 since model-specific non-linear transformations are applied to value before computing the variance recursions."], [527, 2, 2, 0, "cov_type", "Estimation method of parameter covariance.  Supported options are 'robust', which does not assume the Information Matrix Equality holds and 'classic' which does.  In the ARCH literature, 'robust' corresponds to Bollerslev-Wooldridge covariance estimator."], [527, 2, 2, 0, "disp", "Either 'final' to print optimization result or 'off' to display nothing. If using a boolean, False is \"off\" and True is \"final\""], [527, 2, 2, 0, "first_obs", "First observation to use when estimating model"], [527, 2, 2, 0, "last_obs", "Last observation to use when estimating model"], [527, 2, 2, 0, "options", "Options to pass to scipy.optimize.minimize.  Valid entries include 'ftol', 'eps', 'disp', and 'maxiter'."], [527, 2, 2, 0, "show_warning", "Flag indicating whether convergence warnings should be shown."], [527, 2, 2, 0, "starting_values", "Array of starting values to use.  If not provided, starting values are constructed by the model components."], [527, 2, 2, 0, "tol", "Tolerance for termination."], [527, 2, 2, 0, "update_freq", "Frequency of iteration updates.  Output is generated every update_freq iterations. Set to 0 to disable iterative output."]], "arch.univariate.ConstantMean.fix": [[528, 2, 2, 0, "first_obs", "First observation to use when fixing model"], [528, 2, 2, 0, "last_obs", "Last observation to use when fixing model"], [528, 2, 2, 0, "params", "User specified parameters to use when generating the result. Must have the correct number of parameters for a given choice of mean model, volatility model and distribution."]], "arch.univariate.ConstantMean.forecast": [[529, 2, 2, 0, "align", "Either 'origin' or 'target'.  When set of 'origin', the t-th row of forecasts contains the forecasts for t+1, t+2, ..., t+h. When set to 'target', the t-th row contains the 1-step ahead forecast from time t-1, the 2 step from time t-2, ..., and the h-step from time t-h.  'target' simplified computing forecast errors since the realization and h-step forecast are aligned."], [529, 2, 2, 0, "horizon", "Number of steps to forecast"], [529, 2, 2, 0, "method", "Method to use when producing the forecast. The default is analytic. The method only affects the variance forecast generation.  Not all volatility models support all methods. In particular, volatility models that do not evolve in squares such as EGARCH or TARCH do not support the 'analytic' method for horizons > 1."], [529, 2, 2, 0, "params", "Parameters required to forecast. Must be identical in shape to the parameters computed by fitting the model."], [529, 2, 2, 0, "random_state", "NumPy RandomState instance to use when method is 'bootstrap'"], [529, 2, 2, 0, "reindex", "Whether to reindex the forecasts to have the same dimension as the series being forecast. Prior to 4.18 this was the default. As of 4.19 this is now optional. If not provided, a warning is raised about the future change in the default which will occur after September 2021."], [529, 2, 2, 0, "rng", "Custom random number generator to use in simulation-based forecasts. Must produce random samples using the syntax rng(size) where size the 2-element tuple (simulations, horizon)."], [529, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [529, 2, 2, 0, "start", "An integer, datetime or str indicating the first observation to produce the forecast for.  Datetimes can only be used with pandas inputs that have a datetime index. Strings must be convertible to a date time, such as in '1945-01-01'."], [529, 2, 2, 0, "x", "Values to use for exogenous regressors if any are included in the model. Three formats are accepted:"]], "arch.univariate.ConstantMean.resids": [[533, 2, 2, 0, "params", "Model parameters"], [533, 2, 2, 0, "regressors", "Alternative regressor values to use when computing model residuals"], [533, 2, 2, 0, "y", "Alternative values to use when computing model residuals"]], "arch.univariate.ConstantMean.simulate": [[534, 2, 2, 0, "burn", "Number of values to simulate to initialize the model and remove dependence on initial values."], [534, 2, 2, 0, "initial_value", "This value is not used."], [534, 2, 2, 0, "initial_value_vol", "An array or scalar to use when initializing the volatility process."], [534, 2, 2, 0, "nobs", "Length of series to simulate"], [534, 2, 2, 0, "params", "Parameters to use when simulating the model.  Parameter order is [mean volatility distribution]. There is one parameter in the mean model, mu."], [534, 2, 2, 0, "x", "This value is not used."]], "arch.univariate.ConstantVariance": [[539, 3, 1, 0, "backcast", "Construct values for backcasting to start the recursion"], [540, 3, 1, 0, "backcast_transform", "Transformation to apply to user-provided backcast values"], [541, 3, 1, 0, "bounds", "Returns bounds for parameters"], [542, 3, 1, 0, "compute_variance", "Compute the variance for the ARCH model"], [543, 3, 1, 0, "constraints", "Construct parameter constraints arrays for parameter estimation"], [544, 3, 1, 0, "forecast", "Forecast volatility from the model"], [545, 5, 1, 0, "name", "The name of the volatility process"], [546, 5, 1, 0, "num_params", "The number of parameters in the model"], [547, 3, 1, 0, "parameter_names", "Names of model parameters"], [548, 3, 1, 0, "simulate", "Simulate data from the model"], [549, 5, 1, 0, "start", "Index to use to start variance subarray selection"], [550, 3, 1, 0, "starting_values", "Returns starting values for the ARCH model"], [551, 5, 1, 0, "stop", "Index to use to stop variance subarray selection"], [552, 3, 1, 0, "update", "Compute the variance for a single observation"], [553, 5, 1, 0, "updateable", "Flag indicating that the volatility process supports update"], [554, 3, 1, 0, "variance_bounds", "Construct loose bounds for conditional variances."], [555, 5, 1, 0, "volatility_updater", "Get the volatility updater associated with the volatility process"]], "arch.univariate.ConstantVariance.backcast": [[539, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.ConstantVariance.backcast_transform": [[540, 2, 2, 0, "backcast", "User-provided backcast that approximates sigma2[0]."]], "arch.univariate.ConstantVariance.bounds": [[541, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.ConstantVariance.compute_variance": [[542, 2, 2, 0, "backcast", "Value to use when initializing ARCH recursion. Can be an ndarray when the model contains multiple components."], [542, 2, 2, 0, "parameters", "Model parameters"], [542, 2, 2, 0, "resids", "Vector of mean zero residuals"], [542, 2, 2, 0, "sigma2", "Array with same size as resids to store the conditional variance"], [542, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.ConstantVariance.forecast": [[544, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [544, 2, 2, 0, "horizon", "Forecast horizon.  Must be 1 or larger.  Forecasts are produced for horizons in [1, horizon]."], [544, 2, 2, 0, "method", "Method to use when producing the forecast. The default is analytic."], [544, 2, 2, 0, "parameters", "Parameters required to forecast the volatility model"], [544, 2, 2, 0, "random_state", "NumPy RandomState instance to use when method is 'bootstrap'"], [544, 2, 2, 0, "resids", "Residuals to use in the recursion"], [544, 2, 2, 0, "rng", "Callable random number generator required if method is 'simulation'. Must take a single shape input and return random samples numbers with that shape."], [544, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [544, 2, 2, 0, "start", "Index of the first observation to use as the starting point for the forecast.  Default is len(resids)."], [544, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.ConstantVariance.simulate": [[548, 2, 2, 0, "burn", "Number of additional observations to generate when initializing the simulation"], [548, 2, 2, 0, "initial_value", "Scalar or array of initial values to use when initializing the simulation"], [548, 2, 2, 0, "nobs", "Number of data points to simulate"], [548, 2, 2, 0, "parameters", "Parameters required to simulate the volatility model"], [548, 2, 2, 0, "rng", "Callable function that takes a single integer input and returns a vector of random numbers"]], "arch.univariate.ConstantVariance.starting_values": [[550, 2, 2, 0, "resids", "Array of (approximate) residuals to use when computing starting values"]], "arch.univariate.ConstantVariance.update": [[552, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [552, 2, 2, 0, "index", "The numerical index of the variance to compute"], [552, 2, 2, 0, "parameters", "The variance model parameters"], [552, 2, 2, 0, "resids", "The residual array. Only uses resids[:index] when computing sigma2[index]"], [552, 2, 2, 0, "sigma2", "The array containing the variances. Only uses sigma2[:index] when computing sigma2[index]. The computed value is stored in sigma2[index]."], [552, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.ConstantVariance.variance_bounds": [[554, 2, 2, 0, "power", "Power used in the model. 2.0, the default corresponds to standard ARCH models that evolve in squares."], [554, 2, 2, 0, "resids", "Approximate residuals to use to compute the lower and upper bounds on the conditional variance"]], "arch.univariate.EGARCH.__init__": [[431, 2, 2, 0, "o", "Order of the asymmetric innovation"], [431, 2, 2, 0, "p", "Order of the symmetric innovation"], [431, 2, 2, 0, "q", "Order of the lagged (transformed) conditional variance"]], "arch.univariate.EGARCH": [[556, 3, 1, 0, "backcast", "Construct values for backcasting to start the recursion"], [557, 3, 1, 0, "backcast_transform", "Transformation to apply to user-provided backcast values"], [558, 3, 1, 0, "bounds", "Returns bounds for parameters"], [559, 3, 1, 0, "compute_variance", "Compute the variance for the ARCH model"], [560, 3, 1, 0, "constraints", "Construct parameter constraints arrays for parameter estimation"], [561, 3, 1, 0, "forecast", "Forecast volatility from the model"], [562, 5, 1, 0, "name", "The name of the volatility process"], [563, 5, 1, 0, "num_params", "The number of parameters in the model"], [564, 3, 1, 0, "parameter_names", "Names of model parameters"], [565, 3, 1, 0, "simulate", "Simulate data from the model"], [566, 5, 1, 0, "start", "Index to use to start variance subarray selection"], [567, 3, 1, 0, "starting_values", "Returns starting values for the ARCH model"], [568, 5, 1, 0, "stop", "Index to use to stop variance subarray selection"], [569, 3, 1, 0, "update", "Compute the variance for a single observation"], [570, 5, 1, 0, "updateable", "Flag indicating that the volatility process supports update"], [571, 3, 1, 0, "variance_bounds", "Construct loose bounds for conditional variances."], [572, 5, 1, 0, "volatility_updater", "Get the volatility updater associated with the volatility process"]], "arch.univariate.EGARCH.backcast": [[556, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.EGARCH.backcast_transform": [[557, 2, 2, 0, "backcast", "User-provided backcast that approximates sigma2[0]."]], "arch.univariate.EGARCH.bounds": [[558, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.EGARCH.compute_variance": [[559, 2, 2, 0, "backcast", "Value to use when initializing ARCH recursion. Can be an ndarray when the model contains multiple components."], [559, 2, 2, 0, "parameters", "Model parameters"], [559, 2, 2, 0, "resids", "Vector of mean zero residuals"], [559, 2, 2, 0, "sigma2", "Array with same size as resids to store the conditional variance"], [559, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.EGARCH.forecast": [[561, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [561, 2, 2, 0, "horizon", "Forecast horizon.  Must be 1 or larger.  Forecasts are produced for horizons in [1, horizon]."], [561, 2, 2, 0, "method", "Method to use when producing the forecast. The default is analytic."], [561, 2, 2, 0, "parameters", "Parameters required to forecast the volatility model"], [561, 2, 2, 0, "random_state", "NumPy RandomState instance to use when method is 'bootstrap'"], [561, 2, 2, 0, "resids", "Residuals to use in the recursion"], [561, 2, 2, 0, "rng", "Callable random number generator required if method is 'simulation'. Must take a single shape input and return random samples numbers with that shape."], [561, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [561, 2, 2, 0, "start", "Index of the first observation to use as the starting point for the forecast.  Default is len(resids)."], [561, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.EGARCH.simulate": [[565, 2, 2, 0, "burn", "Number of additional observations to generate when initializing the simulation"], [565, 2, 2, 0, "initial_value", "Scalar or array of initial values to use when initializing the simulation"], [565, 2, 2, 0, "nobs", "Number of data points to simulate"], [565, 2, 2, 0, "parameters", "Parameters required to simulate the volatility model"], [565, 2, 2, 0, "rng", "Callable function that takes a single integer input and returns a vector of random numbers"]], "arch.univariate.EGARCH.starting_values": [[567, 2, 2, 0, "resids", "Array of (approximate) residuals to use when computing starting values"]], "arch.univariate.EGARCH.update": [[569, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [569, 2, 2, 0, "index", "The numerical index of the variance to compute"], [569, 2, 2, 0, "parameters", "The variance model parameters"], [569, 2, 2, 0, "resids", "The residual array. Only uses resids[:index] when computing sigma2[index]"], [569, 2, 2, 0, "sigma2", "The array containing the variances. Only uses sigma2[:index] when computing sigma2[index]. The computed value is stored in sigma2[index]."], [569, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.EGARCH.variance_bounds": [[571, 2, 2, 0, "power", "Power used in the model. 2.0, the default corresponds to standard ARCH models that evolve in squares."], [571, 2, 2, 0, "resids", "Approximate residuals to use to compute the lower and upper bounds on the conditional variance"]], "arch.univariate.EWMAVariance.__init__": [[432, 2, 2, 0, "lam", "Smoothing parameter. Default is 0.94. Set to None to estimate lam jointly with other model parameters"]], "arch.univariate.EWMAVariance": [[573, 3, 1, 0, "backcast", "Construct values for backcasting to start the recursion"], [574, 3, 1, 0, "backcast_transform", "Transformation to apply to user-provided backcast values"], [575, 3, 1, 0, "bounds", "Returns bounds for parameters"], [576, 3, 1, 0, "compute_variance", "Compute the variance for the ARCH model"], [577, 3, 1, 0, "constraints", "Construct parameter constraints arrays for parameter estimation"], [578, 3, 1, 0, "forecast", "Forecast volatility from the model"], [579, 5, 1, 0, "name", "The name of the volatility process"], [580, 5, 1, 0, "num_params", "The number of parameters in the model"], [581, 3, 1, 0, "parameter_names", "Names of model parameters"], [582, 3, 1, 0, "simulate", "Simulate data from the model"], [583, 5, 1, 0, "start", "Index to use to start variance subarray selection"], [584, 3, 1, 0, "starting_values", "Returns starting values for the ARCH model"], [585, 5, 1, 0, "stop", "Index to use to stop variance subarray selection"], [586, 3, 1, 0, "update", "Compute the variance for a single observation"], [587, 5, 1, 0, "updateable", "Flag indicating that the volatility process supports update"], [588, 3, 1, 0, "variance_bounds", "Construct loose bounds for conditional variances."], [589, 5, 1, 0, "volatility_updater", "Get the volatility updater associated with the volatility process"]], "arch.univariate.EWMAVariance.backcast": [[573, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.EWMAVariance.backcast_transform": [[574, 2, 2, 0, "backcast", "User-provided backcast that approximates sigma2[0]."]], "arch.univariate.EWMAVariance.bounds": [[575, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.EWMAVariance.compute_variance": [[576, 2, 2, 0, "backcast", "Value to use when initializing ARCH recursion. Can be an ndarray when the model contains multiple components."], [576, 2, 2, 0, "parameters", "Model parameters"], [576, 2, 2, 0, "resids", "Vector of mean zero residuals"], [576, 2, 2, 0, "sigma2", "Array with same size as resids to store the conditional variance"], [576, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.EWMAVariance.forecast": [[578, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [578, 2, 2, 0, "horizon", "Forecast horizon.  Must be 1 or larger.  Forecasts are produced for horizons in [1, horizon]."], [578, 2, 2, 0, "method", "Method to use when producing the forecast. The default is analytic."], [578, 2, 2, 0, "parameters", "Parameters required to forecast the volatility model"], [578, 2, 2, 0, "random_state", "NumPy RandomState instance to use when method is 'bootstrap'"], [578, 2, 2, 0, "resids", "Residuals to use in the recursion"], [578, 2, 2, 0, "rng", "Callable random number generator required if method is 'simulation'. Must take a single shape input and return random samples numbers with that shape."], [578, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [578, 2, 2, 0, "start", "Index of the first observation to use as the starting point for the forecast.  Default is len(resids)."], [578, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.EWMAVariance.simulate": [[582, 2, 2, 0, "burn", "Number of additional observations to generate when initializing the simulation"], [582, 2, 2, 0, "initial_value", "Scalar or array of initial values to use when initializing the simulation"], [582, 2, 2, 0, "nobs", "Number of data points to simulate"], [582, 2, 2, 0, "parameters", "Parameters required to simulate the volatility model"], [582, 2, 2, 0, "rng", "Callable function that takes a single integer input and returns a vector of random numbers"]], "arch.univariate.EWMAVariance.starting_values": [[584, 2, 2, 0, "resids", "Array of (approximate) residuals to use when computing starting values"]], "arch.univariate.EWMAVariance.update": [[586, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [586, 2, 2, 0, "index", "The numerical index of the variance to compute"], [586, 2, 2, 0, "parameters", "The variance model parameters"], [586, 2, 2, 0, "resids", "The residual array. Only uses resids[:index] when computing sigma2[index]"], [586, 2, 2, 0, "sigma2", "The array containing the variances. Only uses sigma2[:index] when computing sigma2[index]. The computed value is stored in sigma2[index]."], [586, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.EWMAVariance.variance_bounds": [[588, 2, 2, 0, "power", "Power used in the model. 2.0, the default corresponds to standard ARCH models that evolve in squares."], [588, 2, 2, 0, "resids", "Approximate residuals to use to compute the lower and upper bounds on the conditional variance"]], "arch.univariate.FIGARCH.__init__": [[433, 2, 2, 0, "p", "Order of the symmetric innovation"], [433, 2, 2, 0, "power", "Power to use with the innovations, abs(e) ** power.  Default is 2.0, which produces FIGARCH and related models. Using 1.0 produces FIAVARCH and related models.  Other powers can be specified, although these should be strictly positive, and usually larger than 0.25."], [433, 2, 2, 0, "q", "Order of the lagged (transformed) conditional variance"], [433, 2, 2, 0, "truncation", "Truncation point to use in ARCH(\\infty) representation. Default is 1000."]], "arch.univariate.FIGARCH": [[590, 3, 1, 0, "backcast", "Construct values for backcasting to start the recursion"], [591, 3, 1, 0, "backcast_transform", "Transformation to apply to user-provided backcast values"], [592, 3, 1, 0, "bounds", "Returns bounds for parameters"], [593, 3, 1, 0, "compute_variance", "Compute the variance for the ARCH model"], [594, 3, 1, 0, "constraints", "Construct parameter constraints arrays for parameter estimation"], [595, 3, 1, 0, "forecast", "Forecast volatility from the model"], [596, 5, 1, 0, "name", "The name of the volatility process"], [597, 5, 1, 0, "num_params", "The number of parameters in the model"], [598, 3, 1, 0, "parameter_names", "Names of model parameters"], [599, 3, 1, 0, "simulate", "Simulate data from the model"], [600, 5, 1, 0, "start", "Index to use to start variance subarray selection"], [601, 3, 1, 0, "starting_values", "Returns starting values for the ARCH model"], [602, 5, 1, 0, "stop", "Index to use to stop variance subarray selection"], [603, 5, 1, 0, "truncation", "Truncation lag for the ARCH-infinity approximation"], [604, 3, 1, 0, "update", "Compute the variance for a single observation"], [605, 5, 1, 0, "updateable", "Flag indicating that the volatility process supports update"], [606, 3, 1, 0, "variance_bounds", "Construct loose bounds for conditional variances."], [607, 5, 1, 0, "volatility_updater", "Get the volatility updater associated with the volatility process"]], "arch.univariate.FIGARCH.backcast": [[590, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.FIGARCH.backcast_transform": [[591, 2, 2, 0, "backcast", "User-provided backcast that approximates sigma2[0]."]], "arch.univariate.FIGARCH.bounds": [[592, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.FIGARCH.compute_variance": [[593, 2, 2, 0, "backcast", "Value to use when initializing ARCH recursion. Can be an ndarray when the model contains multiple components."], [593, 2, 2, 0, "parameters", "Model parameters"], [593, 2, 2, 0, "resids", "Vector of mean zero residuals"], [593, 2, 2, 0, "sigma2", "Array with same size as resids to store the conditional variance"], [593, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.FIGARCH.forecast": [[595, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [595, 2, 2, 0, "horizon", "Forecast horizon.  Must be 1 or larger.  Forecasts are produced for horizons in [1, horizon]."], [595, 2, 2, 0, "method", "Method to use when producing the forecast. The default is analytic."], [595, 2, 2, 0, "parameters", "Parameters required to forecast the volatility model"], [595, 2, 2, 0, "random_state", "NumPy RandomState instance to use when method is 'bootstrap'"], [595, 2, 2, 0, "resids", "Residuals to use in the recursion"], [595, 2, 2, 0, "rng", "Callable random number generator required if method is 'simulation'. Must take a single shape input and return random samples numbers with that shape."], [595, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [595, 2, 2, 0, "start", "Index of the first observation to use as the starting point for the forecast.  Default is len(resids)."], [595, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.FIGARCH.simulate": [[599, 2, 2, 0, "burn", "Number of additional observations to generate when initializing the simulation"], [599, 2, 2, 0, "initial_value", "Scalar or array of initial values to use when initializing the simulation"], [599, 2, 2, 0, "nobs", "Number of data points to simulate"], [599, 2, 2, 0, "parameters", "Parameters required to simulate the volatility model"], [599, 2, 2, 0, "rng", "Callable function that takes a single integer input and returns a vector of random numbers"]], "arch.univariate.FIGARCH.starting_values": [[601, 2, 2, 0, "resids", "Array of (approximate) residuals to use when computing starting values"]], "arch.univariate.FIGARCH.update": [[604, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [604, 2, 2, 0, "index", "The numerical index of the variance to compute"], [604, 2, 2, 0, "parameters", "The variance model parameters"], [604, 2, 2, 0, "resids", "The residual array. Only uses resids[:index] when computing sigma2[index]"], [604, 2, 2, 0, "sigma2", "The array containing the variances. Only uses sigma2[:index] when computing sigma2[index]. The computed value is stored in sigma2[index]."], [604, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.FIGARCH.variance_bounds": [[606, 2, 2, 0, "power", "Power used in the model. 2.0, the default corresponds to standard ARCH models that evolve in squares."], [606, 2, 2, 0, "resids", "Approximate residuals to use to compute the lower and upper bounds on the conditional variance"]], "arch.univariate.FixedVariance.__init__": [[434, 2, 2, 0, "unit_scale", "Flag whether to enforce a unit scale.  If False, a scale parameter will be estimated so that the model variance will be proportional to variance. If True, the model variance is set of variance"], [434, 2, 2, 0, "variance", "Array containing the variances to use.  Should have the same shape as the data used in the model."]], "arch.univariate.FixedVariance": [[608, 3, 1, 0, "backcast", "Construct values for backcasting to start the recursion"], [609, 3, 1, 0, "backcast_transform", "Transformation to apply to user-provided backcast values"], [610, 3, 1, 0, "bounds", "Returns bounds for parameters"], [611, 3, 1, 0, "compute_variance", "Compute the variance for the ARCH model"], [612, 3, 1, 0, "constraints", "Construct parameter constraints arrays for parameter estimation"], [613, 3, 1, 0, "forecast", "Forecast volatility from the model"], [614, 5, 1, 0, "name", "The name of the volatility process"], [615, 5, 1, 0, "num_params", "The number of parameters in the model"], [616, 3, 1, 0, "parameter_names", "Names of model parameters"], [617, 3, 1, 0, "simulate", "Simulate data from the model"], [618, 5, 1, 0, "start", "Index to use to start variance subarray selection"], [619, 3, 1, 0, "starting_values", "Returns starting values for the ARCH model"], [620, 5, 1, 0, "stop", "Index to use to stop variance subarray selection"], [621, 3, 1, 0, "update", "Compute the variance for a single observation"], [622, 5, 1, 0, "updateable", "Flag indicating that the volatility process supports update"], [623, 3, 1, 0, "variance_bounds", "Construct loose bounds for conditional variances."], [624, 5, 1, 0, "volatility_updater", "Get the volatility updater associated with the volatility process"]], "arch.univariate.FixedVariance.backcast": [[608, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.FixedVariance.backcast_transform": [[609, 2, 2, 0, "backcast", "User-provided backcast that approximates sigma2[0]."]], "arch.univariate.FixedVariance.bounds": [[610, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.FixedVariance.compute_variance": [[611, 2, 2, 0, "backcast", "Value to use when initializing ARCH recursion. Can be an ndarray when the model contains multiple components."], [611, 2, 2, 0, "parameters", "Model parameters"], [611, 2, 2, 0, "resids", "Vector of mean zero residuals"], [611, 2, 2, 0, "sigma2", "Array with same size as resids to store the conditional variance"], [611, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.FixedVariance.forecast": [[613, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [613, 2, 2, 0, "horizon", "Forecast horizon.  Must be 1 or larger.  Forecasts are produced for horizons in [1, horizon]."], [613, 2, 2, 0, "method", "Method to use when producing the forecast. The default is analytic."], [613, 2, 2, 0, "parameters", "Parameters required to forecast the volatility model"], [613, 2, 2, 0, "random_state", "NumPy RandomState instance to use when method is 'bootstrap'"], [613, 2, 2, 0, "resids", "Residuals to use in the recursion"], [613, 2, 2, 0, "rng", "Callable random number generator required if method is 'simulation'. Must take a single shape input and return random samples numbers with that shape."], [613, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [613, 2, 2, 0, "start", "Index of the first observation to use as the starting point for the forecast.  Default is len(resids)."], [613, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.FixedVariance.simulate": [[617, 2, 2, 0, "burn", "Number of additional observations to generate when initializing the simulation"], [617, 2, 2, 0, "initial_value", "Scalar or array of initial values to use when initializing the simulation"], [617, 2, 2, 0, "nobs", "Number of data points to simulate"], [617, 2, 2, 0, "parameters", "Parameters required to simulate the volatility model"], [617, 2, 2, 0, "rng", "Callable function that takes a single integer input and returns a vector of random numbers"]], "arch.univariate.FixedVariance.starting_values": [[619, 2, 2, 0, "resids", "Array of (approximate) residuals to use when computing starting values"]], "arch.univariate.FixedVariance.update": [[621, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [621, 2, 2, 0, "index", "The numerical index of the variance to compute"], [621, 2, 2, 0, "parameters", "The variance model parameters"], [621, 2, 2, 0, "resids", "The residual array. Only uses resids[:index] when computing sigma2[index]"], [621, 2, 2, 0, "sigma2", "The array containing the variances. Only uses sigma2[:index] when computing sigma2[index]. The computed value is stored in sigma2[index]."], [621, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.FixedVariance.variance_bounds": [[623, 2, 2, 0, "power", "Power used in the model. 2.0, the default corresponds to standard ARCH models that evolve in squares."], [623, 2, 2, 0, "resids", "Approximate residuals to use to compute the lower and upper bounds on the conditional variance"]], "arch.univariate.GARCH.__init__": [[435, 2, 2, 0, "o", "Order of the asymmetric innovation"], [435, 2, 2, 0, "p", "Order of the symmetric innovation"], [435, 2, 2, 0, "power", "Power to use with the innovations, abs(e) ** power.  Default is 2.0, which produces ARCH and related models. Using 1.0 produces AVARCH and related models.  Other powers can be specified, although these should be strictly positive, and usually larger than 0.25."], [435, 2, 2, 0, "q", "Order of the lagged (transformed) conditional variance"]], "arch.univariate.GARCH": [[625, 3, 1, 0, "backcast", "Construct values for backcasting to start the recursion"], [626, 3, 1, 0, "backcast_transform", "Transformation to apply to user-provided backcast values"], [627, 3, 1, 0, "bounds", "Returns bounds for parameters"], [628, 3, 1, 0, "compute_variance", "Compute the variance for the ARCH model"], [629, 3, 1, 0, "constraints", "Construct parameter constraints arrays for parameter estimation"], [630, 3, 1, 0, "forecast", "Forecast volatility from the model"], [631, 5, 1, 0, "name", "The name of the volatility process"], [632, 5, 1, 0, "num_params", "The number of parameters in the model"], [633, 3, 1, 0, "parameter_names", "Names of model parameters"], [634, 3, 1, 0, "simulate", "Simulate data from the model"], [635, 5, 1, 0, "start", "Index to use to start variance subarray selection"], [636, 3, 1, 0, "starting_values", "Returns starting values for the ARCH model"], [637, 5, 1, 0, "stop", "Index to use to stop variance subarray selection"], [638, 3, 1, 0, "update", "Compute the variance for a single observation"], [639, 5, 1, 0, "updateable", "Flag indicating that the volatility process supports update"], [640, 3, 1, 0, "variance_bounds", "Construct loose bounds for conditional variances."], [641, 5, 1, 0, "volatility_updater", "Get the volatility updater associated with the volatility process"]], "arch.univariate.GARCH.backcast": [[625, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.GARCH.backcast_transform": [[626, 2, 2, 0, "backcast", "User-provided backcast that approximates sigma2[0]."]], "arch.univariate.GARCH.bounds": [[627, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.GARCH.compute_variance": [[628, 2, 2, 0, "backcast", "Value to use when initializing ARCH recursion. Can be an ndarray when the model contains multiple components."], [628, 2, 2, 0, "parameters", "Model parameters"], [628, 2, 2, 0, "resids", "Vector of mean zero residuals"], [628, 2, 2, 0, "sigma2", "Array with same size as resids to store the conditional variance"], [628, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.GARCH.forecast": [[630, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [630, 2, 2, 0, "horizon", "Forecast horizon.  Must be 1 or larger.  Forecasts are produced for horizons in [1, horizon]."], [630, 2, 2, 0, "method", "Method to use when producing the forecast. The default is analytic."], [630, 2, 2, 0, "parameters", "Parameters required to forecast the volatility model"], [630, 2, 2, 0, "random_state", "NumPy RandomState instance to use when method is 'bootstrap'"], [630, 2, 2, 0, "resids", "Residuals to use in the recursion"], [630, 2, 2, 0, "rng", "Callable random number generator required if method is 'simulation'. Must take a single shape input and return random samples numbers with that shape."], [630, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [630, 2, 2, 0, "start", "Index of the first observation to use as the starting point for the forecast.  Default is len(resids)."], [630, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.GARCH.simulate": [[634, 2, 2, 0, "burn", "Number of additional observations to generate when initializing the simulation"], [634, 2, 2, 0, "initial_value", "Scalar or array of initial values to use when initializing the simulation"], [634, 2, 2, 0, "nobs", "Number of data points to simulate"], [634, 2, 2, 0, "parameters", "Parameters required to simulate the volatility model"], [634, 2, 2, 0, "rng", "Callable function that takes a single integer input and returns a vector of random numbers"]], "arch.univariate.GARCH.starting_values": [[636, 2, 2, 0, "resids", "Array of (approximate) residuals to use when computing starting values"]], "arch.univariate.GARCH.update": [[638, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [638, 2, 2, 0, "index", "The numerical index of the variance to compute"], [638, 2, 2, 0, "parameters", "The variance model parameters"], [638, 2, 2, 0, "resids", "The residual array. Only uses resids[:index] when computing sigma2[index]"], [638, 2, 2, 0, "sigma2", "The array containing the variances. Only uses sigma2[:index] when computing sigma2[index]. The computed value is stored in sigma2[index]."], [638, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.GARCH.variance_bounds": [[640, 2, 2, 0, "power", "Power used in the model. 2.0, the default corresponds to standard ARCH models that evolve in squares."], [640, 2, 2, 0, "resids", "Approximate residuals to use to compute the lower and upper bounds on the conditional variance"]], "arch.univariate.GeneralizedError.__init__": [[436, 2, 2, 0, "random_state", "Deprecated since version 5.0: random_state is deprecated. Use seed instead."], [436, 2, 2, 0, "seed", "Random number generator instance or int to use. Set to ensure reproducibility. If using an int, the argument is passed to np.random.default_rng.  If not provided, default_rng is used with system-provided entropy."]], "arch.univariate.GeneralizedError": [[642, 3, 1, 0, "bounds", "Parameter bounds for use in optimization."], [643, 3, 1, 0, "cdf", "Cumulative distribution function"], [644, 3, 1, 0, "constraints", "Construct arrays to use in constrained optimization."], [645, 5, 1, 0, "generator", "The NumPy Generator or RandomState attached to the distribution"], [646, 3, 1, 0, "loglikelihood", "Computes the log-likelihood of assuming residuals are have a Generalized Error Distribution, conditional on the variance."], [647, 3, 1, 0, "moment", "Moment of order n"], [648, 5, 1, 0, "name", "The name of the distribution"], [649, 3, 1, 0, "parameter_names", "Names of distribution shape parameters"], [650, 3, 1, 0, "partial_moment", "Order n lower partial moment from -inf to z"], [651, 3, 1, 0, "ppf", "Inverse cumulative density function (ICDF)"], [652, 5, 1, 0, "random_state", "The NumPy RandomState attached to the distribution"], [653, 3, 1, 0, "simulate", "Simulates i.i.d. draws from the distribution"], [654, 3, 1, 0, "starting_values", "Construct starting values for use in optimization."]], "arch.univariate.GeneralizedError.bounds": [[642, 2, 2, 0, "resids", "Residuals to use when computing the bounds"]], "arch.univariate.GeneralizedError.cdf": [[643, 2, 2, 0, "parameters", "Distribution parameters. Use None for parameterless distributions."], [643, 2, 2, 0, "resids", "Values at which to evaluate the cdf"]], "arch.univariate.GeneralizedError.loglikelihood": [[646, 2, 2, 0, "individual", "Flag indicating whether to return the vector of individual log likelihoods (True) or the sum (False)"], [646, 2, 2, 0, "parameters", "Shape parameter of the GED distribution"], [646, 2, 2, 0, "resids", "The residuals to use in the log-likelihood calculation"], [646, 2, 2, 0, "sigma2", "Conditional variances of resids"]], "arch.univariate.GeneralizedError.moment": [[647, 2, 2, 0, "n", "Order of moment"], [647, 2, 2, 0, "parameters", "Distribution parameters. Use None for parameterless distributions."]], "arch.univariate.GeneralizedError.partial_moment": [[650, 2, 2, 0, "n", "Order of partial moment"], [650, 2, 2, 0, "parameters", "Distribution parameters.  Use None for parameterless distributions."], [650, 2, 2, 0, "z", "Upper bound for partial moment integral"]], "arch.univariate.GeneralizedError.ppf": [[651, 2, 2, 0, "parameters", "Distribution parameters. Use None for parameterless distributions."], [651, 2, 2, 0, "pits", "Probability-integral-transformed values in the interval (0, 1)."]], "arch.univariate.GeneralizedError.simulate": [[653, 2, 2, 0, "parameters", "Distribution parameters"]], "arch.univariate.GeneralizedError.starting_values": [[654, 2, 2, 0, "std_resid", "Estimated standardized residuals to use in computing starting values for the shape parameter"]], "arch.univariate.HARCH.__init__": [[437, 2, 2, 0, "lags", "List of lags to include in the model, or if scalar, includes all lags up the value"]], "arch.univariate.HARCH": [[655, 3, 1, 0, "backcast", "Construct values for backcasting to start the recursion"], [656, 3, 1, 0, "backcast_transform", "Transformation to apply to user-provided backcast values"], [657, 3, 1, 0, "bounds", "Returns bounds for parameters"], [658, 3, 1, 0, "compute_variance", "Compute the variance for the ARCH model"], [659, 3, 1, 0, "constraints", "Construct parameter constraints arrays for parameter estimation"], [660, 3, 1, 0, "forecast", "Forecast volatility from the model"], [661, 5, 1, 0, "name", "The name of the volatility process"], [662, 5, 1, 0, "num_params", "The number of parameters in the model"], [663, 3, 1, 0, "parameter_names", "Names of model parameters"], [664, 3, 1, 0, "simulate", "Simulate data from the model"], [665, 5, 1, 0, "start", "Index to use to start variance subarray selection"], [666, 3, 1, 0, "starting_values", "Returns starting values for the ARCH model"], [667, 5, 1, 0, "stop", "Index to use to stop variance subarray selection"], [668, 3, 1, 0, "update", "Compute the variance for a single observation"], [669, 5, 1, 0, "updateable", "Flag indicating that the volatility process supports update"], [670, 3, 1, 0, "variance_bounds", "Construct loose bounds for conditional variances."], [671, 5, 1, 0, "volatility_updater", "Get the volatility updater associated with the volatility process"]], "arch.univariate.HARCH.backcast": [[655, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.HARCH.backcast_transform": [[656, 2, 2, 0, "backcast", "User-provided backcast that approximates sigma2[0]."]], "arch.univariate.HARCH.bounds": [[657, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.HARCH.compute_variance": [[658, 2, 2, 0, "backcast", "Value to use when initializing ARCH recursion. Can be an ndarray when the model contains multiple components."], [658, 2, 2, 0, "parameters", "Model parameters"], [658, 2, 2, 0, "resids", "Vector of mean zero residuals"], [658, 2, 2, 0, "sigma2", "Array with same size as resids to store the conditional variance"], [658, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.HARCH.forecast": [[660, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [660, 2, 2, 0, "horizon", "Forecast horizon.  Must be 1 or larger.  Forecasts are produced for horizons in [1, horizon]."], [660, 2, 2, 0, "method", "Method to use when producing the forecast. The default is analytic."], [660, 2, 2, 0, "parameters", "Parameters required to forecast the volatility model"], [660, 2, 2, 0, "random_state", "NumPy RandomState instance to use when method is 'bootstrap'"], [660, 2, 2, 0, "resids", "Residuals to use in the recursion"], [660, 2, 2, 0, "rng", "Callable random number generator required if method is 'simulation'. Must take a single shape input and return random samples numbers with that shape."], [660, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [660, 2, 2, 0, "start", "Index of the first observation to use as the starting point for the forecast.  Default is len(resids)."], [660, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.HARCH.simulate": [[664, 2, 2, 0, "burn", "Number of additional observations to generate when initializing the simulation"], [664, 2, 2, 0, "initial_value", "Scalar or array of initial values to use when initializing the simulation"], [664, 2, 2, 0, "nobs", "Number of data points to simulate"], [664, 2, 2, 0, "parameters", "Parameters required to simulate the volatility model"], [664, 2, 2, 0, "rng", "Callable function that takes a single integer input and returns a vector of random numbers"]], "arch.univariate.HARCH.starting_values": [[666, 2, 2, 0, "resids", "Array of (approximate) residuals to use when computing starting values"]], "arch.univariate.HARCH.update": [[668, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [668, 2, 2, 0, "index", "The numerical index of the variance to compute"], [668, 2, 2, 0, "parameters", "The variance model parameters"], [668, 2, 2, 0, "resids", "The residual array. Only uses resids[:index] when computing sigma2[index]"], [668, 2, 2, 0, "sigma2", "The array containing the variances. Only uses sigma2[:index] when computing sigma2[index]. The computed value is stored in sigma2[index]."], [668, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.HARCH.variance_bounds": [[670, 2, 2, 0, "power", "Power used in the model. 2.0, the default corresponds to standard ARCH models that evolve in squares."], [670, 2, 2, 0, "resids", "Approximate residuals to use to compute the lower and upper bounds on the conditional variance"]], "arch.univariate.HARX.__init__": [[438, 2, 2, 0, "constant", "Flag whether the model should include a constant"], [438, 2, 2, 0, "distribution", "Error distribution to use in the model"], [438, 2, 2, 0, "hold_back", "Number of observations at the start of the sample to exclude when estimating model parameters.  Used when comparing models with different lag lengths to estimate on the common sample."], [438, 2, 2, 0, "lags", "Description of lag structure of the HAR."], [438, 2, 2, 0, "rescale", "Flag indicating whether to automatically rescale data if the scale of the data is likely to produce convergence issues when estimating model parameters. If False, the model is estimated on the data without transformation.  If True, than y is rescaled and the new scale is reported in the estimation results."], [438, 2, 2, 0, "use_rotated", "Flag indicating to use the alternative rotated form of the HAR where HAR lags do not overlap"], [438, 2, 2, 0, "volatility", "Volatility process to use in the model"], [438, 2, 2, 0, "x", "nobs by k element array containing exogenous regressors"], [438, 2, 2, 0, "y", "nobs element vector containing the dependent variable"]], "arch.univariate.HARX": [[672, 3, 1, 0, "bounds", "Construct bounds for parameters to use in non-linear optimization"], [673, 3, 1, 0, "compute_param_cov", "Computes parameter covariances using numerical derivatives."], [674, 3, 1, 0, "constraints", "Construct linear constraint arrays  for use in non-linear optimization"], [675, 5, 1, 0, "distribution", "Set or gets the error distribution"], [676, 3, 1, 0, "fit", "Estimate model parameters"], [677, 3, 1, 0, "fix", "Allows an ARCHModelFixedResult to be constructed from fixed parameters."], [678, 3, 1, 0, "forecast", "Construct forecasts from estimated model"], [679, 5, 1, 0, "name", "The name of the model."], [680, 5, 1, 0, "num_params", "Returns the number of parameters"], [681, 3, 1, 0, "parameter_names", "List of parameters names"], [682, 3, 1, 0, "resids", "Compute model residuals"], [683, 3, 1, 0, "simulate", "Simulates data from a linear regression, AR or HAR models"], [684, 3, 1, 0, "starting_values", "Returns starting values for the mean model, often the same as the values returned from fit"], [685, 5, 1, 0, "volatility", "Set or gets the volatility process"], [686, 5, 1, 0, "x", "Gets the value of the exogenous regressors in the model"], [687, 5, 1, 0, "y", "Returns the dependent variable"]], "arch.univariate.HARX.compute_param_cov": [[673, 2, 2, 0, "backcast", "Value to use for pre-sample observations"], [673, 2, 2, 0, "params", "Model parameters"], [673, 2, 2, 0, "robust", "Flag indicating whether to use robust standard errors (True) or classic MLE (False)"]], "arch.univariate.HARX.fit": [[676, 2, 2, 0, "backcast", "Value to use as backcast. Should be measure \\sigma^2_0 since model-specific non-linear transformations are applied to value before computing the variance recursions."], [676, 2, 2, 0, "cov_type", "Estimation method of parameter covariance.  Supported options are 'robust', which does not assume the Information Matrix Equality holds and 'classic' which does.  In the ARCH literature, 'robust' corresponds to Bollerslev-Wooldridge covariance estimator."], [676, 2, 2, 0, "disp", "Either 'final' to print optimization result or 'off' to display nothing. If using a boolean, False is \"off\" and True is \"final\""], [676, 2, 2, 0, "first_obs", "First observation to use when estimating model"], [676, 2, 2, 0, "last_obs", "Last observation to use when estimating model"], [676, 2, 2, 0, "options", "Options to pass to scipy.optimize.minimize.  Valid entries include 'ftol', 'eps', 'disp', and 'maxiter'."], [676, 2, 2, 0, "show_warning", "Flag indicating whether convergence warnings should be shown."], [676, 2, 2, 0, "starting_values", "Array of starting values to use.  If not provided, starting values are constructed by the model components."], [676, 2, 2, 0, "tol", "Tolerance for termination."], [676, 2, 2, 0, "update_freq", "Frequency of iteration updates.  Output is generated every update_freq iterations. Set to 0 to disable iterative output."]], "arch.univariate.HARX.fix": [[677, 2, 2, 0, "first_obs", "First observation to use when fixing model"], [677, 2, 2, 0, "last_obs", "Last observation to use when fixing model"], [677, 2, 2, 0, "params", "User specified parameters to use when generating the result. Must have the correct number of parameters for a given choice of mean model, volatility model and distribution."]], "arch.univariate.HARX.forecast": [[678, 2, 2, 0, "align", "Either 'origin' or 'target'.  When set of 'origin', the t-th row of forecasts contains the forecasts for t+1, t+2, ..., t+h. When set to 'target', the t-th row contains the 1-step ahead forecast from time t-1, the 2 step from time t-2, ..., and the h-step from time t-h.  'target' simplified computing forecast errors since the realization and h-step forecast are aligned."], [678, 2, 2, 0, "horizon", "Number of steps to forecast"], [678, 2, 2, 0, "method", "Method to use when producing the forecast. The default is analytic. The method only affects the variance forecast generation.  Not all volatility models support all methods. In particular, volatility models that do not evolve in squares such as EGARCH or TARCH do not support the 'analytic' method for horizons > 1."], [678, 2, 2, 0, "params", "Parameters required to forecast. Must be identical in shape to the parameters computed by fitting the model."], [678, 2, 2, 0, "random_state", "NumPy RandomState instance to use when method is 'bootstrap'"], [678, 2, 2, 0, "reindex", "Whether to reindex the forecasts to have the same dimension as the series being forecast. Prior to 4.18 this was the default. As of 4.19 this is now optional. If not provided, a warning is raised about the future change in the default which will occur after September 2021."], [678, 2, 2, 0, "rng", "Custom random number generator to use in simulation-based forecasts. Must produce random samples using the syntax rng(size) where size the 2-element tuple (simulations, horizon)."], [678, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [678, 2, 2, 0, "start", "An integer, datetime or str indicating the first observation to produce the forecast for.  Datetimes can only be used with pandas inputs that have a datetime index. Strings must be convertible to a date time, such as in '1945-01-01'."], [678, 2, 2, 0, "x", "Values to use for exogenous regressors if any are included in the model. Three formats are accepted:"]], "arch.univariate.HARX.resids": [[682, 2, 2, 0, "params", "Model parameters"], [682, 2, 2, 0, "regressors", "Alternative regressor values to use when computing model residuals"], [682, 2, 2, 0, "y", "Alternative values to use when computing model residuals"]], "arch.univariate.HARX.simulate": [[683, 2, 2, 0, "burn", "Number of values to simulate to initialize the model and remove dependence on initial values."], [683, 2, 2, 0, "initial_value", "Either a scalar value or max(lags) array set of initial values to use when initializing the model.  If omitted, 0.0 is used."], [683, 2, 2, 0, "initial_value_vol", "An array or scalar to use when initializing the volatility process."], [683, 2, 2, 0, "nobs", "Length of series to simulate"], [683, 2, 2, 0, "params", "Parameters to use when simulating the model.  Parameter order is [mean volatility distribution] where the parameters of the mean model are ordered [constant lag[0] lag[1] ... lag[p] ex[0] ... ex[k-1]] where lag[j] indicates the coefficient on the jth lag in the model and ex[j] is the coefficient on the jth exogenous variable."], [683, 2, 2, 0, "x", "nobs + burn by k array of exogenous variables to include in the simulation."]], "arch.univariate.LS.__init__": [[439, 2, 2, 0, "constant", "Flag whether the model should include a constant"], [439, 2, 2, 0, "distribution", "Error distribution to use in the model"], [439, 2, 2, 0, "hold_back", "Number of observations at the start of the sample to exclude when estimating model parameters.  Used when comparing models with different lag lengths to estimate on the common sample."], [439, 2, 2, 0, "rescale", "Flag indicating whether to automatically rescale data if the scale of the data is likely to produce convergence issues when estimating model parameters. If False, the model is estimated on the data without transformation.  If True, than y is rescaled and the new scale is reported in the estimation results."], [439, 2, 2, 0, "volatility", "Volatility process to use in the model"], [439, 2, 2, 0, "y", "nobs by k element array containing exogenous regressors"]], "arch.univariate.LS": [[688, 3, 1, 0, "bounds", "Construct bounds for parameters to use in non-linear optimization"], [689, 3, 1, 0, "compute_param_cov", "Computes parameter covariances using numerical derivatives."], [690, 3, 1, 0, "constraints", "Construct linear constraint arrays  for use in non-linear optimization"], [691, 5, 1, 0, "distribution", "Set or gets the error distribution"], [692, 3, 1, 0, "fit", "Estimate model parameters"], [693, 3, 1, 0, "fix", "Allows an ARCHModelFixedResult to be constructed from fixed parameters."], [694, 3, 1, 0, "forecast", "Construct forecasts from estimated model"], [695, 5, 1, 0, "name", "The name of the model."], [696, 5, 1, 0, "num_params", "Returns the number of parameters"], [697, 3, 1, 0, "parameter_names", "List of parameters names"], [698, 3, 1, 0, "resids", "Compute model residuals"], [699, 3, 1, 0, "simulate", "Simulates data from a linear regression, AR or HAR models"], [700, 3, 1, 0, "starting_values", "Returns starting values for the mean model, often the same as the values returned from fit"], [701, 5, 1, 0, "volatility", "Set or gets the volatility process"], [702, 5, 1, 0, "x", "Gets the value of the exogenous regressors in the model"], [703, 5, 1, 0, "y", "Returns the dependent variable"]], "arch.univariate.LS.compute_param_cov": [[689, 2, 2, 0, "backcast", "Value to use for pre-sample observations"], [689, 2, 2, 0, "params", "Model parameters"], [689, 2, 2, 0, "robust", "Flag indicating whether to use robust standard errors (True) or classic MLE (False)"]], "arch.univariate.LS.fit": [[692, 2, 2, 0, "backcast", "Value to use as backcast. Should be measure \\sigma^2_0 since model-specific non-linear transformations are applied to value before computing the variance recursions."], [692, 2, 2, 0, "cov_type", "Estimation method of parameter covariance.  Supported options are 'robust', which does not assume the Information Matrix Equality holds and 'classic' which does.  In the ARCH literature, 'robust' corresponds to Bollerslev-Wooldridge covariance estimator."], [692, 2, 2, 0, "disp", "Either 'final' to print optimization result or 'off' to display nothing. If using a boolean, False is \"off\" and True is \"final\""], [692, 2, 2, 0, "first_obs", "First observation to use when estimating model"], [692, 2, 2, 0, "last_obs", "Last observation to use when estimating model"], [692, 2, 2, 0, "options", "Options to pass to scipy.optimize.minimize.  Valid entries include 'ftol', 'eps', 'disp', and 'maxiter'."], [692, 2, 2, 0, "show_warning", "Flag indicating whether convergence warnings should be shown."], [692, 2, 2, 0, "starting_values", "Array of starting values to use.  If not provided, starting values are constructed by the model components."], [692, 2, 2, 0, "tol", "Tolerance for termination."], [692, 2, 2, 0, "update_freq", "Frequency of iteration updates.  Output is generated every update_freq iterations. Set to 0 to disable iterative output."]], "arch.univariate.LS.fix": [[693, 2, 2, 0, "first_obs", "First observation to use when fixing model"], [693, 2, 2, 0, "last_obs", "Last observation to use when fixing model"], [693, 2, 2, 0, "params", "User specified parameters to use when generating the result. Must have the correct number of parameters for a given choice of mean model, volatility model and distribution."]], "arch.univariate.LS.forecast": [[694, 2, 2, 0, "align", "Either 'origin' or 'target'.  When set of 'origin', the t-th row of forecasts contains the forecasts for t+1, t+2, ..., t+h. When set to 'target', the t-th row contains the 1-step ahead forecast from time t-1, the 2 step from time t-2, ..., and the h-step from time t-h.  'target' simplified computing forecast errors since the realization and h-step forecast are aligned."], [694, 2, 2, 0, "horizon", "Number of steps to forecast"], [694, 2, 2, 0, "method", "Method to use when producing the forecast. The default is analytic. The method only affects the variance forecast generation.  Not all volatility models support all methods. In particular, volatility models that do not evolve in squares such as EGARCH or TARCH do not support the 'analytic' method for horizons > 1."], [694, 2, 2, 0, "params", "Parameters required to forecast. Must be identical in shape to the parameters computed by fitting the model."], [694, 2, 2, 0, "random_state", "NumPy RandomState instance to use when method is 'bootstrap'"], [694, 2, 2, 0, "reindex", "Whether to reindex the forecasts to have the same dimension as the series being forecast. Prior to 4.18 this was the default. As of 4.19 this is now optional. If not provided, a warning is raised about the future change in the default which will occur after September 2021."], [694, 2, 2, 0, "rng", "Custom random number generator to use in simulation-based forecasts. Must produce random samples using the syntax rng(size) where size the 2-element tuple (simulations, horizon)."], [694, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [694, 2, 2, 0, "start", "An integer, datetime or str indicating the first observation to produce the forecast for.  Datetimes can only be used with pandas inputs that have a datetime index. Strings must be convertible to a date time, such as in '1945-01-01'."], [694, 2, 2, 0, "x", "Values to use for exogenous regressors if any are included in the model. Three formats are accepted:"]], "arch.univariate.LS.resids": [[698, 2, 2, 0, "params", "Model parameters"], [698, 2, 2, 0, "regressors", "Alternative regressor values to use when computing model residuals"], [698, 2, 2, 0, "y", "Alternative values to use when computing model residuals"]], "arch.univariate.LS.simulate": [[699, 2, 2, 0, "burn", "Number of values to simulate to initialize the model and remove dependence on initial values."], [699, 2, 2, 0, "initial_value", "Either a scalar value or max(lags) array set of initial values to use when initializing the model.  If omitted, 0.0 is used."], [699, 2, 2, 0, "initial_value_vol", "An array or scalar to use when initializing the volatility process."], [699, 2, 2, 0, "nobs", "Length of series to simulate"], [699, 2, 2, 0, "params", "Parameters to use when simulating the model.  Parameter order is [mean volatility distribution] where the parameters of the mean model are ordered [constant lag[0] lag[1] ... lag[p] ex[0] ... ex[k-1]] where lag[j] indicates the coefficient on the jth lag in the model and ex[j] is the coefficient on the jth exogenous variable."], [699, 2, 2, 0, "x", "nobs + burn by k array of exogenous variables to include in the simulation."]], "arch.univariate.MIDASHyperbolic.__init__": [[440, 2, 2, 0, "asym", "Flag indicating whether to include an asymmetric term"], [440, 2, 2, 0, "m", "Length of maximum lag to include in the model"]], "arch.univariate.MIDASHyperbolic": [[704, 3, 1, 0, "backcast", "Construct values for backcasting to start the recursion"], [705, 3, 1, 0, "backcast_transform", "Transformation to apply to user-provided backcast values"], [706, 3, 1, 0, "bounds", "Returns bounds for parameters"], [707, 3, 1, 0, "compute_variance", "Compute the variance for the ARCH model"], [708, 3, 1, 0, "constraints", "Constraints"], [709, 3, 1, 0, "forecast", "Forecast volatility from the model"], [710, 5, 1, 0, "name", "The name of the volatility process"], [711, 5, 1, 0, "num_params", "The number of parameters in the model"], [712, 3, 1, 0, "parameter_names", "Names of model parameters"], [713, 3, 1, 0, "simulate", "Simulate data from the model"], [714, 5, 1, 0, "start", "Index to use to start variance subarray selection"], [715, 3, 1, 0, "starting_values", "Returns starting values for the ARCH model"], [716, 5, 1, 0, "stop", "Index to use to stop variance subarray selection"], [717, 3, 1, 0, "update", "Compute the variance for a single observation"], [718, 5, 1, 0, "updateable", "Flag indicating that the volatility process supports update"], [719, 3, 1, 0, "variance_bounds", "Construct loose bounds for conditional variances."], [720, 5, 1, 0, "volatility_updater", "Get the volatility updater associated with the volatility process"]], "arch.univariate.MIDASHyperbolic.backcast": [[704, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.MIDASHyperbolic.backcast_transform": [[705, 2, 2, 0, "backcast", "User-provided backcast that approximates sigma2[0]."]], "arch.univariate.MIDASHyperbolic.bounds": [[706, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.MIDASHyperbolic.compute_variance": [[707, 2, 2, 0, "backcast", "Value to use when initializing ARCH recursion. Can be an ndarray when the model contains multiple components."], [707, 2, 2, 0, "parameters", "Model parameters"], [707, 2, 2, 0, "resids", "Vector of mean zero residuals"], [707, 2, 2, 0, "sigma2", "Array with same size as resids to store the conditional variance"], [707, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.MIDASHyperbolic.forecast": [[709, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [709, 2, 2, 0, "horizon", "Forecast horizon.  Must be 1 or larger.  Forecasts are produced for horizons in [1, horizon]."], [709, 2, 2, 0, "method", "Method to use when producing the forecast. The default is analytic."], [709, 2, 2, 0, "parameters", "Parameters required to forecast the volatility model"], [709, 2, 2, 0, "random_state", "NumPy RandomState instance to use when method is 'bootstrap'"], [709, 2, 2, 0, "resids", "Residuals to use in the recursion"], [709, 2, 2, 0, "rng", "Callable random number generator required if method is 'simulation'. Must take a single shape input and return random samples numbers with that shape."], [709, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [709, 2, 2, 0, "start", "Index of the first observation to use as the starting point for the forecast.  Default is len(resids)."], [709, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.MIDASHyperbolic.simulate": [[713, 2, 2, 0, "burn", "Number of additional observations to generate when initializing the simulation"], [713, 2, 2, 0, "initial_value", "Scalar or array of initial values to use when initializing the simulation"], [713, 2, 2, 0, "nobs", "Number of data points to simulate"], [713, 2, 2, 0, "parameters", "Parameters required to simulate the volatility model"], [713, 2, 2, 0, "rng", "Callable function that takes a single integer input and returns a vector of random numbers"]], "arch.univariate.MIDASHyperbolic.starting_values": [[715, 2, 2, 0, "resids", "Array of (approximate) residuals to use when computing starting values"]], "arch.univariate.MIDASHyperbolic.update": [[717, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [717, 2, 2, 0, "index", "The numerical index of the variance to compute"], [717, 2, 2, 0, "parameters", "The variance model parameters"], [717, 2, 2, 0, "resids", "The residual array. Only uses resids[:index] when computing sigma2[index]"], [717, 2, 2, 0, "sigma2", "The array containing the variances. Only uses sigma2[:index] when computing sigma2[index]. The computed value is stored in sigma2[index]."], [717, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.MIDASHyperbolic.variance_bounds": [[719, 2, 2, 0, "power", "Power used in the model. 2.0, the default corresponds to standard ARCH models that evolve in squares."], [719, 2, 2, 0, "resids", "Approximate residuals to use to compute the lower and upper bounds on the conditional variance"]], "arch.univariate.Normal.__init__": [[441, 2, 2, 0, "random_state", "Deprecated since version 5.0: random_state is deprecated. Use seed instead."], [441, 2, 2, 0, "seed", "Random number generator instance or int to use. Set to ensure reproducibility. If using an int, the argument is passed to np.random.default_rng.  If not provided, default_rng is used with system-provided entropy."]], "arch.univariate.Normal": [[721, 3, 1, 0, "bounds", "Parameter bounds for use in optimization."], [722, 3, 1, 0, "cdf", "Cumulative distribution function"], [723, 3, 1, 0, "constraints", "Construct arrays to use in constrained optimization."], [724, 5, 1, 0, "generator", "The NumPy Generator or RandomState attached to the distribution"], [725, 3, 1, 0, "loglikelihood", "Computes the log-likelihood of assuming residuals are normally distributed, conditional on the variance"], [726, 3, 1, 0, "moment", "Moment of order n"], [727, 5, 1, 0, "name", "The name of the distribution"], [728, 3, 1, 0, "parameter_names", "Names of distribution shape parameters"], [729, 3, 1, 0, "partial_moment", "Order n lower partial moment from -inf to z"], [730, 3, 1, 0, "ppf", "Inverse cumulative density function (ICDF)"], [731, 5, 1, 0, "random_state", "The NumPy RandomState attached to the distribution"], [732, 3, 1, 0, "simulate", "Simulates i.i.d. draws from the distribution"], [733, 3, 1, 0, "starting_values", "Construct starting values for use in optimization."]], "arch.univariate.Normal.bounds": [[721, 2, 2, 0, "resids", "Residuals to use when computing the bounds"]], "arch.univariate.Normal.cdf": [[722, 2, 2, 0, "parameters", "Distribution parameters. Use None for parameterless distributions."], [722, 2, 2, 0, "resids", "Values at which to evaluate the cdf"]], "arch.univariate.Normal.loglikelihood": [[725, 2, 2, 0, "individual", "Flag indicating whether to return the vector of individual log likelihoods (True) or the sum (False)"], [725, 2, 2, 0, "parameters", "The normal likelihood has no shape parameters. Empty since the standard normal has no shape parameters."], [725, 2, 2, 0, "resids", "The residuals to use in the log-likelihood calculation"], [725, 2, 2, 0, "sigma2", "Conditional variances of resids"]], "arch.univariate.Normal.moment": [[726, 2, 2, 0, "n", "Order of moment"], [726, 2, 2, 0, "parameters", "Distribution parameters. Use None for parameterless distributions."]], "arch.univariate.Normal.partial_moment": [[729, 2, 2, 0, "n", "Order of partial moment"], [729, 2, 2, 0, "parameters", "Distribution parameters.  Use None for parameterless distributions."], [729, 2, 2, 0, "z", "Upper bound for partial moment integral"]], "arch.univariate.Normal.ppf": [[730, 2, 2, 0, "parameters", "Distribution parameters. Use None for parameterless distributions."], [730, 2, 2, 0, "pits", "Probability-integral-transformed values in the interval (0, 1)."]], "arch.univariate.Normal.simulate": [[732, 2, 2, 0, "parameters", "Distribution parameters"]], "arch.univariate.Normal.starting_values": [[733, 2, 2, 0, "std_resid", "Estimated standardized residuals to use in computing starting values for the shape parameter"]], "arch.univariate.RiskMetrics2006.__init__": [[442, 2, 2, 0, "kmax", "Number of components. Default is 14."], [442, 2, 2, 0, "rho", "Relative scale of adjacent cycles. Default is sqrt(2)"], [442, 2, 2, 0, "tau0", "Length of long cycle. Default is 1560."], [442, 2, 2, 0, "tau1", "Length of short cycle. Default is 4."]], "arch.univariate.RiskMetrics2006": [[734, 3, 1, 0, "backcast", "Construct values for backcasting to start the recursion"], [735, 3, 1, 0, "backcast_transform", "Transformation to apply to user-provided backcast values"], [736, 3, 1, 0, "bounds", "Returns bounds for parameters"], [737, 3, 1, 0, "compute_variance", "Compute the variance for the ARCH model"], [738, 3, 1, 0, "constraints", "Construct parameter constraints arrays for parameter estimation"], [739, 3, 1, 0, "forecast", "Forecast volatility from the model"], [740, 5, 1, 0, "name", "The name of the volatility process"], [741, 5, 1, 0, "num_params", "The number of parameters in the model"], [742, 3, 1, 0, "parameter_names", "Names of model parameters"], [743, 3, 1, 0, "simulate", "Simulate data from the model"], [744, 5, 1, 0, "start", "Index to use to start variance subarray selection"], [745, 3, 1, 0, "starting_values", "Returns starting values for the ARCH model"], [746, 5, 1, 0, "stop", "Index to use to stop variance subarray selection"], [747, 3, 1, 0, "update", "Compute the variance for a single observation"], [748, 5, 1, 0, "updateable", "Flag indicating that the volatility process supports update"], [749, 3, 1, 0, "variance_bounds", "Construct loose bounds for conditional variances."], [750, 5, 1, 0, "volatility_updater", "Get the volatility updater associated with the volatility process"]], "arch.univariate.RiskMetrics2006.backcast": [[734, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.RiskMetrics2006.backcast_transform": [[735, 2, 2, 0, "backcast", "User-provided backcast that approximates sigma2[0]."]], "arch.univariate.RiskMetrics2006.bounds": [[736, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.RiskMetrics2006.compute_variance": [[737, 2, 2, 0, "backcast", "Value to use when initializing ARCH recursion. Can be an ndarray when the model contains multiple components."], [737, 2, 2, 0, "parameters", "Model parameters"], [737, 2, 2, 0, "resids", "Vector of mean zero residuals"], [737, 2, 2, 0, "sigma2", "Array with same size as resids to store the conditional variance"], [737, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.RiskMetrics2006.forecast": [[739, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [739, 2, 2, 0, "horizon", "Forecast horizon.  Must be 1 or larger.  Forecasts are produced for horizons in [1, horizon]."], [739, 2, 2, 0, "method", "Method to use when producing the forecast. The default is analytic."], [739, 2, 2, 0, "parameters", "Parameters required to forecast the volatility model"], [739, 2, 2, 0, "random_state", "NumPy RandomState instance to use when method is 'bootstrap'"], [739, 2, 2, 0, "resids", "Residuals to use in the recursion"], [739, 2, 2, 0, "rng", "Callable random number generator required if method is 'simulation'. Must take a single shape input and return random samples numbers with that shape."], [739, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [739, 2, 2, 0, "start", "Index of the first observation to use as the starting point for the forecast.  Default is len(resids)."], [739, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.RiskMetrics2006.simulate": [[743, 2, 2, 0, "burn", "Number of additional observations to generate when initializing the simulation"], [743, 2, 2, 0, "initial_value", "Scalar or array of initial values to use when initializing the simulation"], [743, 2, 2, 0, "nobs", "Number of data points to simulate"], [743, 2, 2, 0, "parameters", "Parameters required to simulate the volatility model"], [743, 2, 2, 0, "rng", "Callable function that takes a single integer input and returns a vector of random numbers"]], "arch.univariate.RiskMetrics2006.starting_values": [[745, 2, 2, 0, "resids", "Array of (approximate) residuals to use when computing starting values"]], "arch.univariate.RiskMetrics2006.update": [[747, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [747, 2, 2, 0, "index", "The numerical index of the variance to compute"], [747, 2, 2, 0, "parameters", "The variance model parameters"], [747, 2, 2, 0, "resids", "The residual array. Only uses resids[:index] when computing sigma2[index]"], [747, 2, 2, 0, "sigma2", "The array containing the variances. Only uses sigma2[:index] when computing sigma2[index]. The computed value is stored in sigma2[index]."], [747, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.RiskMetrics2006.variance_bounds": [[749, 2, 2, 0, "power", "Power used in the model. 2.0, the default corresponds to standard ARCH models that evolve in squares."], [749, 2, 2, 0, "resids", "Approximate residuals to use to compute the lower and upper bounds on the conditional variance"]], "arch.univariate.SkewStudent.__init__": [[443, 2, 2, 0, "random_state", "Deprecated since version 5.0: random_state is deprecated. Use seed instead."], [443, 2, 2, 0, "seed", "Random number generator instance or int to use. Set to ensure reproducibility. If using an int, the argument is passed to np.random.default_rng.  If not provided, default_rng is used with system-provided entropy."]], "arch.univariate.SkewStudent": [[751, 3, 1, 0, "bounds", "Parameter bounds for use in optimization."], [752, 3, 1, 0, "cdf", "Cumulative distribution function"], [753, 3, 1, 0, "constraints", "Construct arrays to use in constrained optimization."], [754, 5, 1, 0, "generator", "The NumPy Generator or RandomState attached to the distribution"], [755, 3, 1, 0, "loglikelihood", "Computes the log-likelihood of assuming residuals are have a standardized (to have unit variance) Skew Student's t distribution, conditional on the variance."], [756, 3, 1, 0, "moment", "Moment of order n"], [757, 5, 1, 0, "name", "The name of the distribution"], [758, 3, 1, 0, "parameter_names", "Names of distribution shape parameters"], [759, 3, 1, 0, "partial_moment", "Order n lower partial moment from -inf to z"], [760, 3, 1, 0, "ppf", "Inverse cumulative density function (ICDF)"], [761, 5, 1, 0, "random_state", "The NumPy RandomState attached to the distribution"], [762, 3, 1, 0, "simulate", "Simulates i.i.d. draws from the distribution"], [763, 3, 1, 0, "starting_values", "Construct starting values for use in optimization."]], "arch.univariate.SkewStudent.bounds": [[751, 2, 2, 0, "resids", "Residuals to use when computing the bounds"]], "arch.univariate.SkewStudent.cdf": [[752, 2, 2, 0, "parameters", "Distribution parameters. Use None for parameterless distributions."], [752, 2, 2, 0, "resids", "Values at which to evaluate the cdf"]], "arch.univariate.SkewStudent.loglikelihood": [[755, 2, 2, 0, "individual", "Flag indicating whether to return the vector of individual log likelihoods (True) or the sum (False)"], [755, 2, 2, 0, "parameters", "Shape parameter of the skew-t distribution"], [755, 2, 2, 0, "resids", "The residuals to use in the log-likelihood calculation"], [755, 2, 2, 0, "sigma2", "Conditional variances of resids"]], "arch.univariate.SkewStudent.moment": [[756, 2, 2, 0, "n", "Order of moment"], [756, 2, 2, 0, "parameters", "Distribution parameters. Use None for parameterless distributions."]], "arch.univariate.SkewStudent.partial_moment": [[759, 2, 2, 0, "n", "Order of partial moment"], [759, 2, 2, 0, "parameters", "Distribution parameters.  Use None for parameterless distributions."], [759, 2, 2, 0, "z", "Upper bound for partial moment integral"]], "arch.univariate.SkewStudent.ppf": [[760, 2, 2, 0, "parameters", "Distribution parameters. Use None for parameterless distributions."], [760, 2, 2, 0, "pits", "Probability-integral-transformed values in the interval (0, 1)."]], "arch.univariate.SkewStudent.simulate": [[762, 2, 2, 0, "parameters", "Distribution parameters"]], "arch.univariate.SkewStudent.starting_values": [[763, 2, 2, 0, "std_resid", "Estimated standardized residuals to use in computing starting values for the shape parameter"]], "arch.univariate.StudentsT.__init__": [[444, 2, 2, 0, "random_state", "Deprecated since version 5.0: random_state is deprecated. Use seed instead."], [444, 2, 2, 0, "seed", "Random number generator instance or int to use. Set to ensure reproducibility. If using an int, the argument is passed to np.random.default_rng.  If not provided, default_rng is used with system-provided entropy."]], "arch.univariate.StudentsT": [[764, 3, 1, 0, "bounds", "Parameter bounds for use in optimization."], [765, 3, 1, 0, "cdf", "Cumulative distribution function"], [766, 3, 1, 0, "constraints", "Construct arrays to use in constrained optimization."], [767, 5, 1, 0, "generator", "The NumPy Generator or RandomState attached to the distribution"], [768, 3, 1, 0, "loglikelihood", "Computes the log-likelihood of assuming residuals are have a standardized (to have unit variance) Student's t distribution, conditional on the variance."], [769, 3, 1, 0, "moment", "Moment of order n"], [770, 5, 1, 0, "name", "The name of the distribution"], [771, 3, 1, 0, "parameter_names", "Names of distribution shape parameters"], [772, 3, 1, 0, "partial_moment", "Order n lower partial moment from -inf to z"], [773, 3, 1, 0, "ppf", "Inverse cumulative density function (ICDF)"], [774, 5, 1, 0, "random_state", "The NumPy RandomState attached to the distribution"], [775, 3, 1, 0, "simulate", "Simulates i.i.d. draws from the distribution"], [776, 3, 1, 0, "starting_values", "Construct starting values for use in optimization."]], "arch.univariate.StudentsT.bounds": [[764, 2, 2, 0, "resids", "Residuals to use when computing the bounds"]], "arch.univariate.StudentsT.cdf": [[765, 2, 2, 0, "parameters", "Distribution parameters. Use None for parameterless distributions."], [765, 2, 2, 0, "resids", "Values at which to evaluate the cdf"]], "arch.univariate.StudentsT.loglikelihood": [[768, 2, 2, 0, "individual", "Flag indicating whether to return the vector of individual log likelihoods (True) or the sum (False)"], [768, 2, 2, 0, "parameters", "Shape parameter of the t distribution"], [768, 2, 2, 0, "resids", "The residuals to use in the log-likelihood calculation"], [768, 2, 2, 0, "sigma2", "Conditional variances of resids"]], "arch.univariate.StudentsT.moment": [[769, 2, 2, 0, "n", "Order of moment"], [769, 2, 2, 0, "parameters", "Distribution parameters. Use None for parameterless distributions."]], "arch.univariate.StudentsT.partial_moment": [[772, 2, 2, 0, "n", "Order of partial moment"], [772, 2, 2, 0, "parameters", "Distribution parameters.  Use None for parameterless distributions."], [772, 2, 2, 0, "z", "Upper bound for partial moment integral"]], "arch.univariate.StudentsT.ppf": [[773, 2, 2, 0, "parameters", "Distribution parameters. Use None for parameterless distributions."], [773, 2, 2, 0, "pits", "Probability-integral-transformed values in the interval (0, 1)."]], "arch.univariate.StudentsT.simulate": [[775, 2, 2, 0, "parameters", "Distribution parameters"]], "arch.univariate.StudentsT.starting_values": [[776, 2, 2, 0, "std_resid", "Estimated standardized residuals to use in computing starting values for the shape parameter"]], "arch.univariate.ZeroMean.__init__": [[445, 2, 2, 0, "distribution", "Error distribution to use in the model"], [445, 2, 2, 0, "hold_back", "Number of observations at the start of the sample to exclude when estimating model parameters.  Used when comparing models with different lag lengths to estimate on the common sample."], [445, 2, 2, 0, "rescale", "Flag indicating whether to automatically rescale data if the scale of the data is likely to produce convergence issues when estimating model parameters. If False, the model is estimated on the data without transformation.  If True, than y is rescaled and the new scale is reported in the estimation results."], [445, 2, 2, 0, "volatility", "Volatility process to use in the model"], [445, 2, 2, 0, "y", "nobs element vector containing the dependent variable"]], "arch.univariate.ZeroMean": [[777, 3, 1, 0, "bounds", "Construct bounds for parameters to use in non-linear optimization"], [778, 3, 1, 0, "compute_param_cov", "Computes parameter covariances using numerical derivatives."], [779, 3, 1, 0, "constraints", "Construct linear constraint arrays  for use in non-linear optimization"], [780, 5, 1, 0, "distribution", "Set or gets the error distribution"], [781, 3, 1, 0, "fit", "Estimate model parameters"], [782, 3, 1, 0, "fix", "Allows an ARCHModelFixedResult to be constructed from fixed parameters."], [783, 3, 1, 0, "forecast", "Construct forecasts from estimated model"], [784, 5, 1, 0, "name", "The name of the model."], [785, 5, 1, 0, "num_params", "Returns the number of parameters"], [786, 3, 1, 0, "parameter_names", "List of parameters names"], [787, 3, 1, 0, "resids", "Compute model residuals"], [788, 3, 1, 0, "simulate", "Simulated data from a zero mean model"], [789, 3, 1, 0, "starting_values", "Returns starting values for the mean model, often the same as the values returned from fit"], [790, 5, 1, 0, "volatility", "Set or gets the volatility process"], [791, 5, 1, 0, "x", "Gets the value of the exogenous regressors in the model"], [792, 5, 1, 0, "y", "Returns the dependent variable"]], "arch.univariate.ZeroMean.compute_param_cov": [[778, 2, 2, 0, "backcast", "Value to use for pre-sample observations"], [778, 2, 2, 0, "params", "Model parameters"], [778, 2, 2, 0, "robust", "Flag indicating whether to use robust standard errors (True) or classic MLE (False)"]], "arch.univariate.ZeroMean.fit": [[781, 2, 2, 0, "backcast", "Value to use as backcast. Should be measure \\sigma^2_0 since model-specific non-linear transformations are applied to value before computing the variance recursions."], [781, 2, 2, 0, "cov_type", "Estimation method of parameter covariance.  Supported options are 'robust', which does not assume the Information Matrix Equality holds and 'classic' which does.  In the ARCH literature, 'robust' corresponds to Bollerslev-Wooldridge covariance estimator."], [781, 2, 2, 0, "disp", "Either 'final' to print optimization result or 'off' to display nothing. If using a boolean, False is \"off\" and True is \"final\""], [781, 2, 2, 0, "first_obs", "First observation to use when estimating model"], [781, 2, 2, 0, "last_obs", "Last observation to use when estimating model"], [781, 2, 2, 0, "options", "Options to pass to scipy.optimize.minimize.  Valid entries include 'ftol', 'eps', 'disp', and 'maxiter'."], [781, 2, 2, 0, "show_warning", "Flag indicating whether convergence warnings should be shown."], [781, 2, 2, 0, "starting_values", "Array of starting values to use.  If not provided, starting values are constructed by the model components."], [781, 2, 2, 0, "tol", "Tolerance for termination."], [781, 2, 2, 0, "update_freq", "Frequency of iteration updates.  Output is generated every update_freq iterations. Set to 0 to disable iterative output."]], "arch.univariate.ZeroMean.fix": [[782, 2, 2, 0, "first_obs", "First observation to use when fixing model"], [782, 2, 2, 0, "last_obs", "Last observation to use when fixing model"], [782, 2, 2, 0, "params", "User specified parameters to use when generating the result. Must have the correct number of parameters for a given choice of mean model, volatility model and distribution."]], "arch.univariate.ZeroMean.forecast": [[783, 2, 2, 0, "align", "Either 'origin' or 'target'.  When set of 'origin', the t-th row of forecasts contains the forecasts for t+1, t+2, ..., t+h. When set to 'target', the t-th row contains the 1-step ahead forecast from time t-1, the 2 step from time t-2, ..., and the h-step from time t-h.  'target' simplified computing forecast errors since the realization and h-step forecast are aligned."], [783, 2, 2, 0, "horizon", "Number of steps to forecast"], [783, 2, 2, 0, "method", "Method to use when producing the forecast. The default is analytic. The method only affects the variance forecast generation.  Not all volatility models support all methods. In particular, volatility models that do not evolve in squares such as EGARCH or TARCH do not support the 'analytic' method for horizons > 1."], [783, 2, 2, 0, "params", "Parameters required to forecast. Must be identical in shape to the parameters computed by fitting the model."], [783, 2, 2, 0, "random_state", "NumPy RandomState instance to use when method is 'bootstrap'"], [783, 2, 2, 0, "reindex", "Whether to reindex the forecasts to have the same dimension as the series being forecast. Prior to 4.18 this was the default. As of 4.19 this is now optional. If not provided, a warning is raised about the future change in the default which will occur after September 2021."], [783, 2, 2, 0, "rng", "Custom random number generator to use in simulation-based forecasts. Must produce random samples using the syntax rng(size) where size the 2-element tuple (simulations, horizon)."], [783, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [783, 2, 2, 0, "start", "An integer, datetime or str indicating the first observation to produce the forecast for.  Datetimes can only be used with pandas inputs that have a datetime index. Strings must be convertible to a date time, such as in '1945-01-01'."], [783, 2, 2, 0, "x", "Values to use for exogenous regressors if any are included in the model. Three formats are accepted:"]], "arch.univariate.ZeroMean.resids": [[787, 2, 2, 0, "params", "Model parameters"], [787, 2, 2, 0, "regressors", "Alternative regressor values to use when computing model residuals"], [787, 2, 2, 0, "y", "Alternative values to use when computing model residuals"]], "arch.univariate.ZeroMean.simulate": [[788, 2, 2, 0, "burn", "Number of values to simulate to initialize the model and remove dependence on initial values."], [788, 2, 2, 0, "initial_value", "This value is not used."], [788, 2, 2, 0, "initial_value_vol", "An array or scalar to use when initializing the volatility process."], [788, 2, 2, 0, "nobs", "Length of series to simulate"], [788, 2, 2, 0, "params", "Parameters to use when simulating the model.  Parameter order is [volatility distribution]. There are no mean parameters."], [788, 2, 2, 0, "x", "This value is not used."]], "arch.univariate.arch_model": [[891, 2, 2, 0, "dist", "Name of the error distribution.  Currently supported options are:"], [891, 2, 2, 0, "hold_back", "Number of observations at the start of the sample to exclude when estimating model parameters.  Used when comparing models with different lag lengths to estimate on the common sample."], [891, 2, 2, 0, "lags", "Either a scalar integer value indicating lag length or a list of integers specifying lag locations."], [891, 2, 2, 0, "mean", "Name of the mean model.  Currently supported options are: 'Constant', 'Zero', 'LS', 'AR', 'ARX', 'HAR' and  'HARX'"], [891, 2, 2, 0, "o", "Lag order of the asymmetric innovation"], [891, 2, 2, 0, "p", "Lag order of the symmetric innovation"], [891, 2, 2, 0, "power", "Power to use with GARCH and related models"], [891, 2, 2, 0, "q", "Lag order of lagged volatility or equivalent"], [891, 2, 2, 0, "rescale", "Flag indicating whether to automatically rescale data if the scale of the data is likely to produce convergence issues when estimating model parameters. If False, the model is estimated on the data without transformation.  If True, than y is rescaled and the new scale is reported in the estimation results."], [891, 2, 2, 0, "vol", "Name of the volatility model.  Currently supported options are: 'GARCH' (default), 'ARCH', 'EGARCH', 'FIGARCH', 'APARCH' and 'HARCH'"], [891, 2, 2, 0, "x", "Exogenous regressors.  Ignored if model does not permit exogenous regressors."], [891, 2, 2, 0, "y", "The dependent variable"]], "arch.univariate.base": [[446, 1, 1, 0, "ARCHModel", "Abstract base class for mean models in ARCH processes.  Specifies the conditional mean process."], [447, 1, 1, 0, "ARCHModelFixedResult", "Results for fixed parameters for an ARCHModel model"], [448, 1, 1, 0, "ARCHModelForecast", "Container for forecasts from an ARCH Model"], [449, 1, 1, 0, "ARCHModelForecastSimulation", "Container for a simulation or bootstrap-based forecasts from an ARCH Model"], [450, 1, 1, 0, "ARCHModelResult", "Results from estimation of an ARCHModel model"]], "arch.univariate.base.ARCHModel": [[793, 3, 1, 0, "bounds", "Construct bounds for parameters to use in non-linear optimization"], [794, 3, 1, 0, "compute_param_cov", "Computes parameter covariances using numerical derivatives."], [795, 3, 1, 0, "constraints", "Construct linear constraint arrays  for use in non-linear optimization"], [796, 5, 1, 0, "distribution", "Set or gets the error distribution"], [797, 3, 1, 0, "fit", "Estimate model parameters"], [798, 3, 1, 0, "fix", "Allows an ARCHModelFixedResult to be constructed from fixed parameters."], [799, 3, 1, 0, "forecast", "Construct forecasts from estimated model"], [800, 5, 1, 0, "name", "The name of the model."], [801, 5, 1, 0, "num_params", "Number of parameters in the model"], [802, 3, 1, 0, "parameter_names", "List of parameters names"], [803, 3, 1, 0, "resids", "Compute model residuals"], [804, 3, 1, 0, "simulate", ""], [805, 3, 1, 0, "starting_values", "Returns starting values for the mean model, often the same as the values returned from fit"], [806, 5, 1, 0, "volatility", "Set or gets the volatility process"], [807, 5, 1, 0, "y", "Returns the dependent variable"]], "arch.univariate.base.ARCHModel.compute_param_cov": [[794, 2, 2, 0, "backcast", "Value to use for pre-sample observations"], [794, 2, 2, 0, "params", "Model parameters"], [794, 2, 2, 0, "robust", "Flag indicating whether to use robust standard errors (True) or classic MLE (False)"]], "arch.univariate.base.ARCHModel.fit": [[797, 2, 2, 0, "backcast", "Value to use as backcast. Should be measure \\sigma^2_0 since model-specific non-linear transformations are applied to value before computing the variance recursions."], [797, 2, 2, 0, "cov_type", "Estimation method of parameter covariance.  Supported options are 'robust', which does not assume the Information Matrix Equality holds and 'classic' which does.  In the ARCH literature, 'robust' corresponds to Bollerslev-Wooldridge covariance estimator."], [797, 2, 2, 0, "disp", "Either 'final' to print optimization result or 'off' to display nothing. If using a boolean, False is \"off\" and True is \"final\""], [797, 2, 2, 0, "first_obs", "First observation to use when estimating model"], [797, 2, 2, 0, "last_obs", "Last observation to use when estimating model"], [797, 2, 2, 0, "options", "Options to pass to scipy.optimize.minimize.  Valid entries include 'ftol', 'eps', 'disp', and 'maxiter'."], [797, 2, 2, 0, "show_warning", "Flag indicating whether convergence warnings should be shown."], [797, 2, 2, 0, "starting_values", "Array of starting values to use.  If not provided, starting values are constructed by the model components."], [797, 2, 2, 0, "tol", "Tolerance for termination."], [797, 2, 2, 0, "update_freq", "Frequency of iteration updates.  Output is generated every update_freq iterations. Set to 0 to disable iterative output."]], "arch.univariate.base.ARCHModel.fix": [[798, 2, 2, 0, "first_obs", "First observation to use when fixing model"], [798, 2, 2, 0, "last_obs", "Last observation to use when fixing model"], [798, 2, 2, 0, "params", "User specified parameters to use when generating the result. Must have the correct number of parameters for a given choice of mean model, volatility model and distribution."]], "arch.univariate.base.ARCHModel.forecast": [[799, 2, 2, 0, "align", "Either 'origin' or 'target'.  When set of 'origin', the t-th row of forecasts contains the forecasts for t+1, t+2, ..., t+h. When set to 'target', the t-th row contains the 1-step ahead forecast from time t-1, the 2 step from time t-2, ..., and the h-step from time t-h.  'target' simplified computing forecast errors since the realization and h-step forecast are aligned."], [799, 2, 2, 0, "horizon", "Number of steps to forecast"], [799, 2, 2, 0, "method", "Method to use when producing the forecast. The default is analytic. The method only affects the variance forecast generation.  Not all volatility models support all methods. In particular, volatility models that do not evolve in squares such as EGARCH or TARCH do not support the 'analytic' method for horizons > 1."], [799, 2, 2, 0, "params", "Parameters required to forecast. Must be identical in shape to the parameters computed by fitting the model."], [799, 2, 2, 0, "random_state", "NumPy RandomState instance to use when method is 'bootstrap'"], [799, 2, 2, 0, "reindex", "Whether to reindex the forecasts to have the same dimension as the series being forecast. Prior to 4.18 this was the default. As of 4.19 this is now optional. If not provided, a warning is raised about the future change in the default which will occur after September 2021."], [799, 2, 2, 0, "rng", "Custom random number generator to use in simulation-based forecasts. Must produce random samples using the syntax rng(size) where size the 2-element tuple (simulations, horizon)."], [799, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [799, 2, 2, 0, "start", "An integer, datetime or str indicating the first observation to produce the forecast for.  Datetimes can only be used with pandas inputs that have a datetime index. Strings must be convertible to a date time, such as in '1945-01-01'."], [799, 2, 2, 0, "x", "Values to use for exogenous regressors if any are included in the model. Three formats are accepted:"]], "arch.univariate.base.ARCHModel.resids": [[803, 2, 2, 0, "params", "Model parameters"], [803, 2, 2, 0, "regressors", "Alternative regressor values to use when computing model residuals"], [803, 2, 2, 0, "y", "Alternative values to use when computing model residuals"]], "arch.univariate.base.ARCHModelFixedResult.__init__": [[447, 2, 2, 0, "dep_var", "Dependent variable"], [447, 2, 2, 0, "is_pandas", "Whether the original input was pandas"], [447, 2, 2, 0, "loglikelihood", "Loglikelihood at specified parameters"], [447, 2, 2, 0, "model", "The model object used to estimate the parameters"], [447, 2, 2, 0, "names", "Model parameter names"], [447, 2, 2, 0, "params", "Estimated parameters"], [447, 2, 2, 0, "resid", "Residuals from model.  Residuals have same shape as original data and contain nan-values in locations not used in estimation"], [447, 2, 2, 0, "volatility", "Conditional volatility from model"]], "arch.univariate.base.ARCHModelFixedResult": [[808, 5, 1, 0, "aic", "Akaike Information Criteria"], [809, 3, 1, 0, "arch_lm_test", "ARCH LM test for conditional heteroskedasticity"], [810, 5, 1, 0, "bic", "Schwarz/Bayesian Information Criteria"], [811, 5, 1, 0, "conditional_volatility", "Estimated conditional volatility"], [812, 3, 1, 0, "forecast", "Construct forecasts from estimated model"], [813, 3, 1, 0, "hedgehog_plot", "Plot forecasts from estimated model"], [814, 5, 1, 0, "loglikelihood", "Model loglikelihood"], [815, 5, 1, 0, "model", "Model instance used to produce the fit"], [816, 5, 1, 0, "nobs", "Number of data points used to estimate model"], [817, 5, 1, 0, "num_params", "Number of parameters in model"], [818, 5, 1, 0, "params", "Model Parameters"], [819, 3, 1, 0, "plot", "Plot standardized residuals and conditional volatility"], [820, 5, 1, 0, "resid", "Model residuals"], [821, 5, 1, 0, "std_resid", "Residuals standardized by conditional volatility"], [822, 3, 1, 0, "summary", "Constructs a summary of the results from a fit model."]], "arch.univariate.base.ARCHModelFixedResult.arch_lm_test": [[809, 2, 2, 0, "lags", "Number of lags to include in the model.  If not specified,"], [809, 2, 2, 0, "standardized", "Flag indicating to test the model residuals divided by their conditional standard deviations.  If False, directly tests the estimated residuals."]], "arch.univariate.base.ARCHModelFixedResult.forecast": [[812, 2, 2, 0, "align", "Either 'origin' or 'target'.  When set of 'origin', the t-th row of forecasts contains the forecasts for t+1, t+2, ..., t+h. When set to 'target', the t-th row contains the 1-step ahead forecast from time t-1, the 2 step from time t-2, ..., and the h-step from time t-h.  'target' simplified computing forecast errors since the realization and h-step forecast are aligned."], [812, 2, 2, 0, "horizon", "Number of steps to forecast"], [812, 2, 2, 0, "method", "Method to use when producing the forecast. The default is analytic. The method only affects the variance forecast generation.  Not all volatility models support all methods. In particular, volatility models that do not evolve in squares such as EGARCH or TARCH do not support the 'analytic' method for horizons > 1."], [812, 2, 2, 0, "params", "Alternative parameters to use.  If not provided, the parameters estimated when fitting the model are used.  Must be identical in shape to the parameters computed by fitting the model."], [812, 2, 2, 0, "random_state", "NumPy RandomState instance to use when method is 'bootstrap'"], [812, 2, 2, 0, "reindex", "Whether to reindex the forecasts to have the same dimension as the series being forecast."], [812, 2, 2, 0, "rng", "Custom random number generator to use in simulation-based forecasts. Must produce random samples using the syntax rng(size) where size the 2-element tuple (simulations, horizon)."], [812, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [812, 2, 2, 0, "start", "An integer, datetime or str indicating the first observation to produce the forecast for.  Datetimes can only be used with pandas inputs that have a datetime index. Strings must be convertible to a date time, such as in '1945-01-01'."], [812, 2, 2, 0, "x", "Values to use for exogenous regressors if any are included in the model. Three formats are accepted:"]], "arch.univariate.base.ARCHModelFixedResult.hedgehog_plot": [[813, 2, 2, 0, "horizon", "Number of steps to forecast"], [813, 2, 2, 0, "method", "Method to use when producing the forecast. The default is analytic. The method only affects the variance forecast generation.  Not all volatility models support all methods. In particular, volatility models that do not evolve in squares such as EGARCH or TARCH do not support the 'analytic' method for horizons > 1."], [813, 2, 2, 0, "params", "Alternative parameters to use.  If not provided, the parameters computed by fitting the model are used.  Must be 1-d and identical in shape to the parameters computed by fitting the model."], [813, 2, 2, 0, "plot_type", "Quantity to plot, the forecast volatility or the forecast mean"], [813, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [813, 2, 2, 0, "start", "An integer, datetime or str indicating the first observation to produce the forecast for.  Datetimes can only be used with pandas inputs that have a datetime index.  Strings must be convertible to a date time, such as in '1945-01-01'.  If not provided, the start is set to the earliest forecastable date."], [813, 2, 2, 0, "step", "Non-negative number of forecasts to skip between spines"]], "arch.univariate.base.ARCHModelFixedResult.plot": [[819, 2, 2, 0, "annualize", "String containing frequency of data that indicates plot should contain annualized volatility.  Supported values are 'D' (daily), 'W' (weekly) and 'M' (monthly), which scale variance by 252, 52, and 12, respectively."], [819, 2, 2, 0, "scale", "Value to use when scaling returns to annualize.  If scale is provided, annualize is ignored and the value in scale is used."]], "arch.univariate.base.ARCHModelForecast.__init__": [[448, 2, 2, 0, "align", ""], [448, 2, 2, 0, "index", ""], [448, 2, 2, 0, "mean", ""], [448, 2, 2, 0, "residual_variance", ""], [448, 2, 2, 0, "simulated_paths", ""], [448, 2, 2, 0, "simulated_residual_variances", ""], [448, 2, 2, 0, "simulated_residuals", ""], [448, 2, 2, 0, "simulated_variances", ""], [448, 2, 2, 0, "variance", ""]], "arch.univariate.base.ARCHModelForecast": [[823, 5, 1, 0, "mean", "Forecast values for the conditional mean of the process"], [824, 5, 1, 0, "residual_variance", "Forecast values for the conditional variance of the residuals"], [825, 5, 1, 0, "simulations", "Detailed simulation results if using a simulation-based method"], [826, 5, 1, 0, "variance", "Forecast values for the conditional variance of the process"]], "arch.univariate.base.ARCHModelForecastSimulation.__init__": [[449, 2, 2, 0, "index", ""], [449, 2, 2, 0, "residual_variances", ""], [449, 2, 2, 0, "residuals", ""], [449, 2, 2, 0, "values", ""], [449, 2, 2, 0, "variances", ""]], "arch.univariate.base.ARCHModelForecastSimulation": [[827, 5, 1, 0, "index", "The index aligned to dimension 0 of the simulation paths"], [828, 5, 1, 0, "residual_variances", "Simulated variance of the residuals"], [829, 5, 1, 0, "residuals", "Simulated residuals used to produce the values"], [830, 5, 1, 0, "values", "The values of the process"], [831, 5, 1, 0, "variances", "Simulated variances of the values"]], "arch.univariate.base.ARCHModelResult.__init__": [[450, 2, 2, 0, "cov_type", "String describing the covariance estimator used"], [450, 2, 2, 0, "dep_var", "Dependent variable"], [450, 2, 2, 0, "fit_start", "Integer index of the first observation used to fit the model"], [450, 2, 2, 0, "fit_stop", "Integer index of the last observation used to fit the model using slice notation fit_start:fit_stop"], [450, 2, 2, 0, "is_pandas", "Whether the original input was pandas"], [450, 2, 2, 0, "loglikelihood", "Loglikelihood at estimated parameters"], [450, 2, 2, 0, "model", "The model object used to estimate the parameters"], [450, 2, 2, 0, "names", "Model parameter names"], [450, 2, 2, 0, "optim_output", "Result of log-likelihood optimization"], [450, 2, 2, 0, "param_cov", "Estimated variance-covariance matrix of params.  If none, calls method to compute variance from model when parameter covariance is first used from result"], [450, 2, 2, 0, "params", "Estimated parameters"], [450, 2, 2, 0, "r2", "Model R-squared"], [450, 2, 2, 0, "resid", "Residuals from model.  Residuals have same shape as original data and contain nan-values in locations not used in estimation"], [450, 2, 2, 0, "volatility", "Conditional volatility from model"]], "arch.univariate.base.ARCHModelResult": [[832, 5, 1, 0, "aic", "Akaike Information Criteria"], [833, 3, 1, 0, "arch_lm_test", "ARCH LM test for conditional heteroskedasticity"], [834, 5, 1, 0, "bic", "Schwarz/Bayesian Information Criteria"], [835, 5, 1, 0, "conditional_volatility", "Estimated conditional volatility"], [836, 3, 1, 0, "conf_int", "Parameter confidence intervals"], [837, 5, 1, 0, "convergence_flag", "scipy.optimize.minimize result flag"], [838, 5, 1, 0, "fit_start", "Start of sample used to estimate parameters"], [839, 5, 1, 0, "fit_stop", "End of sample used to estimate parameters"], [840, 3, 1, 0, "forecast", "Construct forecasts from estimated model"], [841, 3, 1, 0, "hedgehog_plot", "Plot forecasts from estimated model"], [842, 5, 1, 0, "loglikelihood", "Model loglikelihood"], [843, 5, 1, 0, "model", "Model instance used to produce the fit"], [844, 5, 1, 0, "nobs", "Number of data points used to estimate model"], [845, 5, 1, 0, "num_params", "Number of parameters in model"], [846, 5, 1, 0, "optimization_result", "Information about the convergence of the loglikelihood optimization"], [847, 5, 1, 0, "param_cov", "Parameter covariance"], [848, 5, 1, 0, "params", "Model Parameters"], [849, 3, 1, 0, "plot", "Plot standardized residuals and conditional volatility"], [850, 5, 1, 0, "pvalues", "Array of p-values for the t-statistics"], [851, 5, 1, 0, "resid", "Model residuals"], [852, 5, 1, 0, "rsquared", "R-squared"], [853, 5, 1, 0, "rsquared_adj", "Degree of freedom adjusted R-squared"], [854, 5, 1, 0, "scale", "The scale applied to the original data before estimating the model."], [855, 5, 1, 0, "std_err", "Array of parameter standard errors"], [856, 5, 1, 0, "std_resid", "Residuals standardized by conditional volatility"], [857, 3, 1, 0, "summary", "Constructs a summary of the results from a fit model."], [858, 5, 1, 0, "tvalues", "Array of t-statistics testing the null that the coefficient are 0"]], "arch.univariate.base.ARCHModelResult.arch_lm_test": [[833, 2, 2, 0, "lags", "Number of lags to include in the model.  If not specified,"], [833, 2, 2, 0, "standardized", "Flag indicating to test the model residuals divided by their conditional standard deviations.  If False, directly tests the estimated residuals."]], "arch.univariate.base.ARCHModelResult.conf_int": [[836, 2, 2, 0, "alpha", "Size (prob.) to use when constructing the confidence interval."]], "arch.univariate.base.ARCHModelResult.forecast": [[840, 2, 2, 0, "align", "Either 'origin' or 'target'.  When set of 'origin', the t-th row of forecasts contains the forecasts for t+1, t+2, ..., t+h. When set to 'target', the t-th row contains the 1-step ahead forecast from time t-1, the 2 step from time t-2, ..., and the h-step from time t-h.  'target' simplified computing forecast errors since the realization and h-step forecast are aligned."], [840, 2, 2, 0, "horizon", "Number of steps to forecast"], [840, 2, 2, 0, "method", "Method to use when producing the forecast. The default is analytic. The method only affects the variance forecast generation.  Not all volatility models support all methods. In particular, volatility models that do not evolve in squares such as EGARCH or TARCH do not support the 'analytic' method for horizons > 1."], [840, 2, 2, 0, "params", "Alternative parameters to use.  If not provided, the parameters estimated when fitting the model are used.  Must be identical in shape to the parameters computed by fitting the model."], [840, 2, 2, 0, "random_state", "NumPy RandomState instance to use when method is 'bootstrap'"], [840, 2, 2, 0, "reindex", "Whether to reindex the forecasts to have the same dimension as the series being forecast."], [840, 2, 2, 0, "rng", "Custom random number generator to use in simulation-based forecasts. Must produce random samples using the syntax rng(size) where size the 2-element tuple (simulations, horizon)."], [840, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [840, 2, 2, 0, "start", "An integer, datetime or str indicating the first observation to produce the forecast for.  Datetimes can only be used with pandas inputs that have a datetime index. Strings must be convertible to a date time, such as in '1945-01-01'."], [840, 2, 2, 0, "x", "Values to use for exogenous regressors if any are included in the model. Three formats are accepted:"]], "arch.univariate.base.ARCHModelResult.hedgehog_plot": [[841, 2, 2, 0, "horizon", "Number of steps to forecast"], [841, 2, 2, 0, "method", "Method to use when producing the forecast. The default is analytic. The method only affects the variance forecast generation.  Not all volatility models support all methods. In particular, volatility models that do not evolve in squares such as EGARCH or TARCH do not support the 'analytic' method for horizons > 1."], [841, 2, 2, 0, "params", "Alternative parameters to use.  If not provided, the parameters computed by fitting the model are used.  Must be 1-d and identical in shape to the parameters computed by fitting the model."], [841, 2, 2, 0, "plot_type", "Quantity to plot, the forecast volatility or the forecast mean"], [841, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [841, 2, 2, 0, "start", "An integer, datetime or str indicating the first observation to produce the forecast for.  Datetimes can only be used with pandas inputs that have a datetime index.  Strings must be convertible to a date time, such as in '1945-01-01'.  If not provided, the start is set to the earliest forecastable date."], [841, 2, 2, 0, "step", "Non-negative number of forecasts to skip between spines"]], "arch.univariate.base.ARCHModelResult.plot": [[849, 2, 2, 0, "annualize", "String containing frequency of data that indicates plot should contain annualized volatility.  Supported values are 'D' (daily), 'W' (weekly) and 'M' (monthly), which scale variance by 252, 52, and 12, respectively."], [849, 2, 2, 0, "scale", "Value to use when scaling returns to annualize.  If scale is provided, annualize is ignored and the value in scale is used."]], "arch.univariate.distribution": [[451, 1, 1, 0, "Distribution", "Template for subclassing only"]], "arch.univariate.distribution.Distribution": [[859, 3, 1, 0, "bounds", "Parameter bounds for use in optimization."], [860, 3, 1, 0, "cdf", "Cumulative distribution function"], [861, 3, 1, 0, "constraints", "Construct arrays to use in constrained optimization."], [862, 5, 1, 0, "generator", "The NumPy Generator or RandomState attached to the distribution"], [863, 3, 1, 0, "loglikelihood", "Loglikelihood evaluation."], [864, 3, 1, 0, "moment", "Moment of order n"], [865, 5, 1, 0, "name", "The name of the distribution"], [866, 3, 1, 0, "parameter_names", "Names of distribution shape parameters"], [867, 3, 1, 0, "partial_moment", "Order n lower partial moment from -inf to z"], [868, 3, 1, 0, "ppf", "Inverse cumulative density function (ICDF)"], [869, 5, 1, 0, "random_state", "The NumPy RandomState attached to the distribution"], [870, 3, 1, 0, "simulate", "Simulates i.i.d. draws from the distribution"], [871, 3, 1, 0, "starting_values", "Construct starting values for use in optimization."]], "arch.univariate.distribution.Distribution.bounds": [[859, 2, 2, 0, "resids", "Residuals to use when computing the bounds"]], "arch.univariate.distribution.Distribution.cdf": [[860, 2, 2, 0, "parameters", "Distribution parameters. Use None for parameterless distributions."], [860, 2, 2, 0, "resids", "Values at which to evaluate the cdf"]], "arch.univariate.distribution.Distribution.loglikelihood": [[863, 2, 2, 0, "individual", "Flag indicating whether to return the vector of individual log likelihoods (True) or the sum (False)"], [863, 2, 2, 0, "parameters", "Distribution shape parameters"], [863, 2, 2, 0, "resids", "nobs array of model residuals"], [863, 2, 2, 0, "sigma2", "nobs array of conditional variances"]], "arch.univariate.distribution.Distribution.moment": [[864, 2, 2, 0, "n", "Order of moment"], [864, 2, 2, 0, "parameters", "Distribution parameters. Use None for parameterless distributions."]], "arch.univariate.distribution.Distribution.partial_moment": [[867, 2, 2, 0, "n", "Order of partial moment"], [867, 2, 2, 0, "parameters", "Distribution parameters.  Use None for parameterless distributions."], [867, 2, 2, 0, "z", "Upper bound for partial moment integral"]], "arch.univariate.distribution.Distribution.ppf": [[868, 2, 2, 0, "parameters", "Distribution parameters. Use None for parameterless distributions."], [868, 2, 2, 0, "pits", "Probability-integral-transformed values in the interval (0, 1)."]], "arch.univariate.distribution.Distribution.simulate": [[870, 2, 2, 0, "parameters", "Distribution parameters"]], "arch.univariate.distribution.Distribution.starting_values": [[871, 2, 2, 0, "std_resid", "Estimated standardized residuals to use in computing starting values for the shape parameter"]], "arch.univariate.recursions_python": [[452, 1, 1, 0, "VolatilityUpdater", "Base class that all volatility updaters must inherit from."]], "arch.univariate.recursions_python.VolatilityUpdater": [[872, 3, 1, 0, "initialize_update", "Initialize the recursion prior to calling update"], [873, 3, 1, 0, "update", "Update the current variance at location t"]], "arch.univariate.recursions_python.VolatilityUpdater.initialize_update": [[872, 2, 2, 0, "backcast", "The backcast value(s)."], [872, 2, 2, 0, "nobs", "The number of observations in the sample."], [872, 2, 2, 0, "parameters", "The model parameters."]], "arch.univariate.recursions_python.VolatilityUpdater.update": [[873, 2, 2, 0, "parameters", "Model parameters"], [873, 2, 2, 0, "resids", "Residuals to use in the recursion"], [873, 2, 2, 0, "sigma2", "Conditional variances with same shape as resids"], [873, 2, 2, 0, "t", "The index of the value of sigma2 to update. Assumes but does not check that update has been called recursively for 0,1,...,t-1."], [873, 2, 2, 0, "var_bounds", "nobs by 2-element array of upper and lower bounds for conditional variances for each time period"]], "arch.univariate.volatility": [[453, 1, 1, 0, "VolatilityProcess", "Abstract base class for ARCH models.  Allows the conditional mean model to be specified separately from the conditional variance, even though parameters are estimated jointly."]], "arch.univariate.volatility.VolatilityProcess": [[874, 3, 1, 0, "backcast", "Construct values for backcasting to start the recursion"], [875, 3, 1, 0, "backcast_transform", "Transformation to apply to user-provided backcast values"], [876, 3, 1, 0, "bounds", "Returns bounds for parameters"], [877, 3, 1, 0, "compute_variance", "Compute the variance for the ARCH model"], [878, 3, 1, 0, "constraints", "Construct parameter constraints arrays for parameter estimation"], [879, 3, 1, 0, "forecast", "Forecast volatility from the model"], [880, 5, 1, 0, "name", "The name of the volatility process"], [881, 5, 1, 0, "num_params", "The number of parameters in the model"], [882, 3, 1, 0, "parameter_names", "Names of model parameters"], [883, 3, 1, 0, "simulate", "Simulate data from the model"], [884, 5, 1, 0, "start", "Index to use to start variance subarray selection"], [885, 3, 1, 0, "starting_values", "Returns starting values for the ARCH model"], [886, 5, 1, 0, "stop", "Index to use to stop variance subarray selection"], [887, 3, 1, 0, "update", "Compute the variance for a single observation"], [888, 5, 1, 0, "updateable", "Flag indicating that the volatility process supports update"], [889, 3, 1, 0, "variance_bounds", "Construct loose bounds for conditional variances."], [890, 5, 1, 0, "volatility_updater", "Get the volatility updater associated with the volatility process"]], "arch.univariate.volatility.VolatilityProcess.backcast": [[874, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.volatility.VolatilityProcess.backcast_transform": [[875, 2, 2, 0, "backcast", "User-provided backcast that approximates sigma2[0]."]], "arch.univariate.volatility.VolatilityProcess.bounds": [[876, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.volatility.VolatilityProcess.compute_variance": [[877, 2, 2, 0, "backcast", "Value to use when initializing ARCH recursion. Can be an ndarray when the model contains multiple components."], [877, 2, 2, 0, "parameters", "Model parameters"], [877, 2, 2, 0, "resids", "Vector of mean zero residuals"], [877, 2, 2, 0, "sigma2", "Array with same size as resids to store the conditional variance"], [877, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.volatility.VolatilityProcess.forecast": [[879, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [879, 2, 2, 0, "horizon", "Forecast horizon.  Must be 1 or larger.  Forecasts are produced for horizons in [1, horizon]."], [879, 2, 2, 0, "method", "Method to use when producing the forecast. The default is analytic."], [879, 2, 2, 0, "parameters", "Parameters required to forecast the volatility model"], [879, 2, 2, 0, "random_state", "NumPy RandomState instance to use when method is 'bootstrap'"], [879, 2, 2, 0, "resids", "Residuals to use in the recursion"], [879, 2, 2, 0, "rng", "Callable random number generator required if method is 'simulation'. Must take a single shape input and return random samples numbers with that shape."], [879, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [879, 2, 2, 0, "start", "Index of the first observation to use as the starting point for the forecast.  Default is len(resids)."], [879, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.volatility.VolatilityProcess.simulate": [[883, 2, 2, 0, "burn", "Number of additional observations to generate when initializing the simulation"], [883, 2, 2, 0, "initial_value", "Scalar or array of initial values to use when initializing the simulation"], [883, 2, 2, 0, "nobs", "Number of data points to simulate"], [883, 2, 2, 0, "parameters", "Parameters required to simulate the volatility model"], [883, 2, 2, 0, "rng", "Callable function that takes a single integer input and returns a vector of random numbers"]], "arch.univariate.volatility.VolatilityProcess.starting_values": [[885, 2, 2, 0, "resids", "Array of (approximate) residuals to use when computing starting values"]], "arch.univariate.volatility.VolatilityProcess.update": [[887, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [887, 2, 2, 0, "index", "The numerical index of the variance to compute"], [887, 2, 2, 0, "parameters", "The variance model parameters"], [887, 2, 2, 0, "resids", "The residual array. Only uses resids[:index] when computing sigma2[index]"], [887, 2, 2, 0, "sigma2", "The array containing the variances. Only uses sigma2[:index] when computing sigma2[index]. The computed value is stored in sigma2[index]."], [887, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.volatility.VolatilityProcess.variance_bounds": [[889, 2, 2, 0, "power", "Power used in the model. 2.0, the default corresponds to standard ARCH models that evolve in squares."], [889, 2, 2, 0, "resids", "Approximate residuals to use to compute the lower and upper bounds on the conditional variance"]], "arch.utility": [[900, 0, 0, 1, "testing", ""]], "arch.utility.testing": [[900, 1, 1, 0, "WaldTestStatistic", "Test statistic holder for Wald-type tests"]], "arch.utility.testing.WaldTestStatistic.__init__": [[900, 2, 2, 0, "alternative", "A statement of the test's alternative hypothesis"], [900, 2, 2, 0, "df", "Degree of freedom."], [900, 2, 2, 0, "name", "Name of test"], [900, 2, 2, 0, "null", "A statement of the test's null hypothesis"], [900, 2, 2, 0, "stat", "The test statistic"]], "arch.utility.testing.WaldTestStatistic": [[900, 5, 1, 0, "critical_values", "Critical values test for common test sizes"], [900, 5, 1, 0, "null", "Null hypothesis"], [900, 5, 1, 0, "pval", "P-value of test statistic"], [900, 5, 1, 0, "stat", "Test statistic"]]}, "objtypes": {"0": "py:module", "1": "py:class", "2": "py:parameter", "3": "py:method", "4": "py:attribute", "5": "py:property", "6": "py:function"}, "objnames": {"0": ["py", "module", "Python module"], "1": ["py", "class", "Python class"], "2": ["py", "parameter", "Python parameter"], "3": ["py", "method", "Python method"], "4": ["py", "attribute", "Python attribute"], "5": ["py", "property", "Python property"], "6": ["py", "function", "Python function"]}, "titleterms": {"api": 0, "refer": [0, 1, 237, 258], "volatil": [0, 96, 453, 504, 520, 536, 685, 701, 790, 806, 874, 875, 876, 877, 878, 879, 880, 881, 882, 883, 884, 885, 886, 887, 888, 889, 890, 894, 896, 897, 898, 899, 901], "model": [0, 3, 96, 258, 259, 815, 843, 891, 892, 893, 894, 895, 896, 898], "high": 0, "level": [0, 87], "mean": [0, 823, 892, 896, 898], "specif": 0, "process": [0, 896, 898, 901], "shock": 0, "distribut": [0, 86, 423, 451, 493, 510, 526, 675, 691, 780, 796, 859, 860, 861, 862, 863, 864, 865, 866, 867, 868, 869, 870, 871, 898], "unit": [0, 96, 418, 419, 421], "root": [0, 96, 418, 419, 421], "test": [0, 258, 259, 261, 418, 419, 420, 421, 900], "cointegr": [0, 261, 269, 270, 271, 272, 273, 274, 275, 276, 277, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 365, 366, 367, 368, 369, 370, 371, 372, 373, 374, 375, 376, 377, 378, 379, 380, 381, 382, 383, 384, 385, 386, 387, 388, 389, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 416, 419, 420], "relationship": 0, "estim": [0, 3, 87, 88, 98, 261, 896], "bootstrap": [0, 2, 3, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 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"arch.univariate.volatility.VolatilityProcess.num_params": [[881, "arch-univariate-volatility-volatilityprocess-num-params"]], "arch.univariate.volatility.VolatilityProcess.variance_bounds": [[889, "arch-univariate-volatility-volatilityprocess-variance-bounds"]], "arch.univariate.volatility.VolatilityProcess.constraints": [[878, "arch-univariate-volatility-volatilityprocess-constraints"]], "arch.univariate.volatility.VolatilityProcess.stop": [[886, "arch-univariate-volatility-volatilityprocess-stop"]], "(G)ARCH-in-mean Models": [[892, "g-arch-in-mean-models"]], "Special Requirements": [[892, "special-requirements"]], "Writing New Mean Models": [[892, "writing-new-mean-models"]], "Volatility Scenarios": [[899, "Volatility-Scenarios"]], "Custom Random Generators": [[899, "Custom-Random-Generators"]], "Bootstrap Scenarios": [[899, "Bootstrap-Scenarios"]], "Visualizing the differences": [[899, "Visualizing-the-differences"]], "Comparing the paths": [[899, "Comparing-the-paths"]], "Comparing across the year": [[899, "Comparing-across-the-year"]], "Using the Fixed Variance process": [[896, "Using-the-Fixed-Variance-process"]], "Initial Mean Model Estimation": [[896, "Initial-Mean-Model-Estimation"]], "Initial Volatility Model Estimation": [[896, "Initial-Volatility-Model-Estimation"]], "Re-estimating the mean with a FixedVariance": [[896, "Re-estimating-the-mean-with-a-FixedVariance"]], "Zig-Zag estimation": [[896, "Zig-Zag-estimation"]], "Direct Estimation": [[896, "Direct-Estimation"]], "arch.univariate.distribution.Distribution.starting_values": [[871, "arch-univariate-distribution-distribution-starting-values"]], "arch.univariate.volatility.VolatilityProcess.forecast": [[879, "arch-univariate-volatility-volatilityprocess-forecast"]], "arch.univariate.recursions_python.VolatilityUpdater.initialize_update": [[872, "arch-univariate-recursions-python-volatilityupdater-initialize-update"]], "arch.univariate.recursions_python.VolatilityUpdater.update": [[873, 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"arch.univariate.ConstantVariance.parameter_names": [[547, "arch-univariate-constantvariance-parameter-names"]], "arch.univariate.EGARCH.parameter_names": [[564, "arch-univariate-egarch-parameter-names"]], "arch.univariate.EGARCH.backcast_transform": [[557, "arch-univariate-egarch-backcast-transform"]], "arch.univariate.ConstantVariance.backcast": [[539, "arch-univariate-constantvariance-backcast"]], "arch.univariate.EGARCH.update": [[569, "arch-univariate-egarch-update"]], "arch.univariate.EGARCH.backcast": [[556, "arch-univariate-egarch-backcast"]], "arch.univariate.EWMAVariance.variance_bounds": [[588, "arch-univariate-ewmavariance-variance-bounds"]], "arch.univariate.FIGARCH.start": [[600, "arch-univariate-figarch-start"]], "arch.univariate.FIGARCH.starting_values": [[601, "arch-univariate-figarch-starting-values"]], "arch.univariate.EGARCH.bounds": [[558, "arch-univariate-egarch-bounds"]], "arch.univariate.ConstantVariance.backcast_transform": [[540, 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"arch.covariance.kernel.QuadraticSpectral.cov", "arch.covariance.kernel.QuadraticSpectral.force_int", "arch.covariance.kernel.QuadraticSpectral.kernel_const", "arch.covariance.kernel.QuadraticSpectral.kernel_weights", "arch.covariance.kernel.QuadraticSpectral.name", "arch.covariance.kernel.QuadraticSpectral.opt_bandwidth", "arch.covariance.kernel.QuadraticSpectral.rate", "arch.covariance.kernel.TukeyHamming.bandwidth", "arch.covariance.kernel.TukeyHamming.bandwidth_scale", "arch.covariance.kernel.TukeyHamming.centered", "arch.covariance.kernel.TukeyHamming.cov", "arch.covariance.kernel.TukeyHamming.force_int", "arch.covariance.kernel.TukeyHamming.kernel_const", "arch.covariance.kernel.TukeyHamming.kernel_weights", "arch.covariance.kernel.TukeyHamming.name", "arch.covariance.kernel.TukeyHamming.opt_bandwidth", "arch.covariance.kernel.TukeyHamming.rate", "arch.covariance.kernel.TukeyHanning.bandwidth", "arch.covariance.kernel.TukeyHanning.bandwidth_scale", 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"arch.bootstrap.MCS.excluded", "arch.bootstrap.MCS.included", "arch.bootstrap.MCS.pvalues", "arch.bootstrap.MCS.reset", "arch.bootstrap.MCS.seed", "arch.bootstrap.SPA.better_models", "arch.bootstrap.SPA.compute", "arch.bootstrap.SPA.critical_values", "arch.bootstrap.SPA.pvalues", "arch.bootstrap.SPA.reset", "arch.bootstrap.SPA.seed", "arch.bootstrap.SPA.subset", "arch.bootstrap.StepM.compute", "arch.bootstrap.StepM.reset", "arch.bootstrap.StepM.seed", "arch.bootstrap.StepM.superior_models", "Module Reference", "Multiple Comparisons", "Multiple Comparison Procedures", "Cointegration Tests", "arch.unitroot.ADF", "arch.unitroot.DFGLS", "arch.unitroot.KPSS", "arch.unitroot.PhillipsPerron", "arch.unitroot.VarianceRatio", "arch.unitroot.ZivotAndrews", "arch.unitroot.auto_bandwidth", "arch.unitroot.cointegration.CanonicalCointegratingReg", "arch.unitroot.cointegration.CointegrationAnalysisResults", "arch.unitroot.cointegration.DynamicOLS", "arch.unitroot.cointegration.DynamicOLSResults", 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"arch.unitroot.DFGLS.trend", "arch.unitroot.DFGLS.valid_trends", "arch.unitroot.DFGLS.y", "arch.unitroot.KPSS.alternative_hypothesis", "arch.unitroot.KPSS.critical_values", "arch.unitroot.KPSS.lags", "arch.unitroot.KPSS.nobs", "arch.unitroot.KPSS.null_hypothesis", "arch.unitroot.KPSS.pvalue", "arch.unitroot.KPSS.stat", "arch.unitroot.KPSS.summary", "arch.unitroot.KPSS.trend", "arch.unitroot.KPSS.valid_trends", "arch.unitroot.KPSS.y", "arch.unitroot.PhillipsPerron.alternative_hypothesis", "arch.unitroot.PhillipsPerron.critical_values", "arch.unitroot.PhillipsPerron.lags", "arch.unitroot.PhillipsPerron.nobs", "arch.unitroot.PhillipsPerron.null_hypothesis", "arch.unitroot.PhillipsPerron.pvalue", "arch.unitroot.PhillipsPerron.regression", "arch.unitroot.PhillipsPerron.stat", "arch.unitroot.PhillipsPerron.summary", "arch.unitroot.PhillipsPerron.test_type", "arch.unitroot.PhillipsPerron.trend", "arch.unitroot.PhillipsPerron.valid_trends", "arch.unitroot.PhillipsPerron.y", "arch.unitroot.VarianceRatio.alternative_hypothesis", "arch.unitroot.VarianceRatio.critical_values", "arch.unitroot.VarianceRatio.debiased", "arch.unitroot.VarianceRatio.lags", "arch.unitroot.VarianceRatio.nobs", "arch.unitroot.VarianceRatio.null_hypothesis", "arch.unitroot.VarianceRatio.overlap", "arch.unitroot.VarianceRatio.pvalue", "arch.unitroot.VarianceRatio.robust", "arch.unitroot.VarianceRatio.stat", "arch.unitroot.VarianceRatio.summary", "arch.unitroot.VarianceRatio.trend", "arch.unitroot.VarianceRatio.valid_trends", "arch.unitroot.VarianceRatio.vr", "arch.unitroot.VarianceRatio.y", "arch.unitroot.ZivotAndrews.alternative_hypothesis", "arch.unitroot.ZivotAndrews.critical_values", "arch.unitroot.ZivotAndrews.lags", "arch.unitroot.ZivotAndrews.nobs", "arch.unitroot.ZivotAndrews.null_hypothesis", "arch.unitroot.ZivotAndrews.pvalue", "arch.unitroot.ZivotAndrews.stat", "arch.unitroot.ZivotAndrews.summary", "arch.unitroot.ZivotAndrews.trend", 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"arch.unitroot.cointegration.DynamicOLSResults.std_errors", "arch.unitroot.cointegration.DynamicOLSResults.summary", "arch.unitroot.cointegration.DynamicOLSResults.tvalues", "arch.unitroot.cointegration.EngleGrangerTestResults.alternative_hypothesis", "arch.unitroot.cointegration.EngleGrangerTestResults.cointegrating_vector", "arch.unitroot.cointegration.EngleGrangerTestResults.critical_values", "arch.unitroot.cointegration.EngleGrangerTestResults.distribution_order", "arch.unitroot.cointegration.EngleGrangerTestResults.lags", "arch.unitroot.cointegration.EngleGrangerTestResults.max_lags", "arch.unitroot.cointegration.EngleGrangerTestResults.name", "arch.unitroot.cointegration.EngleGrangerTestResults.null_hypothesis", "arch.unitroot.cointegration.EngleGrangerTestResults.plot", "arch.unitroot.cointegration.EngleGrangerTestResults.pvalue", "arch.unitroot.cointegration.EngleGrangerTestResults.resid", "arch.unitroot.cointegration.EngleGrangerTestResults.rho", 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"arch.univariate.ConstantVariance.parameter_names", "arch.univariate.ConstantVariance.simulate", "arch.univariate.ConstantVariance.start", "arch.univariate.ConstantVariance.starting_values", "arch.univariate.ConstantVariance.stop", "arch.univariate.ConstantVariance.update", "arch.univariate.ConstantVariance.updateable", "arch.univariate.ConstantVariance.variance_bounds", "arch.univariate.ConstantVariance.volatility_updater", "arch.univariate.EGARCH.backcast", "arch.univariate.EGARCH.backcast_transform", "arch.univariate.EGARCH.bounds", "arch.univariate.EGARCH.compute_variance", "arch.univariate.EGARCH.constraints", "arch.univariate.EGARCH.forecast", "arch.univariate.EGARCH.name", "arch.univariate.EGARCH.num_params", "arch.univariate.EGARCH.parameter_names", "arch.univariate.EGARCH.simulate", "arch.univariate.EGARCH.start", "arch.univariate.EGARCH.starting_values", "arch.univariate.EGARCH.stop", "arch.univariate.EGARCH.update", "arch.univariate.EGARCH.updateable", "arch.univariate.EGARCH.variance_bounds", "arch.univariate.EGARCH.volatility_updater", "arch.univariate.EWMAVariance.backcast", "arch.univariate.EWMAVariance.backcast_transform", "arch.univariate.EWMAVariance.bounds", "arch.univariate.EWMAVariance.compute_variance", "arch.univariate.EWMAVariance.constraints", "arch.univariate.EWMAVariance.forecast", "arch.univariate.EWMAVariance.name", "arch.univariate.EWMAVariance.num_params", "arch.univariate.EWMAVariance.parameter_names", "arch.univariate.EWMAVariance.simulate", "arch.univariate.EWMAVariance.start", "arch.univariate.EWMAVariance.starting_values", "arch.univariate.EWMAVariance.stop", "arch.univariate.EWMAVariance.update", "arch.univariate.EWMAVariance.updateable", "arch.univariate.EWMAVariance.variance_bounds", "arch.univariate.EWMAVariance.volatility_updater", "arch.univariate.FIGARCH.backcast", "arch.univariate.FIGARCH.backcast_transform", "arch.univariate.FIGARCH.bounds", "arch.univariate.FIGARCH.compute_variance", "arch.univariate.FIGARCH.constraints", "arch.univariate.FIGARCH.forecast", "arch.univariate.FIGARCH.name", "arch.univariate.FIGARCH.num_params", "arch.univariate.FIGARCH.parameter_names", "arch.univariate.FIGARCH.simulate", "arch.univariate.FIGARCH.start", "arch.univariate.FIGARCH.starting_values", "arch.univariate.FIGARCH.stop", "arch.univariate.FIGARCH.truncation", "arch.univariate.FIGARCH.update", "arch.univariate.FIGARCH.updateable", "arch.univariate.FIGARCH.variance_bounds", "arch.univariate.FIGARCH.volatility_updater", "arch.univariate.FixedVariance.backcast", "arch.univariate.FixedVariance.backcast_transform", "arch.univariate.FixedVariance.bounds", "arch.univariate.FixedVariance.compute_variance", "arch.univariate.FixedVariance.constraints", "arch.univariate.FixedVariance.forecast", "arch.univariate.FixedVariance.name", "arch.univariate.FixedVariance.num_params", "arch.univariate.FixedVariance.parameter_names", "arch.univariate.FixedVariance.simulate", "arch.univariate.FixedVariance.start", "arch.univariate.FixedVariance.starting_values", "arch.univariate.FixedVariance.stop", "arch.univariate.FixedVariance.update", "arch.univariate.FixedVariance.updateable", "arch.univariate.FixedVariance.variance_bounds", "arch.univariate.FixedVariance.volatility_updater", "arch.univariate.GARCH.backcast", "arch.univariate.GARCH.backcast_transform", "arch.univariate.GARCH.bounds", "arch.univariate.GARCH.compute_variance", "arch.univariate.GARCH.constraints", "arch.univariate.GARCH.forecast", "arch.univariate.GARCH.name", "arch.univariate.GARCH.num_params", "arch.univariate.GARCH.parameter_names", "arch.univariate.GARCH.simulate", "arch.univariate.GARCH.start", "arch.univariate.GARCH.starting_values", "arch.univariate.GARCH.stop", "arch.univariate.GARCH.update", "arch.univariate.GARCH.updateable", "arch.univariate.GARCH.variance_bounds", "arch.univariate.GARCH.volatility_updater", "arch.univariate.GeneralizedError.bounds", 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214, 224, 234, 269, 271, 274, 276, 277, 424, 425, 431, 435, 437, 440, 646, 725, 755, 768, 898], "l": [4, 433, 891, 898], "where": [4, 7, 10, 11, 14, 15, 25, 26, 29, 30, 40, 41, 44, 45, 55, 56, 59, 60, 70, 71, 74, 75, 85, 86, 87, 88, 95, 96, 100, 104, 105, 106, 107, 108, 109, 110, 111, 120, 130, 144, 154, 164, 174, 184, 194, 204, 214, 224, 234, 244, 258, 259, 269, 271, 274, 276, 277, 354, 362, 368, 377, 381, 402, 420, 424, 427, 431, 433, 435, 437, 438, 440, 442, 446, 456, 475, 496, 502, 513, 518, 529, 541, 558, 575, 592, 610, 627, 646, 657, 678, 683, 694, 699, 706, 736, 755, 768, 783, 799, 812, 836, 840, 863, 876, 895, 896, 897, 898, 899], "alpha": [4, 120, 130, 144, 154, 164, 174, 184, 194, 204, 214, 224, 234, 277, 420, 424, 440, 708, 836, 891, 895, 896, 897, 898, 899], "quantil": [4, 897], "vector": [4, 14, 29, 44, 59, 74, 89, 95, 100, 104, 105, 106, 107, 108, 109, 110, 111, 118, 128, 142, 152, 162, 172, 182, 192, 202, 212, 222, 232, 269, 271, 274, 277, 354, 356, 359, 360, 365, 367, 368, 370, 378, 379, 384, 385, 386, 388, 402, 405, 419, 420, 427, 428, 429, 438, 439, 445, 454, 456, 458, 465, 473, 475, 476, 482, 496, 513, 529, 539, 541, 542, 548, 556, 558, 559, 565, 573, 575, 576, 582, 590, 592, 593, 599, 608, 610, 611, 617, 625, 627, 628, 634, 646, 655, 657, 658, 664, 678, 694, 704, 706, 707, 713, 725, 734, 736, 737, 743, 755, 768, 783, 799, 812, 840, 863, 874, 876, 877, 883], "directli": [4, 5, 6, 7, 8, 9, 87, 89, 421, 809, 833, 891, 896], "cdf": [4, 651, 730, 760, 773, 868], "defin": [4, 94, 101, 259, 269, 274, 277, 362, 381, 417], "combin": [4, 89, 100, 104, 105, 106, 107, 108, 109, 110, 111, 420], "phi": [4, 277, 433], "mai": [4, 86, 89, 95, 276, 277, 421, 424, 895, 896, 898, 899, 901], "accur": [4, 895], "some": [4, 7, 10, 258, 259, 266, 421, 424, 893, 895, 898, 899, 901], "either": [4, 14, 20, 29, 35, 44, 50, 59, 65, 74, 80, 86, 238, 269, 274, 368, 461, 478, 494, 496, 502, 511, 513, 518, 527, 529, 544, 561, 578, 595, 613, 630, 660, 676, 678, 683, 692, 694, 699, 709, 739, 781, 783, 797, 799, 812, 813, 840, 841, 879, 891, 894, 895, 897, 898, 899], "analyt": [4, 14, 29, 44, 59, 74, 88, 461, 478, 496, 513, 529, 544, 561, 578, 595, 613, 630, 660, 678, 694, 709, 739, 783, 799, 812, 813, 840, 841, 879, 894, 899], "nest": [4, 14, 29, 44, 59, 74, 239, 240], "version": [4, 5, 6, 8, 9, 11, 14, 26, 29, 41, 44, 56, 59, 71, 74, 89, 236, 258, 266, 421, 436, 441, 443, 444, 496, 513, 529, 652, 678, 694, 731, 761, 774, 783, 799, 812, 840, 869, 898], "produc": [4, 12, 16, 27, 31, 42, 46, 57, 61, 72, 76, 89, 95, 96, 258, 259, 277, 354, 368, 402, 424, 427, 428, 429, 433, 435, 438, 439, 445, 452, 461, 471, 478, 488, 496, 513, 529, 544, 554, 561, 571, 578, 588, 595, 606, 613, 623, 630, 640, 660, 670, 678, 694, 709, 719, 739, 749, 763, 776, 783, 799, 812, 813, 815, 819, 829, 840, 841, 843, 849, 879, 889, 891, 893, 894, 897, 898, 899], "higher": [4, 87, 259, 271, 895], "refin": 4, "circumst": [4, 89, 898, 899], "although": [4, 89, 259, 420, 421, 424, 433, 435, 895, 898], "slow": [4, 239, 240], "inner": [4, 14, 29, 44, 59, 74, 100, 104, 105, 106, 107, 108, 109, 110, 111, 277], "outer": 4, "In": [4, 89, 238, 239, 240, 259, 417, 420, 424, 425, 431, 433, 435, 437, 440, 494, 496, 511, 513, 527, 529, 676, 678, 692, 694, 781, 783, 797, 799, 812, 813, 840, 841, 891, 892, 895, 896, 898, 899], "necessari": [4, 89, 895], "done": 4, "moment": [4, 95, 650, 729, 759, 763, 772, 776, 867], "delta": [4, 269, 271, 274, 398, 420, 425, 457], "detail": [4, 238, 239, 240, 259, 269, 271, 274, 277, 650, 729, 759, 772, 825, 867, 897], "descript": [4, 273, 427, 428, 438], "mathemat": 4, "formula": 4, "beyond": [4, 899], "intent": [4, 421], "document": [4, 236, 424], "sharpe_ratio_s": 4, "e1": 4, "e2": 4, "vstack": 4, "vcv": 4, "dot": [4, 89, 259, 459, 477, 492, 509, 525, 543, 560, 577, 594, 612, 629, 644, 659, 674, 690, 708, 723, 738, 753, 766, 779, 795, 861, 878], "avar": 4, "std_err_func": [4, 14, 29, 44, 59, 74], "match": [4, 10, 91, 97, 496, 513, 529, 678, 694, 783, 799, 812, 840, 895], "patter": 4, "arg": [4, 5, 6, 7, 8, 9, 11, 13, 14, 15, 25, 26, 28, 29, 30, 40, 41, 43, 44, 45, 55, 56, 58, 59, 60, 70, 71, 73, 74, 75, 85], "kwarg": [4, 5, 6, 7, 8, 9, 11, 13, 14, 15, 25, 26, 28, 29, 30, 40, 41, 43, 44, 45, 55, 56, 58, 59, 60, 70, 71, 73, 74, 75, 85], "improv": [4, 95, 96, 258, 263, 421, 452, 898], "jackknif": [4, 95], "term": [4, 265, 269, 271, 274, 276, 277, 279, 288, 289, 292, 301, 302, 305, 312, 313, 316, 325, 326, 329, 339, 340, 344, 351, 352, 368, 420, 424, 425, 440, 898], "offer": [4, 898], "accuraci": 4, "condit": [4, 95, 266, 421, 424, 425, 427, 431, 433, 435, 443, 445, 446, 447, 450, 453, 458, 471, 476, 488, 497, 502, 518, 534, 542, 554, 559, 571, 576, 588, 593, 606, 611, 623, 628, 640, 646, 658, 670, 683, 699, 707, 719, 725, 737, 749, 755, 768, 788, 809, 811, 819, 821, 823, 824, 826, 833, 835, 849, 856, 863, 873, 877, 889, 892, 895, 897, 898], "satisfi": [4, 266, 425, 492, 509, 525, 644, 674, 690, 723, 753, 766, 779, 795, 861], "special": [4, 437, 901], "case": [4, 89, 100, 104, 105, 106, 107, 108, 109, 110, 111, 259, 420, 437, 891], "base": [4, 89, 95, 260, 264, 265, 267, 277, 282, 295, 307, 318, 332, 346, 354, 368, 402, 420, 424, 452, 453, 496, 513, 529, 678, 694, 763, 776, 783, 894, 897, 899], "frac": [4, 10, 100, 104, 105, 106, 107, 108, 109, 110, 111, 120, 130, 144, 154, 164, 174, 184, 194, 204, 214, 224, 234, 271, 277, 424, 433, 437, 646, 725, 755, 768], "z_": [4, 277, 420], "skew": [4, 94, 421, 443, 755, 891, 898], "like": [4, 10, 88, 89, 258, 420, 424, 427, 428, 429, 438, 439, 445, 496, 513, 529, 678, 694, 783, 799, 812, 840, 891, 896, 898, 899], "leav": [4, 95], "out": [4, 94, 95, 424, 892, 897, 899], "block_siz": [5, 8, 9, 238, 239, 240], "arraylik": [5, 6, 7, 8, 9, 13, 28, 43], "random_st": [5, 6, 7, 8, 9, 95, 96, 436, 441, 443, 444, 451, 461, 478, 496, 513, 529, 544, 561, 578, 595, 613, 630, 660, 678, 694, 709, 739, 783, 799, 812, 840, 879, 899], "sourc": [5, 6, 7, 8, 9, 10, 13, 24, 26, 27, 28, 29, 30, 32, 35, 36, 37, 39, 40, 50, 54, 69, 84, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 238, 239, 240, 241, 247, 248, 249, 251, 253, 254, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 354, 368, 385, 400, 402, 415, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446, 447, 448, 449, 450, 451, 452, 453, 456, 458, 459, 464, 465, 467, 484, 496, 501, 503, 532, 533, 534, 539, 540, 541, 542, 543, 547, 548, 550, 556, 557, 558, 559, 560, 564, 565, 567, 571, 575, 576, 577, 581, 582, 584, 590, 591, 592, 593, 594, 598, 599, 601, 606, 608, 610, 611, 612, 616, 617, 619, 625, 626, 627, 628, 629, 633, 634, 636, 640, 642, 643, 644, 646, 647, 649, 650, 651, 653, 654, 657, 658, 659, 663, 664, 666, 678, 681, 682, 683, 706, 707, 708, 712, 713, 715, 721, 722, 723, 725, 726, 728, 729, 730, 732, 733, 734, 735, 736, 737, 738, 742, 743, 745, 749, 751, 752, 753, 755, 756, 758, 759, 760, 762, 763, 764, 765, 766, 768, 769, 771, 772, 773, 775, 776, 786, 787, 788, 793, 794, 795, 797, 798, 799, 802, 803, 804, 805, 809, 812, 813, 819, 822, 836, 857, 859, 860, 861, 863, 864, 866, 867, 868, 870, 871, 872, 873, 874, 875, 876, 877, 878, 879, 882, 883, 885, 887, 889, 891, 900], "length": [5, 7, 8, 9, 10, 90, 91, 95, 96, 97, 238, 239, 240, 262, 263, 265, 267, 271, 276, 281, 294, 392, 420, 421, 424, 427, 428, 429, 437, 438, 439, 440, 442, 445, 496, 502, 513, 518, 529, 534, 678, 683, 694, 699, 783, 788, 799, 812, 840, 891, 897], "wrap": [5, 8, 9, 10, 90], "around": [5, 8, 9, 10, 90, 258, 420], "posit": [5, 6, 7, 8, 9, 12, 13, 27, 28, 42, 43, 48, 57, 58, 72, 73, 87, 118, 128, 142, 152, 162, 172, 182, 192, 202, 212, 222, 232, 259, 265, 266, 421, 424, 433, 435, 496, 513, 529, 678, 694, 783, 799, 811, 812, 835, 840, 895], "ensur": [5, 6, 7, 8, 9, 89, 277, 436, 441, 443, 444, 471, 488, 554, 571, 588, 606, 623, 640, 670, 719, 749, 889], "reproduc": [5, 6, 7, 8, 9, 89, 95, 259, 436, 441, 443, 444, 898], "If": [5, 6, 7, 8, 9, 20, 23, 35, 38, 50, 53, 65, 68, 80, 83, 95, 96, 100, 101, 104, 105, 106, 107, 108, 109, 110, 111, 238, 239, 240, 262, 263, 264, 265, 266, 267, 271, 277, 354, 362, 368, 377, 381, 402, 424, 425, 427, 428, 429, 434, 436, 438, 439, 441, 443, 444, 445, 450, 461, 472, 478, 489, 494, 496, 502, 511, 513, 518, 527, 529, 544, 555, 561, 572, 578, 589, 595, 607, 613, 624, 630, 641, 660, 671, 676, 678, 683, 692, 694, 699, 709, 720, 739, 750, 781, 783, 797, 799, 809, 812, 813, 819, 833, 840, 841, 849, 854, 879, 890, 891, 895, 897], "fresh": [5, 6, 8, 9, 13, 28, 43, 58, 73], "system": [5, 6, 8, 9, 271, 436, 441, 443, 444], "deprec": [5, 6, 8, 9, 92, 93, 96, 436, 441, 443, 444, 652, 731, 761, 774, 869], "ha": [5, 6, 7, 8, 9, 20, 35, 50, 65, 80, 95, 96, 259, 267, 419, 421, 424, 430, 432, 442, 725, 812, 840, 873, 895, 897, 898, 899], "been": [5, 6, 7, 8, 9, 20, 35, 50, 65, 80, 95, 96, 267, 421, 437, 812, 840, 854, 873, 895, 899], "tupl": [5, 6, 7, 8, 9, 12, 17, 18, 22, 23, 27, 32, 33, 37, 38, 39, 42, 47, 48, 52, 53, 54, 57, 62, 63, 67, 68, 72, 77, 78, 82, 83, 87, 456, 459, 465, 475, 477, 478, 482, 490, 492, 496, 507, 509, 513, 523, 525, 529, 541, 543, 544, 548, 558, 560, 561, 565, 575, 577, 578, 582, 592, 594, 595, 599, 610, 612, 613, 617, 627, 629, 630, 634, 642, 644, 653, 657, 659, 660, 664, 672, 674, 678, 688, 690, 694, 706, 708, 709, 713, 721, 723, 732, 736, 738, 739, 743, 751, 753, 762, 764, 766, 775, 777, 779, 783, 793, 795, 799, 812, 840, 859, 861, 870, 876, 878, 879, 883], "pos_data": [5, 6, 7, 8, 9], "kw_data": [5, 6, 7, 8, 9], "type": [5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 19, 23, 25, 26, 27, 28, 29, 30, 31, 32, 34, 38, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 53, 55, 56, 57, 58, 59, 60, 61, 62, 64, 68, 70, 71, 72, 73, 74, 75, 76, 77, 79, 83, 85, 86, 95, 96, 112, 113, 114, 115, 117, 118, 119, 120, 121, 122, 123, 124, 125, 127, 128, 129, 130, 131, 136, 137, 138, 139, 141, 142, 143, 144, 145, 146, 147, 148, 149, 151, 152, 153, 154, 155, 156, 157, 158, 159, 161, 162, 163, 164, 165, 166, 167, 168, 169, 171, 172, 173, 174, 175, 176, 177, 178, 179, 181, 182, 183, 184, 185, 186, 187, 188, 189, 191, 192, 193, 194, 195, 196, 197, 198, 199, 201, 202, 203, 204, 205, 206, 207, 208, 209, 211, 212, 213, 214, 215, 216, 217, 218, 219, 221, 222, 223, 224, 225, 226, 227, 228, 229, 231, 232, 233, 234, 235, 242, 243, 244, 247, 249, 250, 257, 268, 272, 276, 277, 280, 293, 306, 317, 324, 331, 345, 354, 362, 366, 368, 371, 377, 381, 385, 389, 395, 398, 402, 406, 411, 421, 424, 454, 455, 456, 461, 464, 467, 469, 471, 472, 473, 474, 475, 478, 481, 484, 486, 488, 489, 490, 494, 495, 496, 500, 501, 502, 503, 507, 511, 512, 513, 516, 517, 518, 519, 523, 527, 528, 529, 532, 533, 534, 535, 539, 540, 541, 544, 547, 550, 552, 554, 555, 556, 557, 558, 561, 564, 567, 569, 571, 572, 573, 574, 575, 578, 581, 584, 586, 588, 589, 590, 591, 592, 595, 598, 601, 604, 606, 607, 608, 609, 610, 613, 616, 619, 621, 623, 624, 625, 626, 627, 630, 633, 636, 638, 640, 641, 642, 643, 646, 647, 649, 650, 651, 653, 654, 655, 656, 657, 660, 663, 666, 668, 670, 671, 672, 676, 677, 678, 681, 682, 683, 684, 688, 692, 693, 694, 697, 698, 699, 700, 704, 705, 706, 709, 712, 715, 717, 719, 720, 721, 722, 725, 726, 728, 729, 730, 732, 733, 734, 735, 736, 739, 742, 745, 747, 749, 750, 751, 752, 755, 756, 758, 759, 760, 762, 763, 764, 765, 768, 769, 771, 772, 773, 775, 776, 777, 781, 782, 783, 786, 787, 788, 789, 793, 797, 798, 799, 802, 803, 805, 809, 811, 812, 813, 819, 822, 825, 833, 835, 836, 840, 841, 846, 849, 857, 859, 860, 864, 866, 867, 868, 870, 871, 874, 875, 876, 879, 882, 885, 887, 889, 890, 891, 897, 899, 900], "enter": [5, 6, 7, 8, 9, 12, 27, 42, 57, 72, 892], "dict": [5, 6, 7, 8, 9, 11, 12, 14, 15, 17, 18, 22, 23, 25, 26, 27, 29, 30, 32, 33, 37, 38, 39, 40, 41, 42, 44, 45, 47, 48, 52, 53, 54, 55, 56, 57, 59, 60, 62, 63, 67, 68, 70, 71, 72, 74, 75, 77, 78, 82, 83, 85, 279, 292, 305, 316, 329, 344, 494, 496, 511, 513, 527, 529, 676, 678, 692, 694, 781, 783, 797, 799, 812, 840, 900], "note": [5, 6, 7, 8, 9, 10, 11, 14, 26, 29, 41, 44, 56, 59, 71, 74, 100, 101, 104, 105, 106, 107, 108, 109, 110, 111, 120, 130, 144, 154, 164, 174, 184, 194, 204, 214, 224, 234, 238, 239, 240, 247, 248, 258, 262, 263, 264, 265, 266, 267, 269, 271, 274, 276, 277, 362, 368, 377, 381, 398, 421, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 437, 438, 439, 440, 442, 443, 445, 452, 459, 461, 477, 478, 492, 494, 495, 496, 509, 511, 512, 513, 525, 527, 528, 529, 543, 544, 560, 561, 577, 578, 594, 595, 612, 613, 629, 630, 644, 646, 650, 654, 659, 660, 674, 676, 677, 678, 690, 692, 693, 694, 708, 709, 723, 725, 729, 733, 738, 739, 753, 755, 759, 763, 766, 768, 772, 776, 779, 781, 782, 783, 795, 797, 798, 799, 812, 840, 861, 863, 867, 871, 872, 873, 878, 879, 891, 895, 897, 899], "support": [5, 6, 7, 8, 9, 91, 95, 96, 97, 277, 288, 301, 312, 325, 339, 351, 366, 385, 417, 424, 461, 470, 478, 487, 494, 496, 511, 513, 527, 529, 544, 553, 561, 570, 578, 587, 595, 605, 613, 622, 630, 639, 660, 669, 676, 678, 692, 694, 709, 718, 739, 748, 781, 783, 797, 799, 812, 813, 819, 840, 841, 849, 879, 888, 891, 892, 895, 899], "date": [5, 6, 7, 8, 9, 91, 97, 421, 424, 496, 513, 529, 678, 694, 783, 799, 812, 813, 840, 841, 891, 895, 896, 897, 898, 899], "accessibli": [5, 6, 7, 8, 9], "attribut": [5, 6, 7, 8, 9, 424, 897, 899], "To": [5, 6, 7, 8, 9, 420, 422], "you": [5, 6, 7, 8, 9, 895, 899], "after": [5, 6, 7, 8, 9, 95, 96, 424, 496, 513, 529, 678, 694, 783, 799, 897], "reserv": [5, 6, 7, 8, 9, 898], "instanc": [5, 7, 8, 9, 13, 16, 19, 28, 31, 34, 43, 46, 49, 58, 61, 64, 73, 76, 79, 87, 89, 95, 96, 115, 125, 139, 149, 159, 169, 179, 189, 199, 209, 219, 229, 272, 273, 354, 366, 385, 395, 402, 411, 436, 441, 443, 444, 461, 478, 496, 513, 529, 544, 561, 578, 595, 613, 630, 660, 678, 694, 709, 739, 783, 799, 812, 815, 822, 840, 843, 857, 879, 898], "optim": [5, 8, 9, 10, 95, 100, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 117, 120, 121, 122, 123, 127, 130, 131, 136, 137, 141, 144, 145, 146, 147, 151, 154, 155, 156, 157, 161, 164, 165, 166, 167, 171, 174, 175, 176, 177, 181, 184, 185, 186, 187, 191, 194, 195, 196, 197, 201, 204, 205, 206, 207, 211, 214, 215, 216, 217, 221, 224, 225, 226, 227, 231, 234, 235, 268, 271, 277, 354, 368, 402, 450, 490, 492, 494, 507, 509, 511, 523, 525, 527, 642, 644, 654, 672, 674, 676, 688, 690, 692, 721, 723, 733, 751, 753, 763, 764, 766, 776, 777, 779, 781, 793, 795, 797, 837, 846, 859, 861, 871, 891, 896, 897, 898, 899], "exp": [5, 8, 10], "access": [5, 6, 7, 8, 9, 248, 259, 421, 898], "wai": [5, 6, 7, 8, 9, 898], "retain": [5, 6, 7, 8, 9], "wa": [5, 6, 7, 8, 9, 91, 94, 95, 96, 97, 421, 447, 450, 496, 513, 529, 678, 694, 783, 799, 895], "avail": [5, 6, 7, 8, 9, 259, 262, 265, 421, 424, 452, 461, 478, 496, 513, 529, 544, 561, 578, 595, 613, 630, 660, 678, 694, 709, 739, 783, 799, 812, 840, 879, 897, 898, 899], "syntax": [5, 6, 7, 8, 9, 496, 513, 529, 678, 694, 783, 799, 812, 840], "z": [5, 6, 7, 8, 9, 87, 96, 100, 104, 105, 106, 107, 108, 109, 110, 111, 263, 277, 368, 421, 650, 729, 759, 772, 867], "bs_x": [5, 6, 7, 8, 9], "bs_y": [5, 6, 7, 8, 9], "bs_z": [5, 6, 7, 8, 9], "1234": [5, 6, 7, 8, 9, 438], "properti": [5, 6, 7, 8, 9, 16, 18, 19, 23, 31, 33, 34, 38, 46, 48, 49, 53, 61, 63, 64, 68, 76, 78, 79, 83, 91, 96, 97, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 238, 239, 240, 242, 243, 244, 250, 257, 262, 263, 264, 265, 266, 267, 270, 272, 273, 275, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 312, 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 324, 325, 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 351, 352, 353, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 365, 367, 369, 370, 371, 372, 373, 374, 375, 376, 377, 378, 379, 380, 381, 382, 383, 384, 386, 387, 388, 389, 390, 391, 392, 393, 394, 396, 397, 398, 399, 401, 403, 404, 405, 406, 407, 408, 409, 410, 412, 413, 414, 416, 420, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446, 447, 448, 449, 450, 451, 453, 457, 460, 462, 463, 466, 468, 470, 472, 479, 480, 483, 485, 487, 489, 493, 496, 497, 498, 499, 504, 505, 506, 510, 513, 514, 515, 520, 521, 522, 526, 529, 530, 531, 536, 537, 538, 545, 546, 549, 551, 553, 555, 562, 563, 566, 568, 570, 572, 579, 580, 583, 585, 587, 589, 596, 597, 600, 602, 603, 605, 607, 614, 615, 618, 620, 622, 624, 631, 632, 635, 637, 639, 641, 645, 648, 652, 661, 662, 665, 667, 669, 671, 675, 678, 679, 680, 685, 686, 687, 691, 694, 695, 696, 701, 702, 703, 710, 711, 714, 716, 718, 720, 724, 727, 731, 740, 741, 744, 746, 748, 750, 754, 757, 761, 767, 770, 774, 780, 783, 784, 785, 790, 791, 792, 796, 799, 800, 801, 806, 807, 808, 810, 811, 812, 814, 815, 816, 817, 818, 820, 821, 823, 824, 825, 826, 827, 828, 829, 830, 831, 832, 834, 835, 837, 838, 839, 840, 842, 843, 844, 845, 846, 847, 848, 850, 851, 852, 853, 854, 855, 856, 858, 862, 865, 869, 880, 881, 884, 886, 888, 890, 892, 893, 898, 900], "uniform": 6, "integ": [6, 94, 96, 100, 104, 105, 106, 107, 108, 109, 110, 111, 116, 126, 140, 150, 160, 170, 180, 190, 200, 210, 220, 230, 238, 239, 240, 246, 252, 256, 259, 277, 354, 368, 402, 421, 424, 450, 465, 482, 496, 513, 529, 548, 565, 582, 599, 617, 634, 664, 678, 694, 713, 743, 783, 799, 812, 813, 840, 841, 883, 891], "equival": [6, 259, 368, 427, 438, 496, 513, 529, 678, 694, 783, 799, 812, 840, 891, 899], "structur": [7, 89, 267, 417, 420, 421, 424, 427, 428, 438, 895], "unequ": 7, "cross": [7, 273, 276, 277, 368, 420], "section": [7, 273, 276, 277, 368, 420], "relat": [7, 95, 96, 258, 433, 435, 891, 898], "inappropri": 7, "without": [8, 14, 29, 44, 59, 74, 89, 427, 428, 429, 438, 439, 445, 891], "circularblockbootstrap": [8, 9, 10, 58, 73, 90], "expon": 9, "arraylike1d": [10, 461], "arraylike2d": 10, "dimension": [10, 15, 25, 30, 40, 45, 55, 60, 70, 75, 85, 89, 895, 896], "oper": [10, 433], "b_sb": 10, "b_cb": 10, "algorithm": [10, 262, 267, 901], "its": [10, 20, 35, 50, 65, 80, 251, 258, 259, 368, 420, 421, 898], "correct": [10, 14, 29, 44, 59, 74, 259, 495, 512, 528, 677, 693, 782, 798], "tune": 10, "k_n": 10, "consecut": 10, "insid": [10, 895], "conserv": 10, "band": 10, "pm": 10, "log_": 10, "maximum": [10, 262, 263, 267, 271, 276, 281, 294, 392, 424, 440, 891, 895, 896, 898, 899], "lceil": 10, "rceil": 10, "_i": 10, "2g": 10, "d_": [10, 269, 274], "sum_": [10, 269, 271, 274, 277, 398, 424, 425, 426, 427, 428, 431, 433, 435, 437, 438, 440], "k": [10, 238, 239, 240, 259, 264, 265, 267, 271, 354, 368, 402, 425, 427, 428, 431, 435, 438, 439, 502, 518, 683, 699, 899], "h": [10, 95, 100, 104, 105, 106, 107, 108, 109, 110, 111, 237, 239, 262, 263, 264, 269, 271, 274, 424, 496, 513, 529, 678, 694, 783, 799, 812, 840, 895, 897, 899], "gamma_": [10, 425, 431, 435], "c_": 10, "x_k": 10, "bar": [10, 421, 438], "x_": [10, 95, 271, 895], "remain": [10, 895], "constant": [10, 95, 117, 120, 127, 130, 141, 144, 151, 154, 161, 164, 171, 174, 181, 184, 191, 194, 201, 204, 211, 214, 221, 224, 231, 234, 262, 263, 264, 265, 267, 269, 271, 274, 276, 277, 417, 420, 421, 424, 427, 428, 429, 430, 438, 439, 496, 502, 513, 518, 529, 534, 678, 683, 694, 699, 755, 783, 788, 799, 813, 841, 891, 896, 899], "c_i": 10, "taken": 10, "andrew": [10, 95, 108, 264, 266, 267, 268, 419], "patton": 10, "matlab": 10, "program": 10, "do": [10, 12, 27, 42, 57, 72, 269, 274, 424, 438, 496, 513, 529, 678, 694, 783, 799, 812, 813, 840, 841, 872, 897, 900], "autocovari": [10, 277], "rather": [10, 96, 258, 259, 263, 267, 424, 897, 898], "dimitri": 10, "halbert": 10, "white": [10, 237, 239, 258, 277, 354, 368, 402, 421, 896, 898], "2004": [10, 264, 267], "automat": [10, 91, 95, 96, 97, 262, 263, 264, 265, 267, 268, 276, 281, 294, 417, 419, 421, 427, 428, 429, 438, 439, 445, 891], "select": [10, 95, 96, 121, 131, 145, 155, 165, 175, 185, 195, 205, 215, 225, 235, 240, 262, 263, 264, 267, 268, 271, 273, 276, 281, 294, 392, 417, 419, 421, 466, 468, 483, 485, 549, 551, 566, 568, 583, 585, 600, 602, 618, 620, 635, 637, 665, 667, 714, 716, 744, 746, 884, 886], "review": [10, 264, 443], "23": [10, 237, 239, 421, 896], "53": [10, 420, 896], "70": [10, 259], "doi": [10, 236], "1081": 10, "etc": [10, 424, 898], "120028836": 10, "2009": [10, 420, 424, 897], "28": [10, 421, 424, 891, 895, 896, 897, 898], "372": 10, "375": 10, "1080": 10, "07474930802459016": 10, "func": [11, 14, 15, 25, 26, 29, 30, 40, 41, 44, 45, 55, 56, 59, 60, 70, 71, 74, 75, 85, 891, 896, 897, 898], "callabl": [11, 14, 15, 25, 26, 29, 30, 40, 41, 44, 45, 55, 56, 59, 60, 70, 71, 74, 75, 85, 95, 258, 452, 461, 465, 478, 482, 496, 513, 529, 544, 548, 561, 565, 578, 582, 595, 599, 613, 617, 630, 634, 653, 660, 664, 678, 694, 709, 713, 732, 739, 743, 762, 775, 783, 799, 812, 840, 870, 879, 883], "ndarrai": [11, 12, 14, 15, 17, 18, 21, 22, 23, 24, 25, 26, 27, 29, 30, 32, 33, 36, 37, 38, 39, 40, 41, 42, 44, 45, 47, 48, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 62, 63, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 77, 78, 81, 82, 83, 84, 85, 95, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 118, 128, 132, 133, 134, 135, 142, 152, 162, 172, 182, 192, 202, 212, 222, 232, 238, 239, 240, 246, 247, 252, 253, 256, 262, 263, 264, 265, 266, 267, 268, 269, 271, 274, 276, 277, 290, 303, 314, 327, 342, 353, 427, 428, 429, 434, 438, 439, 445, 446, 447, 450, 454, 455, 456, 458, 459, 465, 467, 469, 471, 473, 474, 475, 476, 477, 478, 482, 484, 486, 488, 491, 492, 494, 495, 496, 501, 502, 503, 505, 506, 508, 509, 511, 512, 513, 517, 518, 519, 521, 522, 524, 525, 527, 528, 529, 533, 534, 535, 537, 538, 539, 540, 541, 542, 543, 544, 548, 550, 552, 554, 556, 557, 558, 559, 560, 561, 565, 567, 569, 571, 573, 574, 575, 576, 577, 578, 582, 584, 586, 588, 590, 591, 592, 593, 594, 595, 599, 601, 604, 606, 608, 609, 610, 611, 612, 613, 617, 619, 621, 623, 625, 626, 627, 628, 629, 630, 634, 636, 638, 640, 642, 643, 644, 646, 647, 650, 651, 653, 654, 655, 656, 657, 658, 659, 660, 664, 666, 668, 670, 673, 674, 676, 677, 678, 682, 683, 684, 686, 687, 689, 690, 692, 693, 694, 698, 699, 700, 702, 703, 704, 705, 706, 707, 708, 709, 713, 715, 717, 719, 721, 722, 723, 725, 726, 729, 730, 732, 733, 734, 735, 736, 737, 738, 739, 743, 745, 747, 749, 751, 752, 753, 755, 756, 759, 760, 762, 763, 764, 765, 766, 768, 769, 772, 773, 775, 776, 778, 779, 781, 782, 783, 787, 788, 789, 791, 792, 794, 795, 797, 798, 799, 803, 804, 805, 807, 811, 812, 813, 820, 821, 828, 829, 830, 831, 835, 840, 841, 851, 856, 859, 860, 861, 863, 864, 867, 868, 870, 871, 872, 873, 874, 875, 876, 877, 878, 879, 883, 885, 887, 889, 891, 895], "rep": [11, 12, 14, 15, 25, 26, 27, 29, 30, 40, 41, 42, 44, 45, 55, 56, 57, 59, 60, 70, 71, 72, 74, 75, 85, 238, 239, 240, 259], "str": [11, 12, 14, 15, 17, 18, 22, 23, 25, 26, 27, 29, 30, 32, 33, 37, 38, 39, 40, 41, 42, 44, 45, 47, 48, 52, 53, 54, 55, 56, 57, 59, 60, 62, 63, 67, 68, 70, 71, 72, 74, 75, 77, 78, 82, 83, 85, 101, 119, 129, 143, 153, 163, 173, 183, 193, 203, 213, 223, 233, 259, 270, 272, 273, 275, 277, 278, 279, 283, 288, 289, 291, 292, 296, 302, 304, 305, 308, 312, 313, 315, 316, 319, 324, 325, 326, 328, 329, 333, 339, 340, 343, 344, 347, 351, 352, 354, 357, 368, 371, 374, 387, 393, 394, 395, 401, 402, 403, 408, 409, 410, 411, 416, 447, 450, 462, 464, 479, 481, 494, 495, 496, 498, 500, 511, 512, 513, 514, 516, 527, 528, 529, 530, 532, 545, 547, 562, 564, 579, 581, 596, 598, 614, 616, 631, 633, 648, 649, 661, 663, 676, 677, 678, 679, 681, 692, 693, 694, 695, 697, 710, 712, 727, 728, 740, 742, 757, 758, 770, 771, 781, 782, 783, 784, 786, 797, 798, 799, 800, 802, 812, 813, 819, 840, 841, 849, 865, 866, 880, 882, 900], "extra": [11, 14, 15, 25, 26, 29, 30, 40, 41, 44, 45, 55, 56, 59, 60, 70, 71, 74, 75, 85, 239, 240], "conflict": [11, 26, 41, 56, 71], "nparam": [11, 26, 41, 56, 71], "row": [11, 14, 26, 29, 41, 44, 56, 59, 71, 74, 424, 496, 513, 529, 678, 694, 783, 799, 812, 836, 840, 895], "correspond": [11, 14, 26, 29, 41, 44, 56, 59, 71, 74, 239, 250, 259, 471, 488, 494, 496, 511, 513, 527, 529, 554, 571, 588, 606, 623, 640, 670, 676, 678, 692, 694, 719, 749, 781, 783, 797, 799, 812, 840, 889, 897, 898], "iter": [11, 12, 24, 26, 27, 39, 41, 42, 54, 56, 57, 69, 71, 72, 84, 92, 452, 494, 511, 527, 676, 692, 781, 797, 891, 896, 897, 898], "up": [11, 14, 26, 29, 41, 44, 56, 59, 71, 74, 267, 421, 424, 437, 496, 513, 529, 678, 694, 783, 799, 812, 840, 895, 897, 899], "append": [11, 14, 26, 29, 41, 44, 56, 59, 71, 74, 259, 895, 897], "befor": [11, 14, 26, 29, 41, 44, 56, 59, 71, 74, 100, 104, 105, 106, 107, 108, 109, 110, 111, 248, 263, 421, 494, 511, 527, 676, 692, 781, 797, 854, 899], "randn": [11, 12, 14, 15, 25, 26, 27, 29, 30, 40, 41, 42, 44, 45, 55, 56, 57, 59, 60, 70, 71, 72, 74, 75, 85, 87, 89, 259, 428, 429, 438, 439, 445, 502, 518, 683, 699], "over": [12, 27, 42, 57, 72, 94, 899], "calcul": [12, 27, 42, 57, 72, 95, 113, 117, 118, 123, 127, 128, 137, 141, 142, 147, 151, 152, 157, 161, 162, 167, 171, 172, 177, 181, 182, 187, 191, 192, 197, 201, 202, 207, 211, 212, 217, 221, 222, 227, 231, 232, 264, 267, 646, 647, 725, 726, 755, 756, 768, 769, 864], "kei": [12, 27, 42, 57, 72, 389, 406, 417, 496, 513, 529, 678, 694, 783, 799, 812, 840, 895], "problem": [12, 27, 42, 57, 72, 89, 96, 421], "doe": [12, 27, 42, 57, 72, 90, 259, 277, 362, 381, 421, 471, 488, 494, 511, 527, 554, 571, 588, 606, 623, 640, 670, 676, 692, 719, 749, 781, 797, 873, 889, 891, 897], "output": [12, 27, 42, 57, 72, 88, 259, 494, 511, 527, 653, 676, 692, 732, 762, 775, 781, 797, 870, 891, 897, 898], "arang": [12, 27, 42, 57, 72, 89, 895, 897, 899], "posdata": [12, 27, 42, 57, 72], "kwdata": [12, 27, 42, 57, 72], "someth": [12, 27, 42, 57, 72], "prng": [13, 16, 28, 31, 43, 46, 58, 61, 73, 76], "norm": [14, 29, 44, 59, 74], "bc": [14, 29, 44, 59, 74, 755], "bca": [14, 29, 44, 59, 74, 95], "float": [14, 15, 17, 22, 23, 25, 29, 30, 32, 37, 38, 40, 44, 45, 47, 52, 53, 55, 59, 60, 62, 67, 68, 70, 74, 75, 77, 82, 83, 85, 99, 100, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 117, 120, 121, 122, 123, 127, 130, 131, 136, 137, 141, 144, 145, 146, 147, 151, 154, 155, 156, 157, 161, 164, 165, 166, 167, 171, 174, 175, 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725, 726, 729, 730, 732, 734, 735, 736, 737, 739, 743, 747, 749, 751, 752, 755, 756, 759, 760, 762, 764, 765, 768, 769, 772, 773, 775, 777, 778, 781, 782, 788, 793, 794, 797, 798, 804, 808, 810, 814, 819, 832, 834, 836, 842, 849, 852, 853, 854, 859, 860, 863, 864, 867, 868, 870, 872, 874, 875, 876, 877, 879, 883, 887, 889, 891, 895, 900], "tail": [14, 29, 44, 59, 74, 424, 443, 654, 895, 898], "bool": [14, 15, 20, 25, 29, 30, 35, 40, 44, 45, 50, 55, 59, 60, 65, 70, 74, 75, 80, 85, 99, 100, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 114, 116, 124, 126, 138, 140, 148, 150, 158, 160, 168, 170, 178, 180, 188, 190, 198, 200, 208, 210, 218, 220, 228, 230, 239, 240, 262, 263, 266, 270, 271, 272, 277, 330, 334, 336, 354, 368, 385, 402, 425, 427, 428, 429, 434, 438, 439, 440, 445, 446, 447, 448, 450, 457, 470, 487, 491, 494, 496, 508, 511, 513, 524, 527, 529, 553, 570, 587, 605, 622, 639, 646, 669, 673, 676, 678, 689, 692, 694, 718, 725, 748, 755, 768, 778, 781, 783, 794, 797, 799, 809, 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This must be overridden when creating new bootstraps."], [25, 3, 1, 0, "var", "Compute parameter variance using bootstrap"]], "arch.bootstrap.CircularBlockBootstrap.apply": [[11, 2, 2, 0, "extra_kwargs", "Extra keyword arguments to use when calling func.  Must not conflict with keyword arguments used to initialize bootstrap"], [11, 2, 2, 0, "func", "Function the computes parameter values.  See Notes for requirements"], [11, 2, 2, 0, "reps", "Number of bootstrap replications"]], "arch.bootstrap.CircularBlockBootstrap.bootstrap": [[12, 2, 2, 0, "reps", "Number of bootstrap replications"]], "arch.bootstrap.CircularBlockBootstrap.clone": [[13, 2, 2, 0, "args", "Positional arguments to bootstrap"], [13, 2, 2, 0, "kwargs", "Keyword arguments to bootstrap"], [13, 2, 2, 0, "seed", "The seed value to pass to the closed generator"]], "arch.bootstrap.CircularBlockBootstrap.conf_int": [[14, 2, 2, 0, "extra_kwargs", "Extra keyword arguments to use when calling func and std_err_func, when appropriate"], [14, 2, 2, 0, "func", "Function the computes parameter values.  See Notes for requirements"], [14, 2, 2, 0, "method", "One of 'basic', 'percentile', 'studentized', 'norm' (identical to 'var', 'cov'), 'bc' (identical to 'debiased', 'bias-corrected'), or 'bca'"], [14, 2, 2, 0, "reps", "Number of bootstrap replications"], [14, 2, 2, 0, "reuse", "Flag indicating whether to reuse previously computed bootstrap results.  This allows alternative methods to be compared without rerunning the bootstrap simulation.  Reuse is ignored if reps is not the same across multiple runs, func changes across calls, or method is 'studentized'."], [14, 2, 2, 0, "sampling", "Type of sampling to use: 'nonparametric', 'semi-parametric' (or 'semi') or 'parametric'.  The default is 'nonparametric'.  See notes about the changes to func required when using 'semi' or 'parametric'."], [14, 2, 2, 0, "size", "Coverage of confidence interval"], [14, 2, 2, 0, "std_err_func", "Function to use when standardizing estimated parameters when using the studentized bootstrap.  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Default is True."], [25, 2, 2, 0, "reps", "Number of bootstrap replications"]], "arch.bootstrap.IIDBootstrap.__init__": [[6, 2, 2, 0, "args", "Positional arguments to bootstrap"], [6, 2, 2, 0, "kwargs", "Keyword arguments to bootstrap"], [6, 2, 2, 0, "random_state", "RandomState to use to ensure reproducable results. Cannot be used with seed"], [6, 2, 2, 0, "seed", "Seed to use to ensure reproducable results. If an int, passes the value to value to np.random.default_rng. If None, a fresh Generator is constructed with system-provided entropy."]], "arch.bootstrap.IIDBootstrap": [[26, 3, 1, 0, "apply", "Applies a function to bootstrap replicated data"], [27, 3, 1, 0, "bootstrap", "Iterator for use when bootstrapping"], [28, 3, 1, 0, "clone", "Clones the bootstrap using different data with a fresh prng."], [29, 3, 1, 0, "conf_int", "Function the computes parameter values.  See Notes for requirements"], [30, 3, 1, 0, "cov", "Compute parameter covariance using bootstrap"], [6, 4, 1, 0, "data", "Two-element tuple with the pos_data in the first position and kw_data in the second (pos_data, kw_data)"], [31, 5, 1, 0, "generator", "Set or get the instance PRNG"], [32, 3, 1, 0, "get_state", "Gets the state of the bootstrap's random number generator"], [33, 5, 1, 0, "index", "The current index of the bootstrap"], [6, 4, 1, 0, "kw_data", "Dictionary containing the keyword arguments"], [6, 4, 1, 0, "pos_data", "Tuple containing the positional arguments (in the order entered)"], [34, 5, 1, 0, "random_state", "Set or get the instance random state"], [35, 3, 1, 0, "reset", "Resets the bootstrap to either its initial state or the last seed."], [36, 3, 1, 0, "seed", "Reseeds the bootstrap's random number generator"], [37, 3, 1, 0, "set_state", "Sets the state of the bootstrap's random number generator"], [38, 5, 1, 0, "state", "Set or get the generator's state"], [39, 3, 1, 0, "update_indices", "Update indices for the next iteration of the bootstrap.  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See Notes for requirements"], [26, 2, 2, 0, "reps", "Number of bootstrap replications"]], "arch.bootstrap.IIDBootstrap.bootstrap": [[27, 2, 2, 0, "reps", "Number of bootstrap replications"]], "arch.bootstrap.IIDBootstrap.clone": [[28, 2, 2, 0, "args", "Positional arguments to bootstrap"], [28, 2, 2, 0, "kwargs", "Keyword arguments to bootstrap"], [28, 2, 2, 0, "seed", "The seed value to pass to the closed generator"]], "arch.bootstrap.IIDBootstrap.conf_int": [[29, 2, 2, 0, "extra_kwargs", "Extra keyword arguments to use when calling func and std_err_func, when appropriate"], [29, 2, 2, 0, "func", "Function the computes parameter values.  See Notes for requirements"], [29, 2, 2, 0, "method", "One of 'basic', 'percentile', 'studentized', 'norm' (identical to 'var', 'cov'), 'bc' (identical to 'debiased', 'bias-corrected'), or 'bca'"], [29, 2, 2, 0, "reps", "Number of bootstrap replications"], [29, 2, 2, 0, "reuse", "Flag indicating whether to reuse previously computed bootstrap results.  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See Notes for requirements"], [41, 2, 2, 0, "reps", "Number of bootstrap replications"]], "arch.bootstrap.IndependentSamplesBootstrap.bootstrap": [[42, 2, 2, 0, "reps", "Number of bootstrap replications"]], "arch.bootstrap.IndependentSamplesBootstrap.clone": [[43, 2, 2, 0, "args", "Positional arguments to bootstrap"], [43, 2, 2, 0, "kwargs", "Keyword arguments to bootstrap"], [43, 2, 2, 0, "seed", "The seed value to pass to the closed generator"]], "arch.bootstrap.IndependentSamplesBootstrap.conf_int": [[44, 2, 2, 0, "extra_kwargs", "Extra keyword arguments to use when calling func and std_err_func, when appropriate"], [44, 2, 2, 0, "func", "Function the computes parameter values.  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If False or if no seed has been set, the bootstrap will be reset to the initial state.  Default is True"]], "arch.bootstrap.IndependentSamplesBootstrap.seed": [[51, 2, 2, 0, "value", "Value to use as the seed."]], "arch.bootstrap.IndependentSamplesBootstrap.set_state": [[52, 2, 2, 0, "state", "Dictionary or tuple containing the state."]], "arch.bootstrap.IndependentSamplesBootstrap.var": [[55, 2, 2, 0, "extra_kwargs", "Dictionary of extra keyword arguments to pass to func"], [55, 2, 2, 0, "func", "Callable function that returns the statistic of interest as a 1-d array"], [55, 2, 2, 0, "recenter", "Whether to center the bootstrap variance estimator on the average of the bootstrap samples (True) or to center on the original sample estimate (False).  Default is True."], [55, 2, 2, 0, "reps", "Number of bootstrap replications"]], "arch.bootstrap.MCS.__init__": [[238, 2, 2, 0, "block_size", "Length of window to use in the bootstrap.  If not provided, sqrt(T) is used.  In general, this should be provided and chosen to be appropriate for the data."], [238, 2, 2, 0, "bootstrap", "Bootstrap to use.  Options are 'stationary' or 'sb': Stationary bootstrap (Default) 'circular' or 'cbb': Circular block bootstrap 'moving block' or 'mbb': Moving block bootstrap"], [238, 2, 2, 0, "losses", "T by k array containing losses from a set of models"], [238, 2, 2, 0, "method", "MCS test and elimination implementation method, either 'max' or 'R'. Default is 'R'."], [238, 2, 2, 0, "reps", "Number of bootstrap replications to uses.  Default is 1000."], [238, 2, 2, 0, "seed", "Seed value to use when creating the bootstrap used in the comparison. If an integer or None, the NumPy default_rng is used with the seed value.  If a Generator or a RandomState, the argument is used."], [238, 2, 2, 0, "size", "Value in (0,1) to use as the test size when implementing the mcs. Default value is 0.05."]], "arch.bootstrap.MCS": [[241, 3, 1, 0, "compute", "Compute the set of models in the confidence set."], [242, 5, 1, 0, "excluded", "List of model indices that are excluded from the MCS"], [243, 5, 1, 0, "included", "List of model indices that are included in the MCS"], [244, 5, 1, 0, "pvalues", "Model p-values for inclusion in the MCS"], [245, 3, 1, 0, "reset", "Reset the bootstrap to it's initial state."], [246, 3, 1, 0, "seed", "Seed the bootstrap's random number generator"]], "arch.bootstrap.MCS.seed": [[246, 2, 2, 0, "value", "Integer to use as the seed"]], "arch.bootstrap.MovingBlockBootstrap.__init__": [[8, 2, 2, 0, "args", "Positional arguments to bootstrap"], [8, 2, 2, 0, "block_size", "Size of block to use"], [8, 2, 2, 0, "kwargs", "Keyword arguments to bootstrap"], [8, 2, 2, 0, "random_state", "RandomState to use to ensure reproducable results. Cannot be used with seed"], [8, 2, 2, 0, "seed", "Seed to use to ensure reproducable results. If an int, passes the value to value to np.random.default_rng. If None, a fresh Generator is constructed with system-provided entropy."]], "arch.bootstrap.MovingBlockBootstrap": [[56, 3, 1, 0, "apply", "Applies a function to bootstrap replicated data"], [57, 3, 1, 0, "bootstrap", "Iterator for use when bootstrapping"], [58, 3, 1, 0, "clone", "Clones the bootstrap using different data with a fresh prng."], [59, 3, 1, 0, "conf_int", "Function the computes parameter values.  See Notes for requirements"], [60, 3, 1, 0, "cov", "Compute parameter covariance using bootstrap"], [8, 4, 1, 0, "data", "Two-element tuple with the pos_data in the first position and kw_data in the second (pos_data, kw_data)"], [61, 5, 1, 0, "generator", "Set or get the instance PRNG"], [62, 3, 1, 0, "get_state", "Gets the state of the bootstrap's random number generator"], [63, 5, 1, 0, "index", "The current index of the bootstrap"], [8, 4, 1, 0, "kw_data", "Dictionary containing the keyword arguments"], [8, 4, 1, 0, "pos_data", "Tuple containing the positional arguments (in the order entered)"], [64, 5, 1, 0, "random_state", "Set or get the instance random state"], [65, 3, 1, 0, "reset", "Resets the bootstrap to either its initial state or the last seed."], [66, 3, 1, 0, "seed", "Reseeds the bootstrap's random number generator"], [67, 3, 1, 0, "set_state", "Sets the state of the bootstrap's random number generator"], [68, 5, 1, 0, "state", "Set or get the generator's state"], [69, 3, 1, 0, "update_indices", "Update indices for the next iteration of the bootstrap.  This must be overridden when creating new bootstraps."], [70, 3, 1, 0, "var", "Compute parameter variance using bootstrap"]], "arch.bootstrap.MovingBlockBootstrap.apply": [[56, 2, 2, 0, "extra_kwargs", "Extra keyword arguments to use when calling func.  Must not conflict with keyword arguments used to initialize bootstrap"], [56, 2, 2, 0, "func", "Function the computes parameter values.  See Notes for requirements"], [56, 2, 2, 0, "reps", "Number of bootstrap replications"]], "arch.bootstrap.MovingBlockBootstrap.bootstrap": [[57, 2, 2, 0, "reps", "Number of bootstrap replications"]], "arch.bootstrap.MovingBlockBootstrap.clone": [[58, 2, 2, 0, "args", "Positional arguments to bootstrap"], [58, 2, 2, 0, "kwargs", "Keyword arguments to bootstrap"], [58, 2, 2, 0, "seed", "The seed value to pass to the closed generator"]], "arch.bootstrap.MovingBlockBootstrap.conf_int": [[59, 2, 2, 0, "extra_kwargs", "Extra keyword arguments to use when calling func and std_err_func, when appropriate"], [59, 2, 2, 0, "func", "Function the computes parameter values.  See Notes for requirements"], [59, 2, 2, 0, "method", "One of 'basic', 'percentile', 'studentized', 'norm' (identical to 'var', 'cov'), 'bc' (identical to 'debiased', 'bias-corrected'), or 'bca'"], [59, 2, 2, 0, "reps", "Number of bootstrap replications"], [59, 2, 2, 0, "reuse", "Flag indicating whether to reuse previously computed bootstrap results.  This allows alternative methods to be compared without rerunning the bootstrap simulation.  Reuse is ignored if reps is not the same across multiple runs, func changes across calls, or method is 'studentized'."], [59, 2, 2, 0, "sampling", "Type of sampling to use: 'nonparametric', 'semi-parametric' (or 'semi') or 'parametric'.  The default is 'nonparametric'.  See notes about the changes to func required when using 'semi' or 'parametric'."], [59, 2, 2, 0, "size", "Coverage of confidence interval"], [59, 2, 2, 0, "std_err_func", "Function to use when standardizing estimated parameters when using the studentized bootstrap.  Providing an analytical function eliminates the need for a nested bootstrap"], [59, 2, 2, 0, "studentize_reps", "Number of bootstraps to use in the inner bootstrap when using the studentized bootstrap.  Ignored when std_err_func is provided"], [59, 2, 2, 0, "tail", "One of 'two', 'upper' or 'lower'."]], "arch.bootstrap.MovingBlockBootstrap.cov": [[60, 2, 2, 0, "extra_kwargs", "Dictionary of extra keyword arguments to pass to func"], [60, 2, 2, 0, "func", "Callable function that returns the statistic of interest as a 1-d array"], [60, 2, 2, 0, "recenter", "Whether to center the bootstrap variance estimator on the average of the bootstrap samples (True) or to center on the original sample estimate (False).  Default is True."], [60, 2, 2, 0, "reps", "Number of bootstrap replications"]], "arch.bootstrap.MovingBlockBootstrap.reset": [[65, 2, 2, 0, "use_seed", "Flag indicating whether to use the last seed if provided.  If False or if no seed has been set, the bootstrap will be reset to the initial state.  Default is True"]], "arch.bootstrap.MovingBlockBootstrap.seed": [[66, 2, 2, 0, "value", "Value to use as the seed."]], "arch.bootstrap.MovingBlockBootstrap.set_state": [[67, 2, 2, 0, "state", "Dictionary or tuple containing the state."]], "arch.bootstrap.MovingBlockBootstrap.var": [[70, 2, 2, 0, "extra_kwargs", "Dictionary of extra keyword arguments to pass to func"], [70, 2, 2, 0, "func", "Callable function that returns the statistic of interest as a 1-d array"], [70, 2, 2, 0, "recenter", "Whether to center the bootstrap variance estimator on the average of the bootstrap samples (True) or to center on the original sample estimate (False).  Default is True."], [70, 2, 2, 0, "reps", "Number of bootstrap replications"]], "arch.bootstrap.SPA.__init__": [[239, 2, 2, 0, "benchmark", "T element array of benchmark model losses"], [239, 2, 2, 0, "block_size", "Length of window to use in the bootstrap.  If not provided, sqrt(T) is used.  In general, this should be provided and chosen to be appropriate for the data."], [239, 2, 2, 0, "bootstrap", "Bootstrap to use.  Options are 'stationary' or 'sb': Stationary bootstrap (Default) 'circular' or 'cbb': Circular block bootstrap 'moving block' or 'mbb': Moving block bootstrap"], [239, 2, 2, 0, "models", "T  by k element array of alternative model losses"], [239, 2, 2, 0, "nested", "Flag indicating to use a nested bootstrap to compute variances for studentization.  Default is False.  Note that this can be slow since the procedure requires k extra bootstraps."], [239, 2, 2, 0, "reps", "Number of bootstrap replications to uses.  Default is 1000."], [239, 2, 2, 0, "seed", "Seed value to use when creating the bootstrap used in the comparison. If an integer or None, the NumPy default_rng is used with the seed value.  If a Generator or a RandomState, the argument is used."], [239, 2, 2, 0, "studentize", "Flag indicating to studentize loss differentials. Default is True"]], "arch.bootstrap.SPA": [[247, 3, 1, 0, "better_models", "Returns set of models rejected as being equal-or-worse than the benchmark"], [248, 3, 1, 0, "compute", "Compute the bootstrap pvalue."], [249, 3, 1, 0, "critical_values", "Returns data-dependent critical values"], [250, 5, 1, 0, "pvalues", "P-values corresponding to the lower, consistent and upper p-values."], [251, 3, 1, 0, "reset", "Reset the bootstrap to its initial state."], [252, 3, 1, 0, "seed", "Seed the bootstrap's random number generator"], [253, 3, 1, 0, "subset", "Sets a list of active models to run the SPA on.  Primarily for internal use."]], "arch.bootstrap.SPA.better_models": [[247, 2, 2, 0, "pvalue", "P-value in (0,1) to use when computing superior models"], [247, 2, 2, 0, "pvalue_type", "String in 'lower', 'consistent', or 'upper' indicating which critical value to use."]], "arch.bootstrap.SPA.critical_values": [[249, 2, 2, 0, "pvalue", "P-value in (0,1) to use when computing the critical values."]], "arch.bootstrap.SPA.seed": [[252, 2, 2, 0, "value", "Integer to use as the seed"]], "arch.bootstrap.SPA.subset": [[253, 2, 2, 0, "selector", "Boolean array indicating which columns to use when computing the p-values.  This is primarily for use by StepM."]], "arch.bootstrap.StationaryBootstrap.__init__": [[9, 2, 2, 0, "args", "Positional arguments to bootstrap"], [9, 2, 2, 0, "block_size", "Average size of block to use"], [9, 2, 2, 0, "kwargs", "Keyword arguments to bootstrap"], [9, 2, 2, 0, "random_state", "RandomState to use to ensure reproducable results. Cannot be used with seed"], [9, 2, 2, 0, "seed", "Seed to use to ensure reproducable results. If an int, passes the value to value to np.random.default_rng. If None, a fresh Generator is constructed with system-provided entropy."]], "arch.bootstrap.StationaryBootstrap": [[71, 3, 1, 0, "apply", "Applies a function to bootstrap replicated data"], [72, 3, 1, 0, "bootstrap", "Iterator for use when bootstrapping"], [73, 3, 1, 0, "clone", "Clones the bootstrap using different data with a fresh prng."], [74, 3, 1, 0, "conf_int", "Function the computes parameter values.  See Notes for requirements"], [75, 3, 1, 0, "cov", "Compute parameter covariance using bootstrap"], [9, 4, 1, 0, "data", "Two-element tuple with the pos_data in the first position and kw_data in the second (pos_data, kw_data)"], [76, 5, 1, 0, "generator", "Set or get the instance PRNG"], [77, 3, 1, 0, "get_state", "Gets the state of the bootstrap's random number generator"], [78, 5, 1, 0, "index", "The current index of the bootstrap"], [9, 4, 1, 0, "kw_data", "Dictionary containing the keyword arguments"], [9, 4, 1, 0, "pos_data", "Tuple containing the positional arguments (in the order entered)"], [79, 5, 1, 0, "random_state", "Set or get the instance random state"], [80, 3, 1, 0, "reset", "Resets the bootstrap to either its initial state or the last seed."], [81, 3, 1, 0, "seed", "Reseeds the bootstrap's random number generator"], [82, 3, 1, 0, "set_state", "Sets the state of the bootstrap's random number generator"], [83, 5, 1, 0, "state", "Set or get the generator's state"], [84, 3, 1, 0, "update_indices", "Update indices for the next iteration of the bootstrap.  This must be overridden when creating new bootstraps."], [85, 3, 1, 0, "var", "Compute parameter variance using bootstrap"]], "arch.bootstrap.StationaryBootstrap.apply": [[71, 2, 2, 0, "extra_kwargs", "Extra keyword arguments to use when calling func.  Must not conflict with keyword arguments used to initialize bootstrap"], [71, 2, 2, 0, "func", "Function the computes parameter values.  See Notes for requirements"], [71, 2, 2, 0, "reps", "Number of bootstrap replications"]], "arch.bootstrap.StationaryBootstrap.bootstrap": [[72, 2, 2, 0, "reps", "Number of bootstrap replications"]], "arch.bootstrap.StationaryBootstrap.clone": [[73, 2, 2, 0, "args", "Positional arguments to bootstrap"], [73, 2, 2, 0, "kwargs", "Keyword arguments to bootstrap"], [73, 2, 2, 0, "seed", "The seed value to pass to the closed generator"]], "arch.bootstrap.StationaryBootstrap.conf_int": [[74, 2, 2, 0, "extra_kwargs", "Extra keyword arguments to use when calling func and std_err_func, when appropriate"], [74, 2, 2, 0, "func", "Function the computes parameter values.  See Notes for requirements"], [74, 2, 2, 0, "method", "One of 'basic', 'percentile', 'studentized', 'norm' (identical to 'var', 'cov'), 'bc' (identical to 'debiased', 'bias-corrected'), or 'bca'"], [74, 2, 2, 0, "reps", "Number of bootstrap replications"], [74, 2, 2, 0, "reuse", "Flag indicating whether to reuse previously computed bootstrap results.  This allows alternative methods to be compared without rerunning the bootstrap simulation.  Reuse is ignored if reps is not the same across multiple runs, func changes across calls, or method is 'studentized'."], [74, 2, 2, 0, "sampling", "Type of sampling to use: 'nonparametric', 'semi-parametric' (or 'semi') or 'parametric'.  The default is 'nonparametric'.  See notes about the changes to func required when using 'semi' or 'parametric'."], [74, 2, 2, 0, "size", "Coverage of confidence interval"], [74, 2, 2, 0, "std_err_func", "Function to use when standardizing estimated parameters when using the studentized bootstrap.  Providing an analytical function eliminates the need for a nested bootstrap"], [74, 2, 2, 0, "studentize_reps", "Number of bootstraps to use in the inner bootstrap when using the studentized bootstrap.  Ignored when std_err_func is provided"], [74, 2, 2, 0, "tail", "One of 'two', 'upper' or 'lower'."]], "arch.bootstrap.StationaryBootstrap.cov": [[75, 2, 2, 0, "extra_kwargs", "Dictionary of extra keyword arguments to pass to func"], [75, 2, 2, 0, "func", "Callable function that returns the statistic of interest as a 1-d array"], [75, 2, 2, 0, "recenter", "Whether to center the bootstrap variance estimator on the average of the bootstrap samples (True) or to center on the original sample estimate (False).  Default is True."], [75, 2, 2, 0, "reps", "Number of bootstrap replications"]], "arch.bootstrap.StationaryBootstrap.reset": [[80, 2, 2, 0, "use_seed", "Flag indicating whether to use the last seed if provided.  If False or if no seed has been set, the bootstrap will be reset to the initial state.  Default is True"]], "arch.bootstrap.StationaryBootstrap.seed": [[81, 2, 2, 0, "value", "Value to use as the seed."]], "arch.bootstrap.StationaryBootstrap.set_state": [[82, 2, 2, 0, "state", "Dictionary or tuple containing the state."]], "arch.bootstrap.StationaryBootstrap.var": [[85, 2, 2, 0, "extra_kwargs", "Dictionary of extra keyword arguments to pass to func"], [85, 2, 2, 0, "func", "Callable function that returns the statistic of interest as a 1-d array"], [85, 2, 2, 0, "recenter", "Whether to center the bootstrap variance estimator on the average of the bootstrap samples (True) or to center on the original sample estimate (False).  Default is True."], [85, 2, 2, 0, "reps", "Number of bootstrap replications"]], "arch.bootstrap.StepM.__init__": [[240, 2, 2, 0, "benchmark", "T element array of benchmark model losses"], [240, 2, 2, 0, "block_size", "Length of window to use in the bootstrap.  If not provided, sqrt(T) is used.  In general, this should be provided and chosen to be appropriate for the data."], [240, 2, 2, 0, "bootstrap", "Bootstrap to use.  Options are 'stationary' or 'sb': Stationary bootstrap (Default) 'circular' or 'cbb': Circular block bootstrap 'moving block' or 'mbb': Moving block bootstrap"], [240, 2, 2, 0, "models", "T by k element array of alternative model losses"], [240, 2, 2, 0, "nested", "Flag indicating to use a nested bootstrap to compute variances for studentization.  Default is False.  Note that this can be slow since the procedure requires k extra bootstraps."], [240, 2, 2, 0, "reps", "Number of bootstrap replications to uses.  Default is 1000."], [240, 2, 2, 0, "seed", "Seed value to use when creating the bootstrap used in the comparison. If an integer or None, the NumPy default_rng is used with the seed value.  If a Generator or a RandomState, the argument is used."], [240, 2, 2, 0, "size", "Value in (0,1) to use as the test size when implementing the comparison. Default value is 0.05."], [240, 2, 2, 0, "studentize", "Flag indicating to studentize loss differentials. Default is True"]], "arch.bootstrap.StepM": [[254, 3, 1, 0, "compute", "Compute the set of superior models."], [255, 3, 1, 0, "reset", "Reset the bootstrap to it's initial state."], [256, 3, 1, 0, "seed", "Seed the bootstrap's random number generator"], [257, 5, 1, 0, "superior_models", "List of the indices or column names of the superior models"]], "arch.bootstrap.StepM.seed": [[256, 2, 2, 0, "value", "Integer to use as the seed"]], "arch.bootstrap.optimal_block_length": [[10, 2, 2, 0, "x", "A one-dimensional or two-dimensional array-like.  Operates columns by column if 2-dimensional."]], "arch.covariance": [[98, 0, 0, 1, "kernel", ""]], "arch.covariance.kernel": [[99, 1, 1, 0, "Andrews", "Alternative name of the QuadraticSpectral covariance estimator."], [100, 1, 1, 0, "Bartlett", "Bartlett's (Newey-West) kernel covariance estimation."], [101, 1, 1, 0, "CovarianceEstimate", "Covariance estimate using a long-run covariance estimator"], [102, 1, 1, 0, "Gallant", "Alternative name for Parzen covariance estimator."], [103, 1, 1, 0, "NeweyWest", "Alternative name for Bartlett covariance estimator."], [104, 1, 1, 0, "Parzen", "Parzen's kernel covariance estimation."], [105, 1, 1, 0, "ParzenCauchy", "Parzen's Cauchy kernel covariance estimation."], [106, 1, 1, 0, "ParzenGeometric", "Parzen's Geometric kernel covariance estimation."], [107, 1, 1, 0, "ParzenRiesz", "Parzen-Reisz kernel covariance estimation."], [108, 1, 1, 0, "QuadraticSpectral", "Quadratic-Spectral (Andrews') kernel covariance estimation."], [109, 1, 1, 0, "TukeyHamming", "Tukey-Hamming kernel covariance estimation."], [110, 1, 1, 0, "TukeyHanning", "Tukey-Hanning kernel covariance estimation."], [111, 1, 1, 0, "TukeyParzen", "Tukey-Parzen kernel covariance estimation."]], "arch.covariance.kernel.Andrews": [[112, 5, 1, 0, "bandwidth", "The bandwidth used by the covariance estimator."], [113, 5, 1, 0, "bandwidth_scale", "The power used in optimal bandwidth calculation."], [114, 5, 1, 0, "centered", "Flag indicating whether the data are centered (demeaned)."], [115, 5, 1, 0, "cov", "The estimated covariances."], [116, 5, 1, 0, "force_int", "Flag indicating whether the bandwidth is restricted to be an integer."], [117, 5, 1, 0, "kernel_const", "The constant used in optimal bandwidth calculation."], [118, 5, 1, 0, "kernel_weights", "Weights used in covariance calculation."], [119, 5, 1, 0, "name", "The covariance estimator's name."], [120, 5, 1, 0, "opt_bandwidth", "Estimate optimal bandwidth."], [121, 5, 1, 0, "rate", "The optimal rate used in bandwidth selection."]], "arch.covariance.kernel.Bartlett.__init__": [[100, 2, 2, 0, "bandwidth", "The kernel's bandwidth.  If None, optimal bandwidth is estimated."], [100, 2, 2, 0, "center", "A flag indicating whether x should be demeaned before estimating the covariance."], [100, 2, 2, 0, "df_adjust", "Degrees of freedom to remove when adjusting the covariance. Uses the number of observations in x minus df_adjust when dividing inner-products."], [100, 2, 2, 0, "force_int", "Force bandwidth to be an integer."], [100, 2, 2, 0, "weights", "An array of weights used to combine when estimating optimal bandwidth. If not provided, a vector of 1s is used. Must have nvar elements."], [100, 2, 2, 0, "x", "The data to use in covariance estimation."]], "arch.covariance.kernel.Bartlett": [[122, 5, 1, 0, "bandwidth", "The bandwidth used by the covariance estimator."], [123, 5, 1, 0, "bandwidth_scale", "The power used in optimal bandwidth calculation."], [124, 5, 1, 0, "centered", "Flag indicating whether the data are centered (demeaned)."], [125, 5, 1, 0, "cov", "The estimated covariances."], [126, 5, 1, 0, "force_int", "Flag indicating whether the bandwidth is restricted to be an integer."], [127, 5, 1, 0, "kernel_const", "The constant used in optimal bandwidth calculation."], [128, 5, 1, 0, "kernel_weights", "Weights used in covariance calculation."], [129, 5, 1, 0, "name", "The covariance estimator's name."], [130, 5, 1, 0, "opt_bandwidth", "Estimate optimal bandwidth."], [131, 5, 1, 0, "rate", "The optimal rate used in bandwidth selection."]], "arch.covariance.kernel.CovarianceEstimate.__init__": [[101, 2, 2, 0, "columns", "Column labels to use if covariance estimates are returned as DataFrames."], [101, 2, 2, 0, "long_run", "The long-run covariance estimate. If not provided, computed from short_run and one_sided_strict."], [101, 2, 2, 0, "one_sided_strict", "The one-sided-strict covariance estimate. If not provided, computed from short_run and one_sided_strict."], [101, 2, 2, 0, "short_run", "The short-run covariance estimate."]], "arch.covariance.kernel.CovarianceEstimate": [[132, 5, 1, 0, "long_run", "The long-run covariance estimate."], [133, 5, 1, 0, "one_sided", "The one-sided covariance estimate."], [134, 5, 1, 0, "one_sided_strict", "The one-sided strict covariance estimate."], [135, 5, 1, 0, "short_run", "The short-run covariance estimate."]], "arch.covariance.kernel.Gallant": [[136, 5, 1, 0, "bandwidth", "The bandwidth used by the covariance estimator."], [137, 5, 1, 0, "bandwidth_scale", "The power used in optimal bandwidth calculation."], [138, 5, 1, 0, "centered", "Flag indicating whether the data are centered (demeaned)."], [139, 5, 1, 0, "cov", "The estimated covariances."], [140, 5, 1, 0, "force_int", "Flag indicating whether the bandwidth is restricted to be an integer."], [141, 5, 1, 0, "kernel_const", "The constant used in optimal bandwidth calculation."], [142, 5, 1, 0, "kernel_weights", "Weights used in covariance calculation."], [143, 5, 1, 0, "name", "The covariance estimator's name."], [144, 5, 1, 0, "opt_bandwidth", "Estimate optimal bandwidth."], [145, 5, 1, 0, "rate", "The optimal rate used in bandwidth selection."]], "arch.covariance.kernel.NeweyWest": [[146, 5, 1, 0, "bandwidth", "The bandwidth used by the covariance estimator."], [147, 5, 1, 0, "bandwidth_scale", "The power used in optimal bandwidth calculation."], [148, 5, 1, 0, "centered", "Flag indicating whether the data are centered (demeaned)."], [149, 5, 1, 0, "cov", "The estimated covariances."], [150, 5, 1, 0, "force_int", "Flag indicating whether the bandwidth is restricted to be an integer."], [151, 5, 1, 0, "kernel_const", "The constant used in optimal bandwidth calculation."], [152, 5, 1, 0, "kernel_weights", "Weights used in covariance calculation."], [153, 5, 1, 0, "name", "The covariance estimator's name."], [154, 5, 1, 0, "opt_bandwidth", "Estimate optimal bandwidth."], [155, 5, 1, 0, "rate", "The optimal rate used in bandwidth selection."]], "arch.covariance.kernel.Parzen.__init__": [[104, 2, 2, 0, "bandwidth", "The kernel's bandwidth.  If None, optimal bandwidth is estimated."], [104, 2, 2, 0, "center", "A flag indicating whether x should be demeaned before estimating the covariance."], [104, 2, 2, 0, "df_adjust", "Degrees of freedom to remove when adjusting the covariance. Uses the number of observations in x minus df_adjust when dividing inner-products."], [104, 2, 2, 0, "force_int", "Force bandwidth to be an integer."], [104, 2, 2, 0, "weights", "An array of weights used to combine when estimating optimal bandwidth. If not provided, a vector of 1s is used. Must have nvar elements."], [104, 2, 2, 0, "x", "The data to use in covariance estimation."]], "arch.covariance.kernel.Parzen": [[156, 5, 1, 0, "bandwidth", "The bandwidth used by the covariance estimator."], [157, 5, 1, 0, "bandwidth_scale", "The power used in optimal bandwidth calculation."], [158, 5, 1, 0, "centered", "Flag indicating whether the data are centered (demeaned)."], [159, 5, 1, 0, "cov", "The estimated covariances."], [160, 5, 1, 0, "force_int", "Flag indicating whether the bandwidth is restricted to be an integer."], [161, 5, 1, 0, "kernel_const", "The constant used in optimal bandwidth calculation."], [162, 5, 1, 0, "kernel_weights", "Weights used in covariance calculation."], [163, 5, 1, 0, "name", "The covariance estimator's name."], [164, 5, 1, 0, "opt_bandwidth", "Estimate optimal bandwidth."], [165, 5, 1, 0, "rate", "The optimal rate used in bandwidth selection."]], "arch.covariance.kernel.ParzenCauchy.__init__": [[105, 2, 2, 0, "bandwidth", "The kernel's bandwidth.  If None, optimal bandwidth is estimated."], [105, 2, 2, 0, "center", "A flag indicating whether x should be demeaned before estimating the covariance."], [105, 2, 2, 0, "df_adjust", "Degrees of freedom to remove when adjusting the covariance. Uses the number of observations in x minus df_adjust when dividing inner-products."], [105, 2, 2, 0, "force_int", "Force bandwidth to be an integer."], [105, 2, 2, 0, "weights", "An array of weights used to combine when estimating optimal bandwidth. If not provided, a vector of 1s is used. Must have nvar elements."], [105, 2, 2, 0, "x", "The data to use in covariance estimation."]], "arch.covariance.kernel.ParzenCauchy": [[166, 5, 1, 0, "bandwidth", "The bandwidth used by the covariance estimator."], [167, 5, 1, 0, "bandwidth_scale", "The power used in optimal bandwidth calculation."], [168, 5, 1, 0, "centered", "Flag indicating whether the data are centered (demeaned)."], [169, 5, 1, 0, "cov", "The estimated covariances."], [170, 5, 1, 0, "force_int", "Flag indicating whether the bandwidth is restricted to be an integer."], [171, 5, 1, 0, "kernel_const", "The constant used in optimal bandwidth calculation."], [172, 5, 1, 0, "kernel_weights", "Weights used in covariance calculation."], [173, 5, 1, 0, "name", "The covariance estimator's name."], [174, 5, 1, 0, "opt_bandwidth", "Estimate optimal bandwidth."], [175, 5, 1, 0, "rate", "The optimal rate used in bandwidth selection."]], "arch.covariance.kernel.ParzenGeometric.__init__": [[106, 2, 2, 0, "bandwidth", "The kernel's bandwidth.  If None, optimal bandwidth is estimated."], [106, 2, 2, 0, "center", "A flag indicating whether x should be demeaned before estimating the covariance."], [106, 2, 2, 0, "df_adjust", "Degrees of freedom to remove when adjusting the covariance. Uses the number of observations in x minus df_adjust when dividing inner-products."], [106, 2, 2, 0, "force_int", "Force bandwidth to be an integer."], [106, 2, 2, 0, "weights", "An array of weights used to combine when estimating optimal bandwidth. If not provided, a vector of 1s is used. Must have nvar elements."], [106, 2, 2, 0, "x", "The data to use in covariance estimation."]], "arch.covariance.kernel.ParzenGeometric": [[176, 5, 1, 0, "bandwidth", "The bandwidth used by the covariance estimator."], [177, 5, 1, 0, "bandwidth_scale", "The power used in optimal bandwidth calculation."], [178, 5, 1, 0, "centered", "Flag indicating whether the data are centered (demeaned)."], [179, 5, 1, 0, "cov", "The estimated covariances."], [180, 5, 1, 0, "force_int", "Flag indicating whether the bandwidth is restricted to be an integer."], [181, 5, 1, 0, "kernel_const", "The constant used in optimal bandwidth calculation."], [182, 5, 1, 0, "kernel_weights", "Weights used in covariance calculation."], [183, 5, 1, 0, "name", "The covariance estimator's name."], [184, 5, 1, 0, "opt_bandwidth", "Estimate optimal bandwidth."], [185, 5, 1, 0, "rate", "The optimal rate used in bandwidth selection."]], "arch.covariance.kernel.ParzenRiesz.__init__": [[107, 2, 2, 0, "bandwidth", "The kernel's bandwidth.  If None, optimal bandwidth is estimated."], [107, 2, 2, 0, "center", "A flag indicating whether x should be demeaned before estimating the covariance."], [107, 2, 2, 0, "df_adjust", "Degrees of freedom to remove when adjusting the covariance. Uses the number of observations in x minus df_adjust when dividing inner-products."], [107, 2, 2, 0, "force_int", "Force bandwidth to be an integer."], [107, 2, 2, 0, "weights", "An array of weights used to combine when estimating optimal bandwidth. If not provided, a vector of 1s is used. Must have nvar elements."], [107, 2, 2, 0, "x", "The data to use in covariance estimation."]], "arch.covariance.kernel.ParzenRiesz": [[186, 5, 1, 0, "bandwidth", "The bandwidth used by the covariance estimator."], [187, 5, 1, 0, "bandwidth_scale", "The power used in optimal bandwidth calculation."], [188, 5, 1, 0, "centered", "Flag indicating whether the data are centered (demeaned)."], [189, 5, 1, 0, "cov", "The estimated covariances."], [190, 5, 1, 0, "force_int", "Flag indicating whether the bandwidth is restricted to be an integer."], [191, 5, 1, 0, "kernel_const", "The constant used in optimal bandwidth calculation."], [192, 5, 1, 0, "kernel_weights", "Weights used in covariance calculation."], [193, 5, 1, 0, "name", "The covariance estimator's name."], [194, 5, 1, 0, "opt_bandwidth", "Estimate optimal bandwidth."], [195, 5, 1, 0, "rate", "The optimal rate used in bandwidth selection."]], "arch.covariance.kernel.QuadraticSpectral.__init__": [[108, 2, 2, 0, "bandwidth", "The kernel's bandwidth.  If None, optimal bandwidth is estimated."], [108, 2, 2, 0, "center", "A flag indicating whether x should be demeaned before estimating the covariance."], [108, 2, 2, 0, "df_adjust", "Degrees of freedom to remove when adjusting the covariance. Uses the number of observations in x minus df_adjust when dividing inner-products."], [108, 2, 2, 0, "force_int", "Force bandwidth to be an integer."], [108, 2, 2, 0, "weights", "An array of weights used to combine when estimating optimal bandwidth. If not provided, a vector of 1s is used. Must have nvar elements."], [108, 2, 2, 0, "x", "The data to use in covariance estimation."]], "arch.covariance.kernel.QuadraticSpectral": [[196, 5, 1, 0, "bandwidth", "The bandwidth used by the covariance estimator."], [197, 5, 1, 0, "bandwidth_scale", "The power used in optimal bandwidth calculation."], [198, 5, 1, 0, "centered", "Flag indicating whether the data are centered (demeaned)."], [199, 5, 1, 0, "cov", "The estimated covariances."], [200, 5, 1, 0, "force_int", "Flag indicating whether the bandwidth is restricted to be an integer."], [201, 5, 1, 0, "kernel_const", "The constant used in optimal bandwidth calculation."], [202, 5, 1, 0, "kernel_weights", "Weights used in covariance calculation."], [203, 5, 1, 0, "name", "The covariance estimator's name."], [204, 5, 1, 0, "opt_bandwidth", "Estimate optimal bandwidth."], [205, 5, 1, 0, "rate", "The optimal rate used in bandwidth selection."]], "arch.covariance.kernel.TukeyHamming.__init__": [[109, 2, 2, 0, "bandwidth", "The kernel's bandwidth.  If None, optimal bandwidth is estimated."], [109, 2, 2, 0, "center", "A flag indicating whether x should be demeaned before estimating the covariance."], [109, 2, 2, 0, "df_adjust", "Degrees of freedom to remove when adjusting the covariance. Uses the number of observations in x minus df_adjust when dividing inner-products."], [109, 2, 2, 0, "force_int", "Force bandwidth to be an integer."], [109, 2, 2, 0, "weights", "An array of weights used to combine when estimating optimal bandwidth. If not provided, a vector of 1s is used. Must have nvar elements."], [109, 2, 2, 0, "x", "The data to use in covariance estimation."]], "arch.covariance.kernel.TukeyHamming": [[206, 5, 1, 0, "bandwidth", "The bandwidth used by the covariance estimator."], [207, 5, 1, 0, "bandwidth_scale", "The power used in optimal bandwidth calculation."], [208, 5, 1, 0, "centered", "Flag indicating whether the data are centered (demeaned)."], [209, 5, 1, 0, "cov", "The estimated covariances."], [210, 5, 1, 0, "force_int", "Flag indicating whether the bandwidth is restricted to be an integer."], [211, 5, 1, 0, "kernel_const", "The constant used in optimal bandwidth calculation."], [212, 5, 1, 0, "kernel_weights", "Weights used in covariance calculation."], [213, 5, 1, 0, "name", "The covariance estimator's name."], [214, 5, 1, 0, "opt_bandwidth", "Estimate optimal bandwidth."], [215, 5, 1, 0, "rate", "The optimal rate used in bandwidth selection."]], "arch.covariance.kernel.TukeyHanning.__init__": [[110, 2, 2, 0, "bandwidth", "The kernel's bandwidth.  If None, optimal bandwidth is estimated."], [110, 2, 2, 0, "center", "A flag indicating whether x should be demeaned before estimating the covariance."], [110, 2, 2, 0, "df_adjust", "Degrees of freedom to remove when adjusting the covariance. Uses the number of observations in x minus df_adjust when dividing inner-products."], [110, 2, 2, 0, "force_int", "Force bandwidth to be an integer."], [110, 2, 2, 0, "weights", "An array of weights used to combine when estimating optimal bandwidth. If not provided, a vector of 1s is used. Must have nvar elements."], [110, 2, 2, 0, "x", "The data to use in covariance estimation."]], "arch.covariance.kernel.TukeyHanning": [[216, 5, 1, 0, "bandwidth", "The bandwidth used by the covariance estimator."], [217, 5, 1, 0, "bandwidth_scale", "The power used in optimal bandwidth calculation."], [218, 5, 1, 0, "centered", "Flag indicating whether the data are centered (demeaned)."], [219, 5, 1, 0, "cov", "The estimated covariances."], [220, 5, 1, 0, "force_int", "Flag indicating whether the bandwidth is restricted to be an integer."], [221, 5, 1, 0, "kernel_const", "The constant used in optimal bandwidth calculation."], [222, 5, 1, 0, "kernel_weights", "Weights used in covariance calculation."], [223, 5, 1, 0, "name", "The covariance estimator's name."], [224, 5, 1, 0, "opt_bandwidth", "Estimate optimal bandwidth."], [225, 5, 1, 0, "rate", "The optimal rate used in bandwidth selection."]], "arch.covariance.kernel.TukeyParzen.__init__": [[111, 2, 2, 0, "bandwidth", "The kernel's bandwidth.  If None, optimal bandwidth is estimated."], [111, 2, 2, 0, "center", "A flag indicating whether x should be demeaned before estimating the covariance."], [111, 2, 2, 0, "df_adjust", "Degrees of freedom to remove when adjusting the covariance. Uses the number of observations in x minus df_adjust when dividing inner-products."], [111, 2, 2, 0, "force_int", "Force bandwidth to be an integer."], [111, 2, 2, 0, "weights", "An array of weights used to combine when estimating optimal bandwidth. If not provided, a vector of 1s is used. Must have nvar elements."], [111, 2, 2, 0, "x", "The data to use in covariance estimation."]], "arch.covariance.kernel.TukeyParzen": [[226, 5, 1, 0, "bandwidth", "The bandwidth used by the covariance estimator."], [227, 5, 1, 0, "bandwidth_scale", "The power used in optimal bandwidth calculation."], [228, 5, 1, 0, "centered", "Flag indicating whether the data are centered (demeaned)."], [229, 5, 1, 0, "cov", "The estimated covariances."], [230, 5, 1, 0, "force_int", "Flag indicating whether the bandwidth is restricted to be an integer."], [231, 5, 1, 0, "kernel_const", "The constant used in optimal bandwidth calculation."], [232, 5, 1, 0, "kernel_weights", "Weights used in covariance calculation."], [233, 5, 1, 0, "name", "The covariance estimator's name."], [234, 5, 1, 0, "opt_bandwidth", "Estimate optimal bandwidth."], [235, 5, 1, 0, "rate", "The optimal rate used in bandwidth selection."]], "arch.unitroot": [[262, 1, 1, 0, "ADF", "Augmented Dickey-Fuller unit root test"], [263, 1, 1, 0, "DFGLS", "Elliott, Rothenberg and Stock's (1) GLS detrended Dickey-Fuller"], [264, 1, 1, 0, "KPSS", "Kwiatkowski, Phillips, Schmidt and Shin (KPSS) stationarity test"], [265, 1, 1, 0, "PhillipsPerron", "Phillips-Perron unit root test"], [266, 1, 1, 0, "VarianceRatio", "Variance Ratio test of a random walk."], [267, 1, 1, 0, "ZivotAndrews", "Zivot-Andrews structural-break unit-root test"], [268, 6, 1, 0, "auto_bandwidth", "Automatic bandwidth selection of Andrews (1991) and Newey & West (1994)."], [261, 0, 0, 1, "cointegration", ""]], "arch.unitroot.ADF.__init__": [[262, 2, 2, 0, "lags", "The number of lags to use in the ADF regression.  If omitted or None, method is used to automatically select the lag length with no more than max_lags are included."], [262, 2, 2, 0, "low_memory", "Flag indicating whether to use a low memory implementation of the lag selection algorithm. The low memory algorithm is slower than the standard algorithm but will use 2-4% of the memory required for the standard algorithm. This options allows automatic lag selection to be used in very long time series. If None, use automatic selection of algorithm."], [262, 2, 2, 0, "max_lags", "The maximum number of lags to use when selecting lag length"], [262, 2, 2, 0, "method", "The method to use when selecting the lag length"], [262, 2, 2, 0, "trend", "The trend component to include in the test"], [262, 2, 2, 0, "y", "The data to test for a unit root"]], "arch.unitroot.ADF": [[278, 5, 1, 0, "alternative_hypothesis", "The alternative hypothesis"], [279, 5, 1, 0, "critical_values", "Dictionary containing critical values specific to the test, number of observations and included deterministic trend terms."], [280, 5, 1, 0, "lags", "Sets or gets the number of lags used in the model. When bootstrap use DF-type regressions, lags is the number of lags in the regression model.  When bootstrap use long-run variance estimators, lags is the number of lags used in the long-run variance estimator."], [281, 5, 1, 0, "max_lags", "Sets or gets the maximum lags used when automatically selecting lag length"], [282, 5, 1, 0, "nobs", "The number of observations used when computing the test statistic. Accounts for loss of data due to lags for regression-based bootstrap."], [283, 5, 1, 0, "null_hypothesis", "The null hypothesis"], [284, 5, 1, 0, "pvalue", "Returns the p-value for the test statistic"], [285, 5, 1, 0, "regression", "Returns the OLS regression results from the ADF model estimated"], [286, 5, 1, 0, "stat", "The test statistic for a unit root"], [287, 3, 1, 0, "summary", "Summary of test, containing statistic, p-value and critical values"], [288, 5, 1, 0, "trend", "Sets or gets the deterministic trend term used in the test. See valid_trends for a list of supported trends"], [289, 5, 1, 0, "valid_trends", "List of valid trend terms."], [290, 5, 1, 0, "y", "Returns the data used in the test statistic"]], "arch.unitroot.DFGLS.__init__": [[263, 2, 2, 0, "lags", "The number of lags to use in the ADF regression.  If omitted or None, method is used to automatically select the lag length with no more than max_lags are included."], [263, 2, 2, 0, "max_lags", "The maximum number of lags to use when selecting lag length. When using automatic lag length selection, the lag is selected using OLS detrending rather than GLS detrending (2)."], [263, 2, 2, 0, "method", "The method to use when selecting the lag length"], [263, 2, 2, 0, "trend", "The trend component to include in the test"], [263, 2, 2, 0, "y", "The data to test for a unit root"]], "arch.unitroot.DFGLS": [[291, 5, 1, 0, "alternative_hypothesis", "The alternative hypothesis"], [292, 5, 1, 0, "critical_values", "Dictionary containing critical values specific to the test, number of observations and included deterministic trend terms."], [293, 5, 1, 0, "lags", "Sets or gets the number of lags used in the model. When bootstrap use DF-type regressions, lags is the number of lags in the regression model.  When bootstrap use long-run variance estimators, lags is the number of lags used in the long-run variance estimator."], [294, 5, 1, 0, "max_lags", "Sets or gets the maximum lags used when automatically selecting lag length"], [295, 5, 1, 0, "nobs", "The number of observations used when computing the test statistic. Accounts for loss of data due to lags for regression-based bootstrap."], [296, 5, 1, 0, "null_hypothesis", "The null hypothesis"], [297, 5, 1, 0, "pvalue", "Returns the p-value for the test statistic"], [298, 5, 1, 0, "regression", "Returns the OLS regression results from the ADF model estimated"], [299, 5, 1, 0, "stat", "The test statistic for a unit root"], [300, 3, 1, 0, "summary", "Summary of test, containing statistic, p-value and critical values"], [301, 5, 1, 0, "trend", "Sets or gets the deterministic trend term used in the test. See valid_trends for a list of supported trends"], [302, 5, 1, 0, "valid_trends", "List of valid trend terms."], [303, 5, 1, 0, "y", "Returns the data used in the test statistic"]], "arch.unitroot.KPSS.__init__": [[264, 2, 2, 0, "lags", "The number of lags to use in the Newey-West estimator of the long-run covariance.  If omitted or None, the number of lags is calculated with the data-dependent method of Hobijn et al. (1998). See also Andrews (1991), Newey & West (1994), and Schwert (1989). Set lags=-1 to use the old method that only depends on the sample size, 12 * (nobs/100) ** (1/4)."], [264, 2, 2, 0, "trend", "\"c\" - Include a constant (Default) \"ct\" - Include a constant and linear time trend"], [264, 2, 2, 0, "y", "The data to test for stationarity"]], "arch.unitroot.KPSS": [[304, 5, 1, 0, "alternative_hypothesis", "The alternative hypothesis"], [305, 5, 1, 0, "critical_values", "Dictionary containing critical values specific to the test, number of observations and included deterministic trend terms."], [306, 5, 1, 0, "lags", "Sets or gets the number of lags used in the model. When bootstrap use DF-type regressions, lags is the number of lags in the regression model.  When bootstrap use long-run variance estimators, lags is the number of lags used in the long-run variance estimator."], [307, 5, 1, 0, "nobs", "The number of observations used when computing the test statistic. Accounts for loss of data due to lags for regression-based bootstrap."], [308, 5, 1, 0, "null_hypothesis", "The null hypothesis"], [309, 5, 1, 0, "pvalue", "Returns the p-value for the test statistic"], [310, 5, 1, 0, "stat", "The test statistic for a unit root"], [311, 3, 1, 0, "summary", "Summary of test, containing statistic, p-value and critical values"], [312, 5, 1, 0, "trend", "Sets or gets the deterministic trend term used in the test. See valid_trends for a list of supported trends"], [313, 5, 1, 0, "valid_trends", "List of valid trend terms."], [314, 5, 1, 0, "y", "Returns the data used in the test statistic"]], "arch.unitroot.PhillipsPerron.__init__": [[265, 2, 2, 0, "lags", "The number of lags to use in the Newey-West estimator of the long-run covariance.  If omitted or None, the lag length is set automatically to 12 * (nobs/100) ** (1/4)"], [265, 2, 2, 0, "test_type", "The test to use when computing the test statistic. \"tau\" is based on the t-stat and \"rho\" uses a test based on nobs times the re-centered regression coefficient"], [265, 2, 2, 0, "trend", "The trend component to include in the test"], [265, 2, 2, 0, "y", "The data to test for a unit root"]], "arch.unitroot.PhillipsPerron": [[315, 5, 1, 0, "alternative_hypothesis", "The alternative hypothesis"], [316, 5, 1, 0, "critical_values", "Dictionary containing critical values specific to the test, number of observations and included deterministic trend terms."], [317, 5, 1, 0, "lags", "Sets or gets the number of lags used in the model. When bootstrap use DF-type regressions, lags is the number of lags in the regression model.  When bootstrap use long-run variance estimators, lags is the number of lags used in the long-run variance estimator."], [318, 5, 1, 0, "nobs", "The number of observations used when computing the test statistic. Accounts for loss of data due to lags for regression-based bootstrap."], [319, 5, 1, 0, "null_hypothesis", "The null hypothesis"], [320, 5, 1, 0, "pvalue", "Returns the p-value for the test statistic"], [321, 5, 1, 0, "regression", "Returns OLS regression results for the specification used in the test"], [322, 5, 1, 0, "stat", "The test statistic for a unit root"], [323, 3, 1, 0, "summary", "Summary of test, containing statistic, p-value and critical values"], [324, 5, 1, 0, "test_type", "Gets or sets the test type returned by stat. Valid values are \"tau\" or \"rho\""], [325, 5, 1, 0, "trend", "Sets or gets the deterministic trend term used in the test. See valid_trends for a list of supported trends"], [326, 5, 1, 0, "valid_trends", "List of valid trend terms."], [327, 5, 1, 0, "y", "Returns the data used in the test statistic"]], "arch.unitroot.VarianceRatio.__init__": [[266, 2, 2, 0, "debiased", "Indicates whether to use a debiased version of the test. Default is True. Only applicable if overlap is True."], [266, 2, 2, 0, "lags", "The number of periods to used in the multi-period variance, which is the numerator of the test statistic.  Must be at least 2"], [266, 2, 2, 0, "overlap", "Indicates whether to use all overlapping blocks.  Default is True.  If False, the number of observations in y minus 1 must be an exact multiple of lags.  If this condition is not satisfied, some values at the end of y will be discarded."], [266, 2, 2, 0, "robust", "Indicates whether to use heteroskedasticity robust inference. Default is True."], [266, 2, 2, 0, "trend", "\"c\" allows for a non-zero drift in the random walk, while \"n\" requires that the increments to y are mean 0"], [266, 2, 2, 0, "y", "The data to test for a random walk"]], "arch.unitroot.VarianceRatio": [[328, 5, 1, 0, "alternative_hypothesis", "The alternative hypothesis"], [329, 5, 1, 0, "critical_values", "Dictionary containing critical values specific to the test, number of observations and included deterministic trend terms."], [330, 5, 1, 0, "debiased", "Sets of gets the indicator to use debiased variances in the ratio"], [331, 5, 1, 0, "lags", "Sets or gets the number of lags used in the model. When bootstrap use DF-type regressions, lags is the number of lags in the regression model.  When bootstrap use long-run variance estimators, lags is the number of lags used in the long-run variance estimator."], [332, 5, 1, 0, "nobs", "The number of observations used when computing the test statistic. Accounts for loss of data due to lags for regression-based bootstrap."], [333, 5, 1, 0, "null_hypothesis", "The null hypothesis"], [334, 5, 1, 0, "overlap", "Sets of gets the indicator to use overlapping returns in the long-period variance estimator"], [335, 5, 1, 0, "pvalue", "Returns the p-value for the test statistic"], [336, 5, 1, 0, "robust", "Sets of gets the indicator to use a heteroskedasticity robust variance estimator"], [337, 5, 1, 0, "stat", "The test statistic for a unit root"], [338, 3, 1, 0, "summary", "Summary of test, containing statistic, p-value and critical values"], [339, 5, 1, 0, "trend", "Sets or gets the deterministic trend term used in the test. See valid_trends for a list of supported trends"], [340, 5, 1, 0, "valid_trends", "List of valid trend terms."], [341, 5, 1, 0, "vr", "The ratio of the long block lags-period variance to the 1-period variance"], [342, 5, 1, 0, "y", "Returns the data used in the test statistic"]], "arch.unitroot.ZivotAndrews.__init__": [[267, 2, 2, 0, "lags", "The number of lags to use in the ADF regression.  If omitted or None, method is used to automatically select the lag length with no more than max_lags are included."], [267, 2, 2, 0, "max_lags", "The maximum number of lags to use when selecting lag length"], [267, 2, 2, 0, "method", "The method to use when selecting the lag length"], [267, 2, 2, 0, "trend", "The trend component to include in the test"], [267, 2, 2, 0, "trim", "percentage of series at begin/end to exclude from break-period calculation in range [0, 0.333] (default=0.15)"], [267, 2, 2, 0, "y", "data series"]], "arch.unitroot.ZivotAndrews": [[343, 5, 1, 0, "alternative_hypothesis", "The alternative hypothesis"], [344, 5, 1, 0, "critical_values", "Dictionary containing critical values specific to the test, number of observations and included deterministic trend terms."], [345, 5, 1, 0, "lags", "Sets or gets the number of lags used in the model. When bootstrap use DF-type regressions, lags is the number of lags in the regression model.  When bootstrap use long-run variance estimators, lags is the number of lags used in the long-run variance estimator."], [346, 5, 1, 0, "nobs", "The number of observations used when computing the test statistic. Accounts for loss of data due to lags for regression-based bootstrap."], [347, 5, 1, 0, "null_hypothesis", "The null hypothesis"], [348, 5, 1, 0, "pvalue", "Returns the p-value for the test statistic"], [349, 5, 1, 0, "stat", "The test statistic for a unit root"], [350, 3, 1, 0, "summary", "Summary of test, containing statistic, p-value and critical values"], [351, 5, 1, 0, "trend", "Sets or gets the deterministic trend term used in the test. See valid_trends for a list of supported trends"], [352, 5, 1, 0, "valid_trends", "List of valid trend terms."], [353, 5, 1, 0, "y", "Returns the data used in the test statistic"]], "arch.unitroot.auto_bandwidth": [[268, 2, 2, 0, "kernel", "The kernel function to use for selecting the bandwidth"], [268, 2, 2, 0, "y", "Data on which to apply the bandwidth selection"]], "arch.unitroot.cointegration": [[269, 1, 1, 0, "CanonicalCointegratingReg", "Canonical Cointegrating Regression cointegrating vector estimation."], [270, 1, 1, 0, "CointegrationAnalysisResults", "summary()"], [271, 1, 1, 0, "DynamicOLS", "Dynamic OLS (DOLS) cointegrating vector estimation"], [272, 1, 1, 0, "DynamicOLSResults", "Estimation results for Dynamic OLS models"], [273, 1, 1, 0, "EngleGrangerTestResults", "Results class for Engle-Granger cointegration tests."], [274, 1, 1, 0, "FullyModifiedOLS", "Fully Modified OLS cointegrating vector estimation."], [275, 1, 1, 0, "PhillipsOuliarisTestResults", "plot([axes, title])"], [276, 6, 1, 0, "engle_granger", "Test for cointegration within a set of time series."], [277, 6, 1, 0, "phillips_ouliaris", "Test for cointegration within a set of time series."]], "arch.unitroot.cointegration.CanonicalCointegratingReg.__init__": [[269, 2, 2, 0, "trend", "Trend to include in the cointegrating regression. Trends are:"], [269, 2, 2, 0, "x", "The right-hand-side variables in the cointegrating regression."], [269, 2, 2, 0, "x_trend", "Trends that affects affect the x-data but do not appear in the cointegrating regression. x_trend must be at least as large as trend, so that if trend is \"ct\", x_trend must be either \"ct\" or \"ctt\"."], [269, 2, 2, 0, "y", "The left-hand-side variable in the cointegrating regression."]], "arch.unitroot.cointegration.CanonicalCointegratingReg": [[354, 3, 1, 0, "fit", "Estimate the cointegrating vector."]], "arch.unitroot.cointegration.CanonicalCointegratingReg.fit": [[354, 2, 2, 0, "bandwidth", "The bandwidth to use. If not provided, the optimal bandwidth is estimated from the data. Setting the bandwidth to 0 and using \"unadjusted\" produces the classic OLS covariance estimator. Setting the bandwidth to 0 and using \"robust\" produces White's covariance estimator."], [354, 2, 2, 0, "df_adjust", "Whether the adjust the parameter covariance to account for the number of parameters estimated in the regression. If true, the parameter covariance estimator is multiplied by T/(T-k) where k is the number of regressors in the model."], [354, 2, 2, 0, "diff", "Use differenced data to estimate the residuals."], [354, 2, 2, 0, "force_int", "Whether the force the estimated optimal bandwidth to be an integer."], [354, 2, 2, 0, "kernel", "The string name of any of any known kernel-based long-run covariance estimators. Common choices are \"bartlett\" for the Bartlett kernel (Newey-West), \"parzen\" for the Parzen kernel and \"quadratic-spectral\" for the Quadratic Spectral kernel."]], "arch.unitroot.cointegration.CointegrationAnalysisResults": [[355, 5, 1, 0, "bandwidth", "The bandwidth used in the parameter covariance estimation"], [356, 5, 1, 0, "cov", "The estimated parameter covariance of the cointegrating vector"], [357, 5, 1, 0, "kernel", "The kernel used to estimate the covariance"], [358, 5, 1, 0, "long_run_variance", "Long-run variance estimate used in the parameter covariance estimator"], [359, 5, 1, 0, "params", "The estimated parameters of the cointegrating vector"], [360, 5, 1, 0, "pvalues", "P-value of the parameters in the cointegrating vector"], [361, 5, 1, 0, "resid", "The model residuals"], [362, 5, 1, 0, "residual_variance", "The variance of the regression residual."], [363, 5, 1, 0, "rsquared", "The model R\u00b2"], [364, 5, 1, 0, "rsquared_adj", "The degree-of-freedom adjusted R\u00b2"], [365, 5, 1, 0, "std_errors", "Standard errors  of the parameters in the cointegrating vector"], [366, 3, 1, 0, "summary", "Summary of the model, containing estimated parameters and std. errors"], [367, 5, 1, 0, "tvalues", "T-statistics of the parameters in the cointegrating vector"]], "arch.unitroot.cointegration.DynamicOLS.__init__": [[271, 2, 2, 0, "common", "Flag indicating that lags and leads should be restricted to the same value. When common is None, lags must equal leads and max_lag must equal max_lead."], [271, 2, 2, 0, "lags", "The number of lags to include in the model.  If None, the optimal number of lags is chosen using method."], [271, 2, 2, 0, "leads", "The number of leads to include in the model.  If None, the optimal number of leads is chosen using method."], [271, 2, 2, 0, "max_lag", "The maximum lag to consider. See Notes for value used when None."], [271, 2, 2, 0, "max_lead", "The maximum lead to consider. See Notes for value used when None."], [271, 2, 2, 0, "method", "The method used to select lag length when lags or leads is None."], [271, 2, 2, 0, "trend", "Trend to include in the cointegrating regression. Trends are:"], [271, 2, 2, 0, "x", "The right-hand-side variables in the cointegrating regression."], [271, 2, 2, 0, "y", "The left-hand-side variable in the cointegrating regression."]], "arch.unitroot.cointegration.DynamicOLS": [[368, 3, 1, 0, "fit", "Estimate the Dynamic OLS regression"]], "arch.unitroot.cointegration.DynamicOLS.fit": [[368, 2, 2, 0, "bandwidth", "The bandwidth to use. If not provided, the optimal bandwidth is estimated from the data. Setting the bandwidth to 0 and using \"unadjusted\" produces the classic OLS covariance estimator. Setting the bandwidth to 0 and using \"robust\" produces White's covariance estimator."], [368, 2, 2, 0, "cov_type", "Either \"unadjusted\" (or is equivalent \"homoskedastic\") or \"robust\" (or its equivalent \"kernel\")."], [368, 2, 2, 0, "df_adjust", "Whether the adjust the parameter covariance to account for the number of parameters estimated in the regression. If true, the parameter covariance estimator is multiplied by T/(T-k) where k is the number of regressors in the model."], [368, 2, 2, 0, "force_int", "Whether the force the estimated optimal bandwidth to be an integer."], [368, 2, 2, 0, "kernel", "The string name of any of any known kernel-based long-run covariance estimators. Common choices are \"bartlett\" for the Bartlett kernel (Newey-West), \"parzen\" for the Parzen kernel and \"quadratic-spectral\" for the Quadratic Spectral kernel."]], "arch.unitroot.cointegration.DynamicOLSResults.__init__": [[272, 2, 2, 0, "cov", "The estimated parameter covariance."], [272, 2, 2, 0, "cov_type", "The type of the parameter covariance estimator used."], [272, 2, 2, 0, "df_adjust", "Whether to degree of freedom adjust the estimator."], [272, 2, 2, 0, "kernel_est", "The covariance estimator instance used to estimate the parameter covariance."], [272, 2, 2, 0, "lags", "The number of lags included in the model."], [272, 2, 2, 0, "leads", "The number of leads included in the model."], [272, 2, 2, 0, "params", "The estimated model parameters."], [272, 2, 2, 0, "reg_results", "Regression results from fitting statsmodels OLS."], [272, 2, 2, 0, "resid", "The model residuals."]], "arch.unitroot.cointegration.DynamicOLSResults": [[369, 5, 1, 0, "bandwidth", "The bandwidth used in the parameter covariance estimation"], [370, 5, 1, 0, "cov", "The estimated parameter covariance of the cointegrating vector"], [371, 5, 1, 0, "cov_type", "The type of parameter covariance estimator used"], [372, 5, 1, 0, "full_cov", "Parameter covariance of the all model parameters, incl. leads and lags"], [373, 5, 1, 0, "full_params", "The complete set of parameters, including leads and lags"], [374, 5, 1, 0, "kernel", "The kernel used to estimate the covariance"], [375, 5, 1, 0, "lags", "The number of lags included in the model"], [376, 5, 1, 0, "leads", "The number of leads included in the model"], [377, 5, 1, 0, "long_run_variance", "The long-run variance of the regression residual."], [378, 5, 1, 0, "params", "The estimated parameters of the cointegrating vector"], [379, 5, 1, 0, "pvalues", "P-value of the parameters in the cointegrating vector"], [380, 5, 1, 0, "resid", "The model residuals"], [381, 5, 1, 0, "residual_variance", "The variance of the regression residual."], [382, 5, 1, 0, "rsquared", "The model R\u00b2"], [383, 5, 1, 0, "rsquared_adj", "The degree-of-freedom adjusted R\u00b2"], [384, 5, 1, 0, "std_errors", "Standard errors  of the parameters in the cointegrating vector"], [385, 3, 1, 0, "summary", "Summary of the model, containing estimated parameters and std. errors"], [386, 5, 1, 0, "tvalues", "T-statistics of the parameters in the cointegrating vector"]], "arch.unitroot.cointegration.DynamicOLSResults.summary": [[385, 2, 2, 0, "full", "Flag indicating whether to include all estimated parameters (True) or only the parameters of the cointegrating vector"]], "arch.unitroot.cointegration.EngleGrangerTestResults.__init__": [[273, 2, 2, 0, "adf", "The ADF instance used to perform the test and lag selection."], [273, 2, 2, 0, "alternative", "The alternative hypothesis."], [273, 2, 2, 0, "crit_vals", "The critical values of the Engle-Granger specific to the sample size and model dimension."], [273, 2, 2, 0, "null", "The null hypothesis."], [273, 2, 2, 0, "order", "The number of stochastic trends in the null distribution."], [273, 2, 2, 0, "pvalue", "The pvalue of the Engle-Granger test statistic."], [273, 2, 2, 0, "stat", "The Engle-Granger test statistic."], [273, 2, 2, 0, "trend", "The model's trend description."], [273, 2, 2, 0, "xsection", "The OLS results used in the cross-sectional regression."]], "arch.unitroot.cointegration.EngleGrangerTestResults": [[387, 5, 1, 0, "alternative_hypothesis", "The alternative hypothesis"], [388, 5, 1, 0, "cointegrating_vector", "The estimated cointegrating vector."], [389, 5, 1, 0, "critical_values", "Critical Values"], [390, 5, 1, 0, "distribution_order", "The number of stochastic trends under the null hypothesis."], [391, 5, 1, 0, "lags", "The number of lags used in the Augmented Dickey-Fuller regression."], [392, 5, 1, 0, "max_lags", "The maximum number of lags used in the lag-length selection."], [393, 5, 1, 0, "name", "Sets or gets the name of the cointegration test"], [394, 5, 1, 0, "null_hypothesis", "The null hypothesis"], [395, 3, 1, 0, "plot", "Plot the cointegration residuals."], [396, 5, 1, 0, "pvalue", "The p-value of the test statistic."], [397, 5, 1, 0, "resid", "The residual from the cointegrating regression."], [398, 5, 1, 0, "rho", "The estimated coefficient in the Dickey-Fuller Test"], [399, 5, 1, 0, "stat", "The test statistic."], [400, 3, 1, 0, "summary", "Summary of test, containing statistic, p-value and critical values"], [401, 5, 1, 0, "trend", "The trend used in the cointegrating regression"]], "arch.unitroot.cointegration.EngleGrangerTestResults.plot": [[395, 2, 2, 0, "axes", "matplotlib axes instance to hold the figure."], [395, 2, 2, 0, "title", "Title for the figure."]], "arch.unitroot.cointegration.FullyModifiedOLS.__init__": [[274, 2, 2, 0, "trend", "Trend to include in the cointegrating regression. Trends are:"], [274, 2, 2, 0, "x", "The right-hand-side variables in the cointegrating regression."], [274, 2, 2, 0, "x_trend", "Trends that affects affect the x-data but do not appear in the cointegrating regression. x_trend must be at least as large as trend, so that if trend is \"ct\", x_trend must be either \"ct\" or \"ctt\"."], [274, 2, 2, 0, "y", "The left-hand-side variable in the cointegrating regression."]], "arch.unitroot.cointegration.FullyModifiedOLS": [[402, 3, 1, 0, "fit", "Estimate the cointegrating vector."]], "arch.unitroot.cointegration.FullyModifiedOLS.fit": [[402, 2, 2, 0, "bandwidth", "The bandwidth to use. If not provided, the optimal bandwidth is estimated from the data. Setting the bandwidth to 0 and using \"unadjusted\" produces the classic OLS covariance estimator. Setting the bandwidth to 0 and using \"robust\" produces White's covariance estimator."], [402, 2, 2, 0, "df_adjust", "Whether the adjust the parameter covariance to account for the number of parameters estimated in the regression. If true, the parameter covariance estimator is multiplied by T/(T-k) where k is the number of regressors in the model."], [402, 2, 2, 0, "diff", "Use differenced data to estimate the residuals."], [402, 2, 2, 0, "force_int", "Whether the force the estimated optimal bandwidth to be an integer."], [402, 2, 2, 0, "kernel", "The string name of any of any known kernel-based long-run covariance estimators. Common choices are \"bartlett\" for the Bartlett kernel (Newey-West), \"parzen\" for the Parzen kernel and \"quadratic-spectral\" for the Quadratic Spectral kernel."]], "arch.unitroot.cointegration.PhillipsOuliarisTestResults": [[403, 5, 1, 0, "alternative_hypothesis", "The alternative hypothesis"], [404, 5, 1, 0, "bandwidth", "Bandwidth used by the long-run covariance estimator"], [405, 5, 1, 0, "cointegrating_vector", "The estimated cointegrating vector."], [406, 5, 1, 0, "critical_values", "Critical Values"], [407, 5, 1, 0, "distribution_order", "The number of stochastic trends under the null hypothesis."], [408, 5, 1, 0, "kernel", "Name of the long-run covariance estimator"], [409, 5, 1, 0, "name", "Sets or gets the name of the cointegration test"], [410, 5, 1, 0, "null_hypothesis", "The null hypothesis"], [411, 3, 1, 0, "plot", "Plot the cointegration residuals."], [412, 5, 1, 0, "pvalue", "The p-value of the test statistic."], [413, 5, 1, 0, "resid", "The residual from the cointegrating regression."], [414, 5, 1, 0, "stat", "The test statistic."], [415, 3, 1, 0, "summary", "Summary of test, containing statistic, p-value and critical values"], [416, 5, 1, 0, "trend", "The trend used in the cointegrating regression"]], "arch.unitroot.cointegration.PhillipsOuliarisTestResults.plot": [[411, 2, 2, 0, "axes", "matplotlib axes instance to hold the figure."], [411, 2, 2, 0, "title", "Title for the figure."]], "arch.unitroot.cointegration.engle_granger": [[276, 2, 2, 0, "lags", "The number of lagged differences to include in the Augmented Dickey-Fuller test used on the residuals of the"], [276, 2, 2, 0, "max_lags", "The maximum number of lags to consider when using automatic lag-length in the Augmented Dickey-Fuller regression."], [276, 2, 2, 0, "method", "The method used to select the number of lags included in the Augmented Dickey-Fuller regression."], [276, 2, 2, 0, "trend", "Trend to include in the cointegrating regression. Trends are:"], [276, 2, 2, 0, "x", "The right-hand-side variables in the cointegrating regression."], [276, 2, 2, 0, "y", "The left-hand-side variable in the cointegrating regression."]], "arch.unitroot.cointegration.phillips_ouliaris": [[277, 2, 2, 0, "bandwidth", "The bandwidth to use. If not provided, the optimal bandwidth is estimated from the data. Setting the bandwidth to 0 and using \"unadjusted\" produces the classic OLS covariance estimator. Setting the bandwidth to 0 and using \"robust\" produces White's covariance estimator."], [277, 2, 2, 0, "force_int", "Whether the force the estimated optimal bandwidth to be an integer."], [277, 2, 2, 0, "kernel", "The string name of any of any known kernel-based long-run covariance estimators. Common choices are \"bartlett\" for the Bartlett kernel (Newey-West), \"parzen\" for the Parzen kernel and \"quadratic-spectral\" for the Quadratic Spectral kernel."], [277, 2, 2, 0, "test_type", "The test statistic to compute. Supported options are:"], [277, 2, 2, 0, "trend", "Trend to include in the cointegrating regression. Trends are:"], [277, 2, 2, 0, "x", "The right-hand-side variables in the cointegrating regression."], [277, 2, 2, 0, "y", "The left-hand-side variable in the cointegrating regression."]], "arch.univariate": [[425, 1, 1, 0, "APARCH", "Asymmetric Power ARCH (APARCH) volatility process"], [426, 1, 1, 0, "ARCH", "ARCH process"], [427, 1, 1, 0, "ARCHInMean", "(G)ARCH-in-mean model and simulation"], [428, 1, 1, 0, "ARX", "Autoregressive model with optional exogenous regressors estimation and simulation"], [429, 1, 1, 0, "ConstantMean", "Constant mean model estimation and simulation."], [430, 1, 1, 0, "ConstantVariance", "Constant volatility process"], [431, 1, 1, 0, "EGARCH", "EGARCH model estimation"], [432, 1, 1, 0, "EWMAVariance", "Exponentially Weighted Moving-Average (RiskMetrics) Variance process"], [433, 1, 1, 0, "FIGARCH", "FIGARCH model"], [434, 1, 1, 0, "FixedVariance", "Fixed volatility process"], [435, 1, 1, 0, "GARCH", "GARCH and related model estimation"], [436, 1, 1, 0, "GeneralizedError", "Generalized Error distribution for use with ARCH models"], [437, 1, 1, 0, "HARCH", "Heterogeneous ARCH process"], [438, 1, 1, 0, "HARX", "Heterogeneous Autoregression (HAR), with optional exogenous regressors, model estimation and simulation"], [439, 1, 1, 0, "LS", "Least squares model estimation and simulation"], [440, 1, 1, 0, "MIDASHyperbolic", "MIDAS Hyperbolic ARCH process"], [441, 1, 1, 0, "Normal", "Standard normal distribution for use with ARCH models"], [442, 1, 1, 0, "RiskMetrics2006", "RiskMetrics 2006 Variance process"], [443, 1, 1, 0, "SkewStudent", "Standardized Skewed Student's distribution for use with ARCH models"], [444, 1, 1, 0, "StudentsT", "Standardized Student's distribution for use with ARCH models"], [445, 1, 1, 0, "ZeroMean", "Model with zero conditional mean estimation and simulation"], [891, 6, 1, 0, "arch_model", "Initialization of common ARCH model specifications"]], "arch.univariate.APARCH.__init__": [[425, 2, 2, 0, "common_asym", "Restrict all asymmetry terms to share the same asymmetry parameter. If False (default), then there are no restrictions on the o asymmetry parameters."], [425, 2, 2, 0, "delta", "Value to use for a fixed delta in the APARCH model. If not provided, the value of delta is jointly estimated with other model parameters. User provided delta is restricted to lie in (0.05, 4.0)."], [425, 2, 2, 0, "o", "Order of the asymmetric innovation. Must satisfy o<=p."], [425, 2, 2, 0, "p", "Order of the symmetric innovation. Must satisfy p>=o."], [425, 2, 2, 0, "q", "Order of the lagged (transformed) conditional variance"]], "arch.univariate.APARCH": [[454, 3, 1, 0, "backcast", "Construct values for backcasting to start the recursion"], [455, 3, 1, 0, "backcast_transform", "Transformation to apply to user-provided backcast values"], [456, 3, 1, 0, "bounds", "Returns bounds for parameters"], [457, 5, 1, 0, "common_asym", "The value of delta in the model. NaN is delta is estimated."], [458, 3, 1, 0, "compute_variance", "Compute the variance for the ARCH model"], [459, 3, 1, 0, "constraints", "Construct parameter constraints arrays for parameter estimation"], [460, 5, 1, 0, "delta", "The value of delta in the model. NaN is delta is estimated."], [461, 3, 1, 0, "forecast", "Forecast volatility from the model"], [462, 5, 1, 0, "name", "The name of the volatility process"], [463, 5, 1, 0, "num_params", "The number of parameters in the model"], [464, 3, 1, 0, "parameter_names", "Names of model parameters"], [465, 3, 1, 0, "simulate", "Simulate data from the model"], [466, 5, 1, 0, "start", "Index to use to start variance subarray selection"], [467, 3, 1, 0, "starting_values", "Returns starting values for the ARCH model"], [468, 5, 1, 0, "stop", "Index to use to stop variance subarray selection"], [469, 3, 1, 0, "update", "Compute the variance for a single observation"], [470, 5, 1, 0, "updateable", "Flag indicating that the volatility process supports update"], [471, 3, 1, 0, "variance_bounds", "Construct loose bounds for conditional variances."], [472, 5, 1, 0, "volatility_updater", "Get the volatility updater associated with the volatility process"]], "arch.univariate.APARCH.backcast": [[454, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.APARCH.backcast_transform": [[455, 2, 2, 0, "backcast", "User-provided backcast that approximates sigma2[0]."]], "arch.univariate.APARCH.bounds": [[456, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.APARCH.compute_variance": [[458, 2, 2, 0, "backcast", "Value to use when initializing ARCH recursion. Can be an ndarray when the model contains multiple components."], [458, 2, 2, 0, "parameters", "Model parameters"], [458, 2, 2, 0, "resids", "Vector of mean zero residuals"], [458, 2, 2, 0, "sigma2", "Array with same size as resids to store the conditional variance"], [458, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.APARCH.forecast": [[461, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [461, 2, 2, 0, "horizon", "Forecast horizon.  Must be 1 or larger.  Forecasts are produced for horizons in [1, horizon]."], [461, 2, 2, 0, "method", "Method to use when producing the forecast. The default is analytic."], [461, 2, 2, 0, "parameters", "Parameters required to forecast the volatility model"], [461, 2, 2, 0, "random_state", "NumPy RandomState instance to use when method is 'bootstrap'"], [461, 2, 2, 0, "resids", "Residuals to use in the recursion"], [461, 2, 2, 0, "rng", "Callable random number generator required if method is 'simulation'. Must take a single shape input and return random samples numbers with that shape."], [461, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [461, 2, 2, 0, "start", "Index of the first observation to use as the starting point for the forecast.  Default is len(resids)."], [461, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.APARCH.simulate": [[465, 2, 2, 0, "burn", "Number of additional observations to generate when initializing the simulation"], [465, 2, 2, 0, "initial_value", "Scalar or array of initial values to use when initializing the simulation"], [465, 2, 2, 0, "nobs", "Number of data points to simulate"], [465, 2, 2, 0, "parameters", "Parameters required to simulate the volatility model"], [465, 2, 2, 0, "rng", "Callable function that takes a single integer input and returns a vector of random numbers"]], "arch.univariate.APARCH.starting_values": [[467, 2, 2, 0, "resids", "Array of (approximate) residuals to use when computing starting values"]], "arch.univariate.APARCH.update": [[469, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [469, 2, 2, 0, "index", "The numerical index of the variance to compute"], [469, 2, 2, 0, "parameters", "The variance model parameters"], [469, 2, 2, 0, "resids", "The residual array. Only uses resids[:index] when computing sigma2[index]"], [469, 2, 2, 0, "sigma2", "The array containing the variances. Only uses sigma2[:index] when computing sigma2[index]. The computed value is stored in sigma2[index]."], [469, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.APARCH.variance_bounds": [[471, 2, 2, 0, "power", "Power used in the model. 2.0, the default corresponds to standard ARCH models that evolve in squares."], [471, 2, 2, 0, "resids", "Approximate residuals to use to compute the lower and upper bounds on the conditional variance"]], "arch.univariate.ARCH.__init__": [[426, 2, 2, 0, "p", "Order of the symmetric innovation"]], "arch.univariate.ARCH": [[473, 3, 1, 0, "backcast", "Construct values for backcasting to start the recursion"], [474, 3, 1, 0, "backcast_transform", "Transformation to apply to user-provided backcast values"], [475, 3, 1, 0, "bounds", "Returns bounds for parameters"], [476, 3, 1, 0, "compute_variance", "Compute the variance for the ARCH model"], [477, 3, 1, 0, "constraints", "Construct parameter constraints arrays for parameter estimation"], [478, 3, 1, 0, "forecast", "Forecast volatility from the model"], [479, 5, 1, 0, "name", "The name of the volatility process"], [480, 5, 1, 0, "num_params", "The number of parameters in the model"], [481, 3, 1, 0, "parameter_names", "Names of model parameters"], [482, 3, 1, 0, "simulate", "Simulate data from the model"], [483, 5, 1, 0, "start", "Index to use to start variance subarray selection"], [484, 3, 1, 0, "starting_values", "Returns starting values for the ARCH model"], [485, 5, 1, 0, "stop", "Index to use to stop variance subarray selection"], [486, 3, 1, 0, "update", "Compute the variance for a single observation"], [487, 5, 1, 0, "updateable", "Flag indicating that the volatility process supports update"], [488, 3, 1, 0, "variance_bounds", "Construct loose bounds for conditional variances."], [489, 5, 1, 0, "volatility_updater", "Get the volatility updater associated with the volatility process"]], "arch.univariate.ARCH.backcast": [[473, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.ARCH.backcast_transform": [[474, 2, 2, 0, "backcast", "User-provided backcast that approximates sigma2[0]."]], "arch.univariate.ARCH.bounds": [[475, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.ARCH.compute_variance": [[476, 2, 2, 0, "backcast", "Value to use when initializing ARCH recursion. Can be an ndarray when the model contains multiple components."], [476, 2, 2, 0, "parameters", "Model parameters"], [476, 2, 2, 0, "resids", "Vector of mean zero residuals"], [476, 2, 2, 0, "sigma2", "Array with same size as resids to store the conditional variance"], [476, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.ARCH.forecast": [[478, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [478, 2, 2, 0, "horizon", "Forecast horizon.  Must be 1 or larger.  Forecasts are produced for horizons in [1, horizon]."], [478, 2, 2, 0, "method", "Method to use when producing the forecast. The default is analytic."], [478, 2, 2, 0, "parameters", "Parameters required to forecast the volatility model"], [478, 2, 2, 0, "random_state", "NumPy RandomState instance to use when method is 'bootstrap'"], [478, 2, 2, 0, "resids", "Residuals to use in the recursion"], [478, 2, 2, 0, "rng", "Callable random number generator required if method is 'simulation'. Must take a single shape input and return random samples numbers with that shape."], [478, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [478, 2, 2, 0, "start", "Index of the first observation to use as the starting point for the forecast.  Default is len(resids)."], [478, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.ARCH.simulate": [[482, 2, 2, 0, "burn", "Number of additional observations to generate when initializing the simulation"], [482, 2, 2, 0, "initial_value", "Scalar or array of initial values to use when initializing the simulation"], [482, 2, 2, 0, "nobs", "Number of data points to simulate"], [482, 2, 2, 0, "parameters", "Parameters required to simulate the volatility model"], [482, 2, 2, 0, "rng", "Callable function that takes a single integer input and returns a vector of random numbers"]], "arch.univariate.ARCH.starting_values": [[484, 2, 2, 0, "resids", "Array of (approximate) residuals to use when computing starting values"]], "arch.univariate.ARCH.update": [[486, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [486, 2, 2, 0, "index", "The numerical index of the variance to compute"], [486, 2, 2, 0, "parameters", "The variance model parameters"], [486, 2, 2, 0, "resids", "The residual array. Only uses resids[:index] when computing sigma2[index]"], [486, 2, 2, 0, "sigma2", "The array containing the variances. Only uses sigma2[:index] when computing sigma2[index]. The computed value is stored in sigma2[index]."], [486, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.ARCH.variance_bounds": [[488, 2, 2, 0, "power", "Power used in the model. 2.0, the default corresponds to standard ARCH models that evolve in squares."], [488, 2, 2, 0, "resids", "Approximate residuals to use to compute the lower and upper bounds on the conditional variance"]], "arch.univariate.ARCHInMean.__init__": [[427, 2, 2, 0, "constant", "Flag whether the model should include a constant"], [427, 2, 2, 0, "distribution", "Error distribution to use in the model"], [427, 2, 2, 0, "form", "The form of the conditional variance that appears in the mean equation. The string names use the log of the conditional variance (\"log\"), the square-root of the conditional variance (\"vol\") or the conditional variance. When specified using a float, interpreted as \\sigma_t^{form} so that 1 is equivalent to \"vol\" and 2 is equivalent to \"var\". When using a number, must be different from 0."], [427, 2, 2, 0, "hold_back", "Number of observations at the start of the sample to exclude when estimating model parameters.  Used when comparing models with different lag lengths to estimate on the common sample."], [427, 2, 2, 0, "lags", "Description of lag structure of the HAR.  Scalar included all lags between 1 and the value.  A 1-d array includes the AR lags lags[0], lags[1], ..."], [427, 2, 2, 0, "rescale", "Flag indicating whether to automatically rescale data if the scale of the data is likely to produce convergence issues when estimating model parameters. If False, the model is estimated on the data without transformation.  If True, than y is rescaled and the new scale is reported in the estimation results."], [427, 2, 2, 0, "volatility", "Volatility process to use in the model. volatility.updateable must return True."], [427, 2, 2, 0, "x", "nobs by k element array containing exogenous regressors"], [427, 2, 2, 0, "y", "nobs element vector containing the dependent variable"]], "arch.univariate.ARCHInMean": [[490, 3, 1, 0, "bounds", "Construct bounds for parameters to use in non-linear optimization"], [491, 3, 1, 0, "compute_param_cov", "Computes parameter covariances using numerical derivatives."], [492, 3, 1, 0, "constraints", "Construct linear constraint arrays  for use in non-linear optimization"], [493, 5, 1, 0, "distribution", "Set or gets the error distribution"], [494, 3, 1, 0, "fit", "Estimate model parameters"], [495, 3, 1, 0, "fix", "Allows an ARCHModelFixedResult to be constructed from fixed parameters."], [496, 3, 1, 0, "forecast", "Construct forecasts from estimated model"], [497, 5, 1, 0, "form", "The form of the conditional variance in the mean"], [498, 5, 1, 0, "name", "The name of the model."], [499, 5, 1, 0, "num_params", "Returns the number of parameters"], [500, 3, 1, 0, "parameter_names", "List of parameters names"], [501, 3, 1, 0, "resids", "Compute model residuals"], [502, 3, 1, 0, "simulate", "Simulates data from a linear regression, AR or HAR models"], [503, 3, 1, 0, "starting_values", "Returns starting values for the mean model, often the same as the values returned from fit"], [504, 5, 1, 0, "volatility", "Set or gets the volatility process"], [505, 5, 1, 0, "x", "Gets the value of the exogenous regressors in the model"], [506, 5, 1, 0, "y", "Returns the dependent variable"]], "arch.univariate.ARCHInMean.compute_param_cov": [[491, 2, 2, 0, "backcast", "Value to use for pre-sample observations"], [491, 2, 2, 0, "params", "Model parameters"], [491, 2, 2, 0, "robust", "Flag indicating whether to use robust standard errors (True) or classic MLE (False)"]], "arch.univariate.ARCHInMean.fit": [[494, 2, 2, 0, "backcast", "Value to use as backcast. Should be measure \\sigma^2_0 since model-specific non-linear transformations are applied to value before computing the variance recursions."], [494, 2, 2, 0, "cov_type", "Estimation method of parameter covariance.  Supported options are 'robust', which does not assume the Information Matrix Equality holds and 'classic' which does.  In the ARCH literature, 'robust' corresponds to Bollerslev-Wooldridge covariance estimator."], [494, 2, 2, 0, "disp", "Either 'final' to print optimization result or 'off' to display nothing. If using a boolean, False is \"off\" and True is \"final\""], [494, 2, 2, 0, "first_obs", "First observation to use when estimating model"], [494, 2, 2, 0, "last_obs", "Last observation to use when estimating model"], [494, 2, 2, 0, "options", "Options to pass to scipy.optimize.minimize.  Valid entries include 'ftol', 'eps', 'disp', and 'maxiter'."], [494, 2, 2, 0, "show_warning", "Flag indicating whether convergence warnings should be shown."], [494, 2, 2, 0, "starting_values", "Array of starting values to use.  If not provided, starting values are constructed by the model components."], [494, 2, 2, 0, "tol", "Tolerance for termination."], [494, 2, 2, 0, "update_freq", "Frequency of iteration updates.  Output is generated every update_freq iterations. Set to 0 to disable iterative output."]], "arch.univariate.ARCHInMean.fix": [[495, 2, 2, 0, "first_obs", "First observation to use when fixing model"], [495, 2, 2, 0, "last_obs", "Last observation to use when fixing model"], [495, 2, 2, 0, "params", "User specified parameters to use when generating the result. Must have the correct number of parameters for a given choice of mean model, volatility model and distribution."]], "arch.univariate.ARCHInMean.forecast": [[496, 2, 2, 0, "align", "Either 'origin' or 'target'.  When set of 'origin', the t-th row of forecasts contains the forecasts for t+1, t+2, ..., t+h. When set to 'target', the t-th row contains the 1-step ahead forecast from time t-1, the 2 step from time t-2, ..., and the h-step from time t-h.  'target' simplified computing forecast errors since the realization and h-step forecast are aligned."], [496, 2, 2, 0, "horizon", "Number of steps to forecast"], [496, 2, 2, 0, "method", "Method to use when producing the forecast. The default is analytic. The method only affects the variance forecast generation.  Not all volatility models support all methods. In particular, volatility models that do not evolve in squares such as EGARCH or TARCH do not support the 'analytic' method for horizons > 1."], [496, 2, 2, 0, "params", "Parameters required to forecast. Must be identical in shape to the parameters computed by fitting the model."], [496, 2, 2, 0, "random_state", "NumPy RandomState instance to use when method is 'bootstrap'"], [496, 2, 2, 0, "reindex", "Whether to reindex the forecasts to have the same dimension as the series being forecast. Prior to 4.18 this was the default. As of 4.19 this is now optional. If not provided, a warning is raised about the future change in the default which will occur after September 2021."], [496, 2, 2, 0, "rng", "Custom random number generator to use in simulation-based forecasts. Must produce random samples using the syntax rng(size) where size the 2-element tuple (simulations, horizon)."], [496, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [496, 2, 2, 0, "start", "An integer, datetime or str indicating the first observation to produce the forecast for.  Datetimes can only be used with pandas inputs that have a datetime index. Strings must be convertible to a date time, such as in '1945-01-01'."], [496, 2, 2, 0, "x", "Values to use for exogenous regressors if any are included in the model. Three formats are accepted:"]], "arch.univariate.ARCHInMean.resids": [[501, 2, 2, 0, "params", "Model parameters"], [501, 2, 2, 0, "regressors", "Alternative regressor values to use when computing model residuals"], [501, 2, 2, 0, "y", "Alternative values to use when computing model residuals"]], "arch.univariate.ARCHInMean.simulate": [[502, 2, 2, 0, "burn", "Number of values to simulate to initialize the model and remove dependence on initial values."], [502, 2, 2, 0, "initial_value", "Either a scalar value or max(lags) array set of initial values to use when initializing the model.  If omitted, 0.0 is used."], [502, 2, 2, 0, "initial_value_vol", "An array or scalar to use when initializing the volatility process."], [502, 2, 2, 0, "nobs", "Length of series to simulate"], [502, 2, 2, 0, "params", "Parameters to use when simulating the model.  Parameter order is [mean volatility distribution] where the parameters of the mean model are ordered [constant lag[0] lag[1] ... lag[p] ex[0] ... ex[k-1]] where lag[j] indicates the coefficient on the jth lag in the model and ex[j] is the coefficient on the jth exogenous variable."], [502, 2, 2, 0, "x", "nobs + burn by k array of exogenous variables to include in the simulation."]], "arch.univariate.ARX.__init__": [[428, 2, 2, 0, "constant", "Flag whether the model should include a constant"], [428, 2, 2, 0, "distribution", "Error distribution to use in the model"], [428, 2, 2, 0, "hold_back", "Number of observations at the start of the sample to exclude when estimating model parameters.  Used when comparing models with different lag lengths to estimate on the common sample."], [428, 2, 2, 0, "lags", "Description of lag structure of the HAR.  Scalar included all lags between 1 and the value.  A 1-d array includes the AR lags lags[0], lags[1], ..."], [428, 2, 2, 0, "rescale", "Flag indicating whether to automatically rescale data if the scale of the data is likely to produce convergence issues when estimating model parameters. If False, the model is estimated on the data without transformation.  If True, than y is rescaled and the new scale is reported in the estimation results."], [428, 2, 2, 0, "volatility", "Volatility process to use in the model"], [428, 2, 2, 0, "x", "nobs by k element array containing exogenous regressors"], [428, 2, 2, 0, "y", "nobs element vector containing the dependent variable"]], "arch.univariate.ARX": [[507, 3, 1, 0, "bounds", "Construct bounds for parameters to use in non-linear optimization"], [508, 3, 1, 0, "compute_param_cov", "Computes parameter covariances using numerical derivatives."], [509, 3, 1, 0, "constraints", "Construct linear constraint arrays  for use in non-linear optimization"], [510, 5, 1, 0, "distribution", "Set or gets the error distribution"], [511, 3, 1, 0, "fit", "Estimate model parameters"], [512, 3, 1, 0, "fix", "Allows an ARCHModelFixedResult to be constructed from fixed parameters."], [513, 3, 1, 0, "forecast", "Construct forecasts from estimated model"], [514, 5, 1, 0, "name", "The name of the model."], [515, 5, 1, 0, "num_params", "Returns the number of parameters"], [516, 3, 1, 0, "parameter_names", "List of parameters names"], [517, 3, 1, 0, "resids", "Compute model residuals"], [518, 3, 1, 0, "simulate", "Simulates data from a linear regression, AR or HAR models"], [519, 3, 1, 0, "starting_values", "Returns starting values for the mean model, often the same as the values returned from fit"], [520, 5, 1, 0, "volatility", "Set or gets the volatility process"], [521, 5, 1, 0, "x", "Gets the value of the exogenous regressors in the model"], [522, 5, 1, 0, "y", "Returns the dependent variable"]], "arch.univariate.ARX.compute_param_cov": [[508, 2, 2, 0, "backcast", "Value to use for pre-sample observations"], [508, 2, 2, 0, "params", "Model parameters"], [508, 2, 2, 0, "robust", "Flag indicating whether to use robust standard errors (True) or classic MLE (False)"]], "arch.univariate.ARX.fit": [[511, 2, 2, 0, "backcast", "Value to use as backcast. Should be measure \\sigma^2_0 since model-specific non-linear transformations are applied to value before computing the variance recursions."], [511, 2, 2, 0, "cov_type", "Estimation method of parameter covariance.  Supported options are 'robust', which does not assume the Information Matrix Equality holds and 'classic' which does.  In the ARCH literature, 'robust' corresponds to Bollerslev-Wooldridge covariance estimator."], [511, 2, 2, 0, "disp", "Either 'final' to print optimization result or 'off' to display nothing. If using a boolean, False is \"off\" and True is \"final\""], [511, 2, 2, 0, "first_obs", "First observation to use when estimating model"], [511, 2, 2, 0, "last_obs", "Last observation to use when estimating model"], [511, 2, 2, 0, "options", "Options to pass to scipy.optimize.minimize.  Valid entries include 'ftol', 'eps', 'disp', and 'maxiter'."], [511, 2, 2, 0, "show_warning", "Flag indicating whether convergence warnings should be shown."], [511, 2, 2, 0, "starting_values", "Array of starting values to use.  If not provided, starting values are constructed by the model components."], [511, 2, 2, 0, "tol", "Tolerance for termination."], [511, 2, 2, 0, "update_freq", "Frequency of iteration updates.  Output is generated every update_freq iterations. Set to 0 to disable iterative output."]], "arch.univariate.ARX.fix": [[512, 2, 2, 0, "first_obs", "First observation to use when fixing model"], [512, 2, 2, 0, "last_obs", "Last observation to use when fixing model"], [512, 2, 2, 0, "params", "User specified parameters to use when generating the result. Must have the correct number of parameters for a given choice of mean model, volatility model and distribution."]], "arch.univariate.ARX.forecast": [[513, 2, 2, 0, "align", "Either 'origin' or 'target'.  When set of 'origin', the t-th row of forecasts contains the forecasts for t+1, t+2, ..., t+h. When set to 'target', the t-th row contains the 1-step ahead forecast from time t-1, the 2 step from time t-2, ..., and the h-step from time t-h.  'target' simplified computing forecast errors since the realization and h-step forecast are aligned."], [513, 2, 2, 0, "horizon", "Number of steps to forecast"], [513, 2, 2, 0, "method", "Method to use when producing the forecast. The default is analytic. The method only affects the variance forecast generation.  Not all volatility models support all methods. In particular, volatility models that do not evolve in squares such as EGARCH or TARCH do not support the 'analytic' method for horizons > 1."], [513, 2, 2, 0, "params", "Parameters required to forecast. Must be identical in shape to the parameters computed by fitting the model."], [513, 2, 2, 0, "random_state", "NumPy RandomState instance to use when method is 'bootstrap'"], [513, 2, 2, 0, "reindex", "Whether to reindex the forecasts to have the same dimension as the series being forecast. Prior to 4.18 this was the default. As of 4.19 this is now optional. If not provided, a warning is raised about the future change in the default which will occur after September 2021."], [513, 2, 2, 0, "rng", "Custom random number generator to use in simulation-based forecasts. Must produce random samples using the syntax rng(size) where size the 2-element tuple (simulations, horizon)."], [513, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [513, 2, 2, 0, "start", "An integer, datetime or str indicating the first observation to produce the forecast for.  Datetimes can only be used with pandas inputs that have a datetime index. Strings must be convertible to a date time, such as in '1945-01-01'."], [513, 2, 2, 0, "x", "Values to use for exogenous regressors if any are included in the model. Three formats are accepted:"]], "arch.univariate.ARX.resids": [[517, 2, 2, 0, "params", "Model parameters"], [517, 2, 2, 0, "regressors", "Alternative regressor values to use when computing model residuals"], [517, 2, 2, 0, "y", "Alternative values to use when computing model residuals"]], "arch.univariate.ARX.simulate": [[518, 2, 2, 0, "burn", "Number of values to simulate to initialize the model and remove dependence on initial values."], [518, 2, 2, 0, "initial_value", "Either a scalar value or max(lags) array set of initial values to use when initializing the model.  If omitted, 0.0 is used."], [518, 2, 2, 0, "initial_value_vol", "An array or scalar to use when initializing the volatility process."], [518, 2, 2, 0, "nobs", "Length of series to simulate"], [518, 2, 2, 0, "params", "Parameters to use when simulating the model.  Parameter order is [mean volatility distribution] where the parameters of the mean model are ordered [constant lag[0] lag[1] ... lag[p] ex[0] ... ex[k-1]] where lag[j] indicates the coefficient on the jth lag in the model and ex[j] is the coefficient on the jth exogenous variable."], [518, 2, 2, 0, "x", "nobs + burn by k array of exogenous variables to include in the simulation."]], "arch.univariate.ConstantMean.__init__": [[429, 2, 2, 0, "distribution", "Error distribution to use in the model"], [429, 2, 2, 0, "hold_back", "Number of observations at the start of the sample to exclude when estimating model parameters.  Used when comparing models with different lag lengths to estimate on the common sample."], [429, 2, 2, 0, "rescale", "Flag indicating whether to automatically rescale data if the scale of the data is likely to produce convergence issues when estimating model parameters. If False, the model is estimated on the data without transformation.  If True, than y is rescaled and the new scale is reported in the estimation results."], [429, 2, 2, 0, "volatility", "Volatility process to use in the model"], [429, 2, 2, 0, "y", "nobs element vector containing the dependent variable"]], "arch.univariate.ConstantMean": [[523, 3, 1, 0, "bounds", "Construct bounds for parameters to use in non-linear optimization"], [524, 3, 1, 0, "compute_param_cov", "Computes parameter covariances using numerical derivatives."], [525, 3, 1, 0, "constraints", "Construct linear constraint arrays  for use in non-linear optimization"], [526, 5, 1, 0, "distribution", "Set or gets the error distribution"], [527, 3, 1, 0, "fit", "Estimate model parameters"], [528, 3, 1, 0, "fix", "Allows an ARCHModelFixedResult to be constructed from fixed parameters."], [529, 3, 1, 0, "forecast", "Construct forecasts from estimated model"], [530, 5, 1, 0, "name", "The name of the model."], [531, 5, 1, 0, "num_params", "Returns the number of parameters"], [532, 3, 1, 0, "parameter_names", "List of parameters names"], [533, 3, 1, 0, "resids", "Compute model residuals"], [534, 3, 1, 0, "simulate", "Simulated data from a constant mean model"], [535, 3, 1, 0, "starting_values", "Returns starting values for the mean model, often the same as the values returned from fit"], [536, 5, 1, 0, "volatility", "Set or gets the volatility process"], [537, 5, 1, 0, "x", "Gets the value of the exogenous regressors in the model"], [538, 5, 1, 0, "y", "Returns the dependent variable"]], "arch.univariate.ConstantMean.compute_param_cov": [[524, 2, 2, 0, "backcast", "Value to use for pre-sample observations"], [524, 2, 2, 0, "params", "Model parameters"], [524, 2, 2, 0, "robust", "Flag indicating whether to use robust standard errors (True) or classic MLE (False)"]], "arch.univariate.ConstantMean.fit": [[527, 2, 2, 0, "backcast", "Value to use as backcast. Should be measure \\sigma^2_0 since model-specific non-linear transformations are applied to value before computing the variance recursions."], [527, 2, 2, 0, "cov_type", "Estimation method of parameter covariance.  Supported options are 'robust', which does not assume the Information Matrix Equality holds and 'classic' which does.  In the ARCH literature, 'robust' corresponds to Bollerslev-Wooldridge covariance estimator."], [527, 2, 2, 0, "disp", "Either 'final' to print optimization result or 'off' to display nothing. If using a boolean, False is \"off\" and True is \"final\""], [527, 2, 2, 0, "first_obs", "First observation to use when estimating model"], [527, 2, 2, 0, "last_obs", "Last observation to use when estimating model"], [527, 2, 2, 0, "options", "Options to pass to scipy.optimize.minimize.  Valid entries include 'ftol', 'eps', 'disp', and 'maxiter'."], [527, 2, 2, 0, "show_warning", "Flag indicating whether convergence warnings should be shown."], [527, 2, 2, 0, "starting_values", "Array of starting values to use.  If not provided, starting values are constructed by the model components."], [527, 2, 2, 0, "tol", "Tolerance for termination."], [527, 2, 2, 0, "update_freq", "Frequency of iteration updates.  Output is generated every update_freq iterations. Set to 0 to disable iterative output."]], "arch.univariate.ConstantMean.fix": [[528, 2, 2, 0, "first_obs", "First observation to use when fixing model"], [528, 2, 2, 0, "last_obs", "Last observation to use when fixing model"], [528, 2, 2, 0, "params", "User specified parameters to use when generating the result. Must have the correct number of parameters for a given choice of mean model, volatility model and distribution."]], "arch.univariate.ConstantMean.forecast": [[529, 2, 2, 0, "align", "Either 'origin' or 'target'.  When set of 'origin', the t-th row of forecasts contains the forecasts for t+1, t+2, ..., t+h. When set to 'target', the t-th row contains the 1-step ahead forecast from time t-1, the 2 step from time t-2, ..., and the h-step from time t-h.  'target' simplified computing forecast errors since the realization and h-step forecast are aligned."], [529, 2, 2, 0, "horizon", "Number of steps to forecast"], [529, 2, 2, 0, "method", "Method to use when producing the forecast. The default is analytic. The method only affects the variance forecast generation.  Not all volatility models support all methods. In particular, volatility models that do not evolve in squares such as EGARCH or TARCH do not support the 'analytic' method for horizons > 1."], [529, 2, 2, 0, "params", "Parameters required to forecast. Must be identical in shape to the parameters computed by fitting the model."], [529, 2, 2, 0, "random_state", "NumPy RandomState instance to use when method is 'bootstrap'"], [529, 2, 2, 0, "reindex", "Whether to reindex the forecasts to have the same dimension as the series being forecast. Prior to 4.18 this was the default. As of 4.19 this is now optional. If not provided, a warning is raised about the future change in the default which will occur after September 2021."], [529, 2, 2, 0, "rng", "Custom random number generator to use in simulation-based forecasts. Must produce random samples using the syntax rng(size) where size the 2-element tuple (simulations, horizon)."], [529, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [529, 2, 2, 0, "start", "An integer, datetime or str indicating the first observation to produce the forecast for.  Datetimes can only be used with pandas inputs that have a datetime index. Strings must be convertible to a date time, such as in '1945-01-01'."], [529, 2, 2, 0, "x", "Values to use for exogenous regressors if any are included in the model. Three formats are accepted:"]], "arch.univariate.ConstantMean.resids": [[533, 2, 2, 0, "params", "Model parameters"], [533, 2, 2, 0, "regressors", "Alternative regressor values to use when computing model residuals"], [533, 2, 2, 0, "y", "Alternative values to use when computing model residuals"]], "arch.univariate.ConstantMean.simulate": [[534, 2, 2, 0, "burn", "Number of values to simulate to initialize the model and remove dependence on initial values."], [534, 2, 2, 0, "initial_value", "This value is not used."], [534, 2, 2, 0, "initial_value_vol", "An array or scalar to use when initializing the volatility process."], [534, 2, 2, 0, "nobs", "Length of series to simulate"], [534, 2, 2, 0, "params", "Parameters to use when simulating the model.  Parameter order is [mean volatility distribution]. There is one parameter in the mean model, mu."], [534, 2, 2, 0, "x", "This value is not used."]], "arch.univariate.ConstantVariance": [[539, 3, 1, 0, "backcast", "Construct values for backcasting to start the recursion"], [540, 3, 1, 0, "backcast_transform", "Transformation to apply to user-provided backcast values"], [541, 3, 1, 0, "bounds", "Returns bounds for parameters"], [542, 3, 1, 0, "compute_variance", "Compute the variance for the ARCH model"], [543, 3, 1, 0, "constraints", "Construct parameter constraints arrays for parameter estimation"], [544, 3, 1, 0, "forecast", "Forecast volatility from the model"], [545, 5, 1, 0, "name", "The name of the volatility process"], [546, 5, 1, 0, "num_params", "The number of parameters in the model"], [547, 3, 1, 0, "parameter_names", "Names of model parameters"], [548, 3, 1, 0, "simulate", "Simulate data from the model"], [549, 5, 1, 0, "start", "Index to use to start variance subarray selection"], [550, 3, 1, 0, "starting_values", "Returns starting values for the ARCH model"], [551, 5, 1, 0, "stop", "Index to use to stop variance subarray selection"], [552, 3, 1, 0, "update", "Compute the variance for a single observation"], [553, 5, 1, 0, "updateable", "Flag indicating that the volatility process supports update"], [554, 3, 1, 0, "variance_bounds", "Construct loose bounds for conditional variances."], [555, 5, 1, 0, "volatility_updater", "Get the volatility updater associated with the volatility process"]], "arch.univariate.ConstantVariance.backcast": [[539, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.ConstantVariance.backcast_transform": [[540, 2, 2, 0, "backcast", "User-provided backcast that approximates sigma2[0]."]], "arch.univariate.ConstantVariance.bounds": [[541, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.ConstantVariance.compute_variance": [[542, 2, 2, 0, "backcast", "Value to use when initializing ARCH recursion. Can be an ndarray when the model contains multiple components."], [542, 2, 2, 0, "parameters", "Model parameters"], [542, 2, 2, 0, "resids", "Vector of mean zero residuals"], [542, 2, 2, 0, "sigma2", "Array with same size as resids to store the conditional variance"], [542, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.ConstantVariance.forecast": [[544, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [544, 2, 2, 0, "horizon", "Forecast horizon.  Must be 1 or larger.  Forecasts are produced for horizons in [1, horizon]."], [544, 2, 2, 0, "method", "Method to use when producing the forecast. The default is analytic."], [544, 2, 2, 0, "parameters", "Parameters required to forecast the volatility model"], [544, 2, 2, 0, "random_state", "NumPy RandomState instance to use when method is 'bootstrap'"], [544, 2, 2, 0, "resids", "Residuals to use in the recursion"], [544, 2, 2, 0, "rng", "Callable random number generator required if method is 'simulation'. Must take a single shape input and return random samples numbers with that shape."], [544, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [544, 2, 2, 0, "start", "Index of the first observation to use as the starting point for the forecast.  Default is len(resids)."], [544, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.ConstantVariance.simulate": [[548, 2, 2, 0, "burn", "Number of additional observations to generate when initializing the simulation"], [548, 2, 2, 0, "initial_value", "Scalar or array of initial values to use when initializing the simulation"], [548, 2, 2, 0, "nobs", "Number of data points to simulate"], [548, 2, 2, 0, "parameters", "Parameters required to simulate the volatility model"], [548, 2, 2, 0, "rng", "Callable function that takes a single integer input and returns a vector of random numbers"]], "arch.univariate.ConstantVariance.starting_values": [[550, 2, 2, 0, "resids", "Array of (approximate) residuals to use when computing starting values"]], "arch.univariate.ConstantVariance.update": [[552, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [552, 2, 2, 0, "index", "The numerical index of the variance to compute"], [552, 2, 2, 0, "parameters", "The variance model parameters"], [552, 2, 2, 0, "resids", "The residual array. Only uses resids[:index] when computing sigma2[index]"], [552, 2, 2, 0, "sigma2", "The array containing the variances. Only uses sigma2[:index] when computing sigma2[index]. The computed value is stored in sigma2[index]."], [552, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.ConstantVariance.variance_bounds": [[554, 2, 2, 0, "power", "Power used in the model. 2.0, the default corresponds to standard ARCH models that evolve in squares."], [554, 2, 2, 0, "resids", "Approximate residuals to use to compute the lower and upper bounds on the conditional variance"]], "arch.univariate.EGARCH.__init__": [[431, 2, 2, 0, "o", "Order of the asymmetric innovation"], [431, 2, 2, 0, "p", "Order of the symmetric innovation"], [431, 2, 2, 0, "q", "Order of the lagged (transformed) conditional variance"]], "arch.univariate.EGARCH": [[556, 3, 1, 0, "backcast", "Construct values for backcasting to start the recursion"], [557, 3, 1, 0, "backcast_transform", "Transformation to apply to user-provided backcast values"], [558, 3, 1, 0, "bounds", "Returns bounds for parameters"], [559, 3, 1, 0, "compute_variance", "Compute the variance for the ARCH model"], [560, 3, 1, 0, "constraints", "Construct parameter constraints arrays for parameter estimation"], [561, 3, 1, 0, "forecast", "Forecast volatility from the model"], [562, 5, 1, 0, "name", "The name of the volatility process"], [563, 5, 1, 0, "num_params", "The number of parameters in the model"], [564, 3, 1, 0, "parameter_names", "Names of model parameters"], [565, 3, 1, 0, "simulate", "Simulate data from the model"], [566, 5, 1, 0, "start", "Index to use to start variance subarray selection"], [567, 3, 1, 0, "starting_values", "Returns starting values for the ARCH model"], [568, 5, 1, 0, "stop", "Index to use to stop variance subarray selection"], [569, 3, 1, 0, "update", "Compute the variance for a single observation"], [570, 5, 1, 0, "updateable", "Flag indicating that the volatility process supports update"], [571, 3, 1, 0, "variance_bounds", "Construct loose bounds for conditional variances."], [572, 5, 1, 0, "volatility_updater", "Get the volatility updater associated with the volatility process"]], "arch.univariate.EGARCH.backcast": [[556, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.EGARCH.backcast_transform": [[557, 2, 2, 0, "backcast", "User-provided backcast that approximates sigma2[0]."]], "arch.univariate.EGARCH.bounds": [[558, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.EGARCH.compute_variance": [[559, 2, 2, 0, "backcast", "Value to use when initializing ARCH recursion. Can be an ndarray when the model contains multiple components."], [559, 2, 2, 0, "parameters", "Model parameters"], [559, 2, 2, 0, "resids", "Vector of mean zero residuals"], [559, 2, 2, 0, "sigma2", "Array with same size as resids to store the conditional variance"], [559, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.EGARCH.forecast": [[561, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [561, 2, 2, 0, "horizon", "Forecast horizon.  Must be 1 or larger.  Forecasts are produced for horizons in [1, horizon]."], [561, 2, 2, 0, "method", "Method to use when producing the forecast. The default is analytic."], [561, 2, 2, 0, "parameters", "Parameters required to forecast the volatility model"], [561, 2, 2, 0, "random_state", "NumPy RandomState instance to use when method is 'bootstrap'"], [561, 2, 2, 0, "resids", "Residuals to use in the recursion"], [561, 2, 2, 0, "rng", "Callable random number generator required if method is 'simulation'. Must take a single shape input and return random samples numbers with that shape."], [561, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [561, 2, 2, 0, "start", "Index of the first observation to use as the starting point for the forecast.  Default is len(resids)."], [561, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.EGARCH.simulate": [[565, 2, 2, 0, "burn", "Number of additional observations to generate when initializing the simulation"], [565, 2, 2, 0, "initial_value", "Scalar or array of initial values to use when initializing the simulation"], [565, 2, 2, 0, "nobs", "Number of data points to simulate"], [565, 2, 2, 0, "parameters", "Parameters required to simulate the volatility model"], [565, 2, 2, 0, "rng", "Callable function that takes a single integer input and returns a vector of random numbers"]], "arch.univariate.EGARCH.starting_values": [[567, 2, 2, 0, "resids", "Array of (approximate) residuals to use when computing starting values"]], "arch.univariate.EGARCH.update": [[569, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [569, 2, 2, 0, "index", "The numerical index of the variance to compute"], [569, 2, 2, 0, "parameters", "The variance model parameters"], [569, 2, 2, 0, "resids", "The residual array. Only uses resids[:index] when computing sigma2[index]"], [569, 2, 2, 0, "sigma2", "The array containing the variances. Only uses sigma2[:index] when computing sigma2[index]. The computed value is stored in sigma2[index]."], [569, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.EGARCH.variance_bounds": [[571, 2, 2, 0, "power", "Power used in the model. 2.0, the default corresponds to standard ARCH models that evolve in squares."], [571, 2, 2, 0, "resids", "Approximate residuals to use to compute the lower and upper bounds on the conditional variance"]], "arch.univariate.EWMAVariance.__init__": [[432, 2, 2, 0, "lam", "Smoothing parameter. Default is 0.94. Set to None to estimate lam jointly with other model parameters"]], "arch.univariate.EWMAVariance": [[573, 3, 1, 0, "backcast", "Construct values for backcasting to start the recursion"], [574, 3, 1, 0, "backcast_transform", "Transformation to apply to user-provided backcast values"], [575, 3, 1, 0, "bounds", "Returns bounds for parameters"], [576, 3, 1, 0, "compute_variance", "Compute the variance for the ARCH model"], [577, 3, 1, 0, "constraints", "Construct parameter constraints arrays for parameter estimation"], [578, 3, 1, 0, "forecast", "Forecast volatility from the model"], [579, 5, 1, 0, "name", "The name of the volatility process"], [580, 5, 1, 0, "num_params", "The number of parameters in the model"], [581, 3, 1, 0, "parameter_names", "Names of model parameters"], [582, 3, 1, 0, "simulate", "Simulate data from the model"], [583, 5, 1, 0, "start", "Index to use to start variance subarray selection"], [584, 3, 1, 0, "starting_values", "Returns starting values for the ARCH model"], [585, 5, 1, 0, "stop", "Index to use to stop variance subarray selection"], [586, 3, 1, 0, "update", "Compute the variance for a single observation"], [587, 5, 1, 0, "updateable", "Flag indicating that the volatility process supports update"], [588, 3, 1, 0, "variance_bounds", "Construct loose bounds for conditional variances."], [589, 5, 1, 0, "volatility_updater", "Get the volatility updater associated with the volatility process"]], "arch.univariate.EWMAVariance.backcast": [[573, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.EWMAVariance.backcast_transform": [[574, 2, 2, 0, "backcast", "User-provided backcast that approximates sigma2[0]."]], "arch.univariate.EWMAVariance.bounds": [[575, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.EWMAVariance.compute_variance": [[576, 2, 2, 0, "backcast", "Value to use when initializing ARCH recursion. Can be an ndarray when the model contains multiple components."], [576, 2, 2, 0, "parameters", "Model parameters"], [576, 2, 2, 0, "resids", "Vector of mean zero residuals"], [576, 2, 2, 0, "sigma2", "Array with same size as resids to store the conditional variance"], [576, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.EWMAVariance.forecast": [[578, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [578, 2, 2, 0, "horizon", "Forecast horizon.  Must be 1 or larger.  Forecasts are produced for horizons in [1, horizon]."], [578, 2, 2, 0, "method", "Method to use when producing the forecast. The default is analytic."], [578, 2, 2, 0, "parameters", "Parameters required to forecast the volatility model"], [578, 2, 2, 0, "random_state", "NumPy RandomState instance to use when method is 'bootstrap'"], [578, 2, 2, 0, "resids", "Residuals to use in the recursion"], [578, 2, 2, 0, "rng", "Callable random number generator required if method is 'simulation'. Must take a single shape input and return random samples numbers with that shape."], [578, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [578, 2, 2, 0, "start", "Index of the first observation to use as the starting point for the forecast.  Default is len(resids)."], [578, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.EWMAVariance.simulate": [[582, 2, 2, 0, "burn", "Number of additional observations to generate when initializing the simulation"], [582, 2, 2, 0, "initial_value", "Scalar or array of initial values to use when initializing the simulation"], [582, 2, 2, 0, "nobs", "Number of data points to simulate"], [582, 2, 2, 0, "parameters", "Parameters required to simulate the volatility model"], [582, 2, 2, 0, "rng", "Callable function that takes a single integer input and returns a vector of random numbers"]], "arch.univariate.EWMAVariance.starting_values": [[584, 2, 2, 0, "resids", "Array of (approximate) residuals to use when computing starting values"]], "arch.univariate.EWMAVariance.update": [[586, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [586, 2, 2, 0, "index", "The numerical index of the variance to compute"], [586, 2, 2, 0, "parameters", "The variance model parameters"], [586, 2, 2, 0, "resids", "The residual array. Only uses resids[:index] when computing sigma2[index]"], [586, 2, 2, 0, "sigma2", "The array containing the variances. Only uses sigma2[:index] when computing sigma2[index]. The computed value is stored in sigma2[index]."], [586, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.EWMAVariance.variance_bounds": [[588, 2, 2, 0, "power", "Power used in the model. 2.0, the default corresponds to standard ARCH models that evolve in squares."], [588, 2, 2, 0, "resids", "Approximate residuals to use to compute the lower and upper bounds on the conditional variance"]], "arch.univariate.FIGARCH.__init__": [[433, 2, 2, 0, "p", "Order of the symmetric innovation"], [433, 2, 2, 0, "power", "Power to use with the innovations, abs(e) ** power.  Default is 2.0, which produces FIGARCH and related models. Using 1.0 produces FIAVARCH and related models.  Other powers can be specified, although these should be strictly positive, and usually larger than 0.25."], [433, 2, 2, 0, "q", "Order of the lagged (transformed) conditional variance"], [433, 2, 2, 0, "truncation", "Truncation point to use in ARCH(\\infty) representation. Default is 1000."]], "arch.univariate.FIGARCH": [[590, 3, 1, 0, "backcast", "Construct values for backcasting to start the recursion"], [591, 3, 1, 0, "backcast_transform", "Transformation to apply to user-provided backcast values"], [592, 3, 1, 0, "bounds", "Returns bounds for parameters"], [593, 3, 1, 0, "compute_variance", "Compute the variance for the ARCH model"], [594, 3, 1, 0, "constraints", "Construct parameter constraints arrays for parameter estimation"], [595, 3, 1, 0, "forecast", "Forecast volatility from the model"], [596, 5, 1, 0, "name", "The name of the volatility process"], [597, 5, 1, 0, "num_params", "The number of parameters in the model"], [598, 3, 1, 0, "parameter_names", "Names of model parameters"], [599, 3, 1, 0, "simulate", "Simulate data from the model"], [600, 5, 1, 0, "start", "Index to use to start variance subarray selection"], [601, 3, 1, 0, "starting_values", "Returns starting values for the ARCH model"], [602, 5, 1, 0, "stop", "Index to use to stop variance subarray selection"], [603, 5, 1, 0, "truncation", "Truncation lag for the ARCH-infinity approximation"], [604, 3, 1, 0, "update", "Compute the variance for a single observation"], [605, 5, 1, 0, "updateable", "Flag indicating that the volatility process supports update"], [606, 3, 1, 0, "variance_bounds", "Construct loose bounds for conditional variances."], [607, 5, 1, 0, "volatility_updater", "Get the volatility updater associated with the volatility process"]], "arch.univariate.FIGARCH.backcast": [[590, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.FIGARCH.backcast_transform": [[591, 2, 2, 0, "backcast", "User-provided backcast that approximates sigma2[0]."]], "arch.univariate.FIGARCH.bounds": [[592, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.FIGARCH.compute_variance": [[593, 2, 2, 0, "backcast", "Value to use when initializing ARCH recursion. Can be an ndarray when the model contains multiple components."], [593, 2, 2, 0, "parameters", "Model parameters"], [593, 2, 2, 0, "resids", "Vector of mean zero residuals"], [593, 2, 2, 0, "sigma2", "Array with same size as resids to store the conditional variance"], [593, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.FIGARCH.forecast": [[595, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [595, 2, 2, 0, "horizon", "Forecast horizon.  Must be 1 or larger.  Forecasts are produced for horizons in [1, horizon]."], [595, 2, 2, 0, "method", "Method to use when producing the forecast. The default is analytic."], [595, 2, 2, 0, "parameters", "Parameters required to forecast the volatility model"], [595, 2, 2, 0, "random_state", "NumPy RandomState instance to use when method is 'bootstrap'"], [595, 2, 2, 0, "resids", "Residuals to use in the recursion"], [595, 2, 2, 0, "rng", "Callable random number generator required if method is 'simulation'. Must take a single shape input and return random samples numbers with that shape."], [595, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [595, 2, 2, 0, "start", "Index of the first observation to use as the starting point for the forecast.  Default is len(resids)."], [595, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.FIGARCH.simulate": [[599, 2, 2, 0, "burn", "Number of additional observations to generate when initializing the simulation"], [599, 2, 2, 0, "initial_value", "Scalar or array of initial values to use when initializing the simulation"], [599, 2, 2, 0, "nobs", "Number of data points to simulate"], [599, 2, 2, 0, "parameters", "Parameters required to simulate the volatility model"], [599, 2, 2, 0, "rng", "Callable function that takes a single integer input and returns a vector of random numbers"]], "arch.univariate.FIGARCH.starting_values": [[601, 2, 2, 0, "resids", "Array of (approximate) residuals to use when computing starting values"]], "arch.univariate.FIGARCH.update": [[604, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [604, 2, 2, 0, "index", "The numerical index of the variance to compute"], [604, 2, 2, 0, "parameters", "The variance model parameters"], [604, 2, 2, 0, "resids", "The residual array. Only uses resids[:index] when computing sigma2[index]"], [604, 2, 2, 0, "sigma2", "The array containing the variances. Only uses sigma2[:index] when computing sigma2[index]. The computed value is stored in sigma2[index]."], [604, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.FIGARCH.variance_bounds": [[606, 2, 2, 0, "power", "Power used in the model. 2.0, the default corresponds to standard ARCH models that evolve in squares."], [606, 2, 2, 0, "resids", "Approximate residuals to use to compute the lower and upper bounds on the conditional variance"]], "arch.univariate.FixedVariance.__init__": [[434, 2, 2, 0, "unit_scale", "Flag whether to enforce a unit scale.  If False, a scale parameter will be estimated so that the model variance will be proportional to variance. If True, the model variance is set of variance"], [434, 2, 2, 0, "variance", "Array containing the variances to use.  Should have the same shape as the data used in the model."]], "arch.univariate.FixedVariance": [[608, 3, 1, 0, "backcast", "Construct values for backcasting to start the recursion"], [609, 3, 1, 0, "backcast_transform", "Transformation to apply to user-provided backcast values"], [610, 3, 1, 0, "bounds", "Returns bounds for parameters"], [611, 3, 1, 0, "compute_variance", "Compute the variance for the ARCH model"], [612, 3, 1, 0, "constraints", "Construct parameter constraints arrays for parameter estimation"], [613, 3, 1, 0, "forecast", "Forecast volatility from the model"], [614, 5, 1, 0, "name", "The name of the volatility process"], [615, 5, 1, 0, "num_params", "The number of parameters in the model"], [616, 3, 1, 0, "parameter_names", "Names of model parameters"], [617, 3, 1, 0, "simulate", "Simulate data from the model"], [618, 5, 1, 0, "start", "Index to use to start variance subarray selection"], [619, 3, 1, 0, "starting_values", "Returns starting values for the ARCH model"], [620, 5, 1, 0, "stop", "Index to use to stop variance subarray selection"], [621, 3, 1, 0, "update", "Compute the variance for a single observation"], [622, 5, 1, 0, "updateable", "Flag indicating that the volatility process supports update"], [623, 3, 1, 0, "variance_bounds", "Construct loose bounds for conditional variances."], [624, 5, 1, 0, "volatility_updater", "Get the volatility updater associated with the volatility process"]], "arch.univariate.FixedVariance.backcast": [[608, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.FixedVariance.backcast_transform": [[609, 2, 2, 0, "backcast", "User-provided backcast that approximates sigma2[0]."]], "arch.univariate.FixedVariance.bounds": [[610, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.FixedVariance.compute_variance": [[611, 2, 2, 0, "backcast", "Value to use when initializing ARCH recursion. Can be an ndarray when the model contains multiple components."], [611, 2, 2, 0, "parameters", "Model parameters"], [611, 2, 2, 0, "resids", "Vector of mean zero residuals"], [611, 2, 2, 0, "sigma2", "Array with same size as resids to store the conditional variance"], [611, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.FixedVariance.forecast": [[613, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [613, 2, 2, 0, "horizon", "Forecast horizon.  Must be 1 or larger.  Forecasts are produced for horizons in [1, horizon]."], [613, 2, 2, 0, "method", "Method to use when producing the forecast. The default is analytic."], [613, 2, 2, 0, "parameters", "Parameters required to forecast the volatility model"], [613, 2, 2, 0, "random_state", "NumPy RandomState instance to use when method is 'bootstrap'"], [613, 2, 2, 0, "resids", "Residuals to use in the recursion"], [613, 2, 2, 0, "rng", "Callable random number generator required if method is 'simulation'. Must take a single shape input and return random samples numbers with that shape."], [613, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [613, 2, 2, 0, "start", "Index of the first observation to use as the starting point for the forecast.  Default is len(resids)."], [613, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.FixedVariance.simulate": [[617, 2, 2, 0, "burn", "Number of additional observations to generate when initializing the simulation"], [617, 2, 2, 0, "initial_value", "Scalar or array of initial values to use when initializing the simulation"], [617, 2, 2, 0, "nobs", "Number of data points to simulate"], [617, 2, 2, 0, "parameters", "Parameters required to simulate the volatility model"], [617, 2, 2, 0, "rng", "Callable function that takes a single integer input and returns a vector of random numbers"]], "arch.univariate.FixedVariance.starting_values": [[619, 2, 2, 0, "resids", "Array of (approximate) residuals to use when computing starting values"]], "arch.univariate.FixedVariance.update": [[621, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [621, 2, 2, 0, "index", "The numerical index of the variance to compute"], [621, 2, 2, 0, "parameters", "The variance model parameters"], [621, 2, 2, 0, "resids", "The residual array. Only uses resids[:index] when computing sigma2[index]"], [621, 2, 2, 0, "sigma2", "The array containing the variances. Only uses sigma2[:index] when computing sigma2[index]. The computed value is stored in sigma2[index]."], [621, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.FixedVariance.variance_bounds": [[623, 2, 2, 0, "power", "Power used in the model. 2.0, the default corresponds to standard ARCH models that evolve in squares."], [623, 2, 2, 0, "resids", "Approximate residuals to use to compute the lower and upper bounds on the conditional variance"]], "arch.univariate.GARCH.__init__": [[435, 2, 2, 0, "o", "Order of the asymmetric innovation"], [435, 2, 2, 0, "p", "Order of the symmetric innovation"], [435, 2, 2, 0, "power", "Power to use with the innovations, abs(e) ** power.  Default is 2.0, which produces ARCH and related models. Using 1.0 produces AVARCH and related models.  Other powers can be specified, although these should be strictly positive, and usually larger than 0.25."], [435, 2, 2, 0, "q", "Order of the lagged (transformed) conditional variance"]], "arch.univariate.GARCH": [[625, 3, 1, 0, "backcast", "Construct values for backcasting to start the recursion"], [626, 3, 1, 0, "backcast_transform", "Transformation to apply to user-provided backcast values"], [627, 3, 1, 0, "bounds", "Returns bounds for parameters"], [628, 3, 1, 0, "compute_variance", "Compute the variance for the ARCH model"], [629, 3, 1, 0, "constraints", "Construct parameter constraints arrays for parameter estimation"], [630, 3, 1, 0, "forecast", "Forecast volatility from the model"], [631, 5, 1, 0, "name", "The name of the volatility process"], [632, 5, 1, 0, "num_params", "The number of parameters in the model"], [633, 3, 1, 0, "parameter_names", "Names of model parameters"], [634, 3, 1, 0, "simulate", "Simulate data from the model"], [635, 5, 1, 0, "start", "Index to use to start variance subarray selection"], [636, 3, 1, 0, "starting_values", "Returns starting values for the ARCH model"], [637, 5, 1, 0, "stop", "Index to use to stop variance subarray selection"], [638, 3, 1, 0, "update", "Compute the variance for a single observation"], [639, 5, 1, 0, "updateable", "Flag indicating that the volatility process supports update"], [640, 3, 1, 0, "variance_bounds", "Construct loose bounds for conditional variances."], [641, 5, 1, 0, "volatility_updater", "Get the volatility updater associated with the volatility process"]], "arch.univariate.GARCH.backcast": [[625, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.GARCH.backcast_transform": [[626, 2, 2, 0, "backcast", "User-provided backcast that approximates sigma2[0]."]], "arch.univariate.GARCH.bounds": [[627, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.GARCH.compute_variance": [[628, 2, 2, 0, "backcast", "Value to use when initializing ARCH recursion. Can be an ndarray when the model contains multiple components."], [628, 2, 2, 0, "parameters", "Model parameters"], [628, 2, 2, 0, "resids", "Vector of mean zero residuals"], [628, 2, 2, 0, "sigma2", "Array with same size as resids to store the conditional variance"], [628, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.GARCH.forecast": [[630, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [630, 2, 2, 0, "horizon", "Forecast horizon.  Must be 1 or larger.  Forecasts are produced for horizons in [1, horizon]."], [630, 2, 2, 0, "method", "Method to use when producing the forecast. The default is analytic."], [630, 2, 2, 0, "parameters", "Parameters required to forecast the volatility model"], [630, 2, 2, 0, "random_state", "NumPy RandomState instance to use when method is 'bootstrap'"], [630, 2, 2, 0, "resids", "Residuals to use in the recursion"], [630, 2, 2, 0, "rng", "Callable random number generator required if method is 'simulation'. Must take a single shape input and return random samples numbers with that shape."], [630, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [630, 2, 2, 0, "start", "Index of the first observation to use as the starting point for the forecast.  Default is len(resids)."], [630, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.GARCH.simulate": [[634, 2, 2, 0, "burn", "Number of additional observations to generate when initializing the simulation"], [634, 2, 2, 0, "initial_value", "Scalar or array of initial values to use when initializing the simulation"], [634, 2, 2, 0, "nobs", "Number of data points to simulate"], [634, 2, 2, 0, "parameters", "Parameters required to simulate the volatility model"], [634, 2, 2, 0, "rng", "Callable function that takes a single integer input and returns a vector of random numbers"]], "arch.univariate.GARCH.starting_values": [[636, 2, 2, 0, "resids", "Array of (approximate) residuals to use when computing starting values"]], "arch.univariate.GARCH.update": [[638, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [638, 2, 2, 0, "index", "The numerical index of the variance to compute"], [638, 2, 2, 0, "parameters", "The variance model parameters"], [638, 2, 2, 0, "resids", "The residual array. Only uses resids[:index] when computing sigma2[index]"], [638, 2, 2, 0, "sigma2", "The array containing the variances. Only uses sigma2[:index] when computing sigma2[index]. The computed value is stored in sigma2[index]."], [638, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.GARCH.variance_bounds": [[640, 2, 2, 0, "power", "Power used in the model. 2.0, the default corresponds to standard ARCH models that evolve in squares."], [640, 2, 2, 0, "resids", "Approximate residuals to use to compute the lower and upper bounds on the conditional variance"]], "arch.univariate.GeneralizedError.__init__": [[436, 2, 2, 0, "random_state", "Deprecated since version 5.0: random_state is deprecated. Use seed instead."], [436, 2, 2, 0, "seed", "Random number generator instance or int to use. Set to ensure reproducibility. If using an int, the argument is passed to np.random.default_rng.  If not provided, default_rng is used with system-provided entropy."]], "arch.univariate.GeneralizedError": [[642, 3, 1, 0, "bounds", "Parameter bounds for use in optimization."], [643, 3, 1, 0, "cdf", "Cumulative distribution function"], [644, 3, 1, 0, "constraints", "Construct arrays to use in constrained optimization."], [645, 5, 1, 0, "generator", "The NumPy Generator or RandomState attached to the distribution"], [646, 3, 1, 0, "loglikelihood", "Computes the log-likelihood of assuming residuals are have a Generalized Error Distribution, conditional on the variance."], [647, 3, 1, 0, "moment", "Moment of order n"], [648, 5, 1, 0, "name", "The name of the distribution"], [649, 3, 1, 0, "parameter_names", "Names of distribution shape parameters"], [650, 3, 1, 0, "partial_moment", "Order n lower partial moment from -inf to z"], [651, 3, 1, 0, "ppf", "Inverse cumulative density function (ICDF)"], [652, 5, 1, 0, "random_state", "The NumPy RandomState attached to the distribution"], [653, 3, 1, 0, "simulate", "Simulates i.i.d. draws from the distribution"], [654, 3, 1, 0, "starting_values", "Construct starting values for use in optimization."]], "arch.univariate.GeneralizedError.bounds": [[642, 2, 2, 0, "resids", "Residuals to use when computing the bounds"]], "arch.univariate.GeneralizedError.cdf": [[643, 2, 2, 0, "parameters", "Distribution parameters. Use None for parameterless distributions."], [643, 2, 2, 0, "resids", "Values at which to evaluate the cdf"]], "arch.univariate.GeneralizedError.loglikelihood": [[646, 2, 2, 0, "individual", "Flag indicating whether to return the vector of individual log likelihoods (True) or the sum (False)"], [646, 2, 2, 0, "parameters", "Shape parameter of the GED distribution"], [646, 2, 2, 0, "resids", "The residuals to use in the log-likelihood calculation"], [646, 2, 2, 0, "sigma2", "Conditional variances of resids"]], "arch.univariate.GeneralizedError.moment": [[647, 2, 2, 0, "n", "Order of moment"], [647, 2, 2, 0, "parameters", "Distribution parameters. Use None for parameterless distributions."]], "arch.univariate.GeneralizedError.partial_moment": [[650, 2, 2, 0, "n", "Order of partial moment"], [650, 2, 2, 0, "parameters", "Distribution parameters.  Use None for parameterless distributions."], [650, 2, 2, 0, "z", "Upper bound for partial moment integral"]], "arch.univariate.GeneralizedError.ppf": [[651, 2, 2, 0, "parameters", "Distribution parameters. Use None for parameterless distributions."], [651, 2, 2, 0, "pits", "Probability-integral-transformed values in the interval (0, 1)."]], "arch.univariate.GeneralizedError.simulate": [[653, 2, 2, 0, "parameters", "Distribution parameters"]], "arch.univariate.GeneralizedError.starting_values": [[654, 2, 2, 0, "std_resid", "Estimated standardized residuals to use in computing starting values for the shape parameter"]], "arch.univariate.HARCH.__init__": [[437, 2, 2, 0, "lags", "List of lags to include in the model, or if scalar, includes all lags up the value"]], "arch.univariate.HARCH": [[655, 3, 1, 0, "backcast", "Construct values for backcasting to start the recursion"], [656, 3, 1, 0, "backcast_transform", "Transformation to apply to user-provided backcast values"], [657, 3, 1, 0, "bounds", "Returns bounds for parameters"], [658, 3, 1, 0, "compute_variance", "Compute the variance for the ARCH model"], [659, 3, 1, 0, "constraints", "Construct parameter constraints arrays for parameter estimation"], [660, 3, 1, 0, "forecast", "Forecast volatility from the model"], [661, 5, 1, 0, "name", "The name of the volatility process"], [662, 5, 1, 0, "num_params", "The number of parameters in the model"], [663, 3, 1, 0, "parameter_names", "Names of model parameters"], [664, 3, 1, 0, "simulate", "Simulate data from the model"], [665, 5, 1, 0, "start", "Index to use to start variance subarray selection"], [666, 3, 1, 0, "starting_values", "Returns starting values for the ARCH model"], [667, 5, 1, 0, "stop", "Index to use to stop variance subarray selection"], [668, 3, 1, 0, "update", "Compute the variance for a single observation"], [669, 5, 1, 0, "updateable", "Flag indicating that the volatility process supports update"], [670, 3, 1, 0, "variance_bounds", "Construct loose bounds for conditional variances."], [671, 5, 1, 0, "volatility_updater", "Get the volatility updater associated with the volatility process"]], "arch.univariate.HARCH.backcast": [[655, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.HARCH.backcast_transform": [[656, 2, 2, 0, "backcast", "User-provided backcast that approximates sigma2[0]."]], "arch.univariate.HARCH.bounds": [[657, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.HARCH.compute_variance": [[658, 2, 2, 0, "backcast", "Value to use when initializing ARCH recursion. Can be an ndarray when the model contains multiple components."], [658, 2, 2, 0, "parameters", "Model parameters"], [658, 2, 2, 0, "resids", "Vector of mean zero residuals"], [658, 2, 2, 0, "sigma2", "Array with same size as resids to store the conditional variance"], [658, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.HARCH.forecast": [[660, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [660, 2, 2, 0, "horizon", "Forecast horizon.  Must be 1 or larger.  Forecasts are produced for horizons in [1, horizon]."], [660, 2, 2, 0, "method", "Method to use when producing the forecast. The default is analytic."], [660, 2, 2, 0, "parameters", "Parameters required to forecast the volatility model"], [660, 2, 2, 0, "random_state", "NumPy RandomState instance to use when method is 'bootstrap'"], [660, 2, 2, 0, "resids", "Residuals to use in the recursion"], [660, 2, 2, 0, "rng", "Callable random number generator required if method is 'simulation'. Must take a single shape input and return random samples numbers with that shape."], [660, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [660, 2, 2, 0, "start", "Index of the first observation to use as the starting point for the forecast.  Default is len(resids)."], [660, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.HARCH.simulate": [[664, 2, 2, 0, "burn", "Number of additional observations to generate when initializing the simulation"], [664, 2, 2, 0, "initial_value", "Scalar or array of initial values to use when initializing the simulation"], [664, 2, 2, 0, "nobs", "Number of data points to simulate"], [664, 2, 2, 0, "parameters", "Parameters required to simulate the volatility model"], [664, 2, 2, 0, "rng", "Callable function that takes a single integer input and returns a vector of random numbers"]], "arch.univariate.HARCH.starting_values": [[666, 2, 2, 0, "resids", "Array of (approximate) residuals to use when computing starting values"]], "arch.univariate.HARCH.update": [[668, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [668, 2, 2, 0, "index", "The numerical index of the variance to compute"], [668, 2, 2, 0, "parameters", "The variance model parameters"], [668, 2, 2, 0, "resids", "The residual array. Only uses resids[:index] when computing sigma2[index]"], [668, 2, 2, 0, "sigma2", "The array containing the variances. Only uses sigma2[:index] when computing sigma2[index]. The computed value is stored in sigma2[index]."], [668, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.HARCH.variance_bounds": [[670, 2, 2, 0, "power", "Power used in the model. 2.0, the default corresponds to standard ARCH models that evolve in squares."], [670, 2, 2, 0, "resids", "Approximate residuals to use to compute the lower and upper bounds on the conditional variance"]], "arch.univariate.HARX.__init__": [[438, 2, 2, 0, "constant", "Flag whether the model should include a constant"], [438, 2, 2, 0, "distribution", "Error distribution to use in the model"], [438, 2, 2, 0, "hold_back", "Number of observations at the start of the sample to exclude when estimating model parameters.  Used when comparing models with different lag lengths to estimate on the common sample."], [438, 2, 2, 0, "lags", "Description of lag structure of the HAR."], [438, 2, 2, 0, "rescale", "Flag indicating whether to automatically rescale data if the scale of the data is likely to produce convergence issues when estimating model parameters. If False, the model is estimated on the data without transformation.  If True, than y is rescaled and the new scale is reported in the estimation results."], [438, 2, 2, 0, "use_rotated", "Flag indicating to use the alternative rotated form of the HAR where HAR lags do not overlap"], [438, 2, 2, 0, "volatility", "Volatility process to use in the model"], [438, 2, 2, 0, "x", "nobs by k element array containing exogenous regressors"], [438, 2, 2, 0, "y", "nobs element vector containing the dependent variable"]], "arch.univariate.HARX": [[672, 3, 1, 0, "bounds", "Construct bounds for parameters to use in non-linear optimization"], [673, 3, 1, 0, "compute_param_cov", "Computes parameter covariances using numerical derivatives."], [674, 3, 1, 0, "constraints", "Construct linear constraint arrays  for use in non-linear optimization"], [675, 5, 1, 0, "distribution", "Set or gets the error distribution"], [676, 3, 1, 0, "fit", "Estimate model parameters"], [677, 3, 1, 0, "fix", "Allows an ARCHModelFixedResult to be constructed from fixed parameters."], [678, 3, 1, 0, "forecast", "Construct forecasts from estimated model"], [679, 5, 1, 0, "name", "The name of the model."], [680, 5, 1, 0, "num_params", "Returns the number of parameters"], [681, 3, 1, 0, "parameter_names", "List of parameters names"], [682, 3, 1, 0, "resids", "Compute model residuals"], [683, 3, 1, 0, "simulate", "Simulates data from a linear regression, AR or HAR models"], [684, 3, 1, 0, "starting_values", "Returns starting values for the mean model, often the same as the values returned from fit"], [685, 5, 1, 0, "volatility", "Set or gets the volatility process"], [686, 5, 1, 0, "x", "Gets the value of the exogenous regressors in the model"], [687, 5, 1, 0, "y", "Returns the dependent variable"]], "arch.univariate.HARX.compute_param_cov": [[673, 2, 2, 0, "backcast", "Value to use for pre-sample observations"], [673, 2, 2, 0, "params", "Model parameters"], [673, 2, 2, 0, "robust", "Flag indicating whether to use robust standard errors (True) or classic MLE (False)"]], "arch.univariate.HARX.fit": [[676, 2, 2, 0, "backcast", "Value to use as backcast. Should be measure \\sigma^2_0 since model-specific non-linear transformations are applied to value before computing the variance recursions."], [676, 2, 2, 0, "cov_type", "Estimation method of parameter covariance.  Supported options are 'robust', which does not assume the Information Matrix Equality holds and 'classic' which does.  In the ARCH literature, 'robust' corresponds to Bollerslev-Wooldridge covariance estimator."], [676, 2, 2, 0, "disp", "Either 'final' to print optimization result or 'off' to display nothing. If using a boolean, False is \"off\" and True is \"final\""], [676, 2, 2, 0, "first_obs", "First observation to use when estimating model"], [676, 2, 2, 0, "last_obs", "Last observation to use when estimating model"], [676, 2, 2, 0, "options", "Options to pass to scipy.optimize.minimize.  Valid entries include 'ftol', 'eps', 'disp', and 'maxiter'."], [676, 2, 2, 0, "show_warning", "Flag indicating whether convergence warnings should be shown."], [676, 2, 2, 0, "starting_values", "Array of starting values to use.  If not provided, starting values are constructed by the model components."], [676, 2, 2, 0, "tol", "Tolerance for termination."], [676, 2, 2, 0, "update_freq", "Frequency of iteration updates.  Output is generated every update_freq iterations. Set to 0 to disable iterative output."]], "arch.univariate.HARX.fix": [[677, 2, 2, 0, "first_obs", "First observation to use when fixing model"], [677, 2, 2, 0, "last_obs", "Last observation to use when fixing model"], [677, 2, 2, 0, "params", "User specified parameters to use when generating the result. Must have the correct number of parameters for a given choice of mean model, volatility model and distribution."]], "arch.univariate.HARX.forecast": [[678, 2, 2, 0, "align", "Either 'origin' or 'target'.  When set of 'origin', the t-th row of forecasts contains the forecasts for t+1, t+2, ..., t+h. When set to 'target', the t-th row contains the 1-step ahead forecast from time t-1, the 2 step from time t-2, ..., and the h-step from time t-h.  'target' simplified computing forecast errors since the realization and h-step forecast are aligned."], [678, 2, 2, 0, "horizon", "Number of steps to forecast"], [678, 2, 2, 0, "method", "Method to use when producing the forecast. The default is analytic. The method only affects the variance forecast generation.  Not all volatility models support all methods. In particular, volatility models that do not evolve in squares such as EGARCH or TARCH do not support the 'analytic' method for horizons > 1."], [678, 2, 2, 0, "params", "Parameters required to forecast. Must be identical in shape to the parameters computed by fitting the model."], [678, 2, 2, 0, "random_state", "NumPy RandomState instance to use when method is 'bootstrap'"], [678, 2, 2, 0, "reindex", "Whether to reindex the forecasts to have the same dimension as the series being forecast. Prior to 4.18 this was the default. As of 4.19 this is now optional. If not provided, a warning is raised about the future change in the default which will occur after September 2021."], [678, 2, 2, 0, "rng", "Custom random number generator to use in simulation-based forecasts. Must produce random samples using the syntax rng(size) where size the 2-element tuple (simulations, horizon)."], [678, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [678, 2, 2, 0, "start", "An integer, datetime or str indicating the first observation to produce the forecast for.  Datetimes can only be used with pandas inputs that have a datetime index. Strings must be convertible to a date time, such as in '1945-01-01'."], [678, 2, 2, 0, "x", "Values to use for exogenous regressors if any are included in the model. Three formats are accepted:"]], "arch.univariate.HARX.resids": [[682, 2, 2, 0, "params", "Model parameters"], [682, 2, 2, 0, "regressors", "Alternative regressor values to use when computing model residuals"], [682, 2, 2, 0, "y", "Alternative values to use when computing model residuals"]], "arch.univariate.HARX.simulate": [[683, 2, 2, 0, "burn", "Number of values to simulate to initialize the model and remove dependence on initial values."], [683, 2, 2, 0, "initial_value", "Either a scalar value or max(lags) array set of initial values to use when initializing the model.  If omitted, 0.0 is used."], [683, 2, 2, 0, "initial_value_vol", "An array or scalar to use when initializing the volatility process."], [683, 2, 2, 0, "nobs", "Length of series to simulate"], [683, 2, 2, 0, "params", "Parameters to use when simulating the model.  Parameter order is [mean volatility distribution] where the parameters of the mean model are ordered [constant lag[0] lag[1] ... lag[p] ex[0] ... ex[k-1]] where lag[j] indicates the coefficient on the jth lag in the model and ex[j] is the coefficient on the jth exogenous variable."], [683, 2, 2, 0, "x", "nobs + burn by k array of exogenous variables to include in the simulation."]], "arch.univariate.LS.__init__": [[439, 2, 2, 0, "constant", "Flag whether the model should include a constant"], [439, 2, 2, 0, "distribution", "Error distribution to use in the model"], [439, 2, 2, 0, "hold_back", "Number of observations at the start of the sample to exclude when estimating model parameters.  Used when comparing models with different lag lengths to estimate on the common sample."], [439, 2, 2, 0, "rescale", "Flag indicating whether to automatically rescale data if the scale of the data is likely to produce convergence issues when estimating model parameters. If False, the model is estimated on the data without transformation.  If True, than y is rescaled and the new scale is reported in the estimation results."], [439, 2, 2, 0, "volatility", "Volatility process to use in the model"], [439, 2, 2, 0, "y", "nobs by k element array containing exogenous regressors"]], "arch.univariate.LS": [[688, 3, 1, 0, "bounds", "Construct bounds for parameters to use in non-linear optimization"], [689, 3, 1, 0, "compute_param_cov", "Computes parameter covariances using numerical derivatives."], [690, 3, 1, 0, "constraints", "Construct linear constraint arrays  for use in non-linear optimization"], [691, 5, 1, 0, "distribution", "Set or gets the error distribution"], [692, 3, 1, 0, "fit", "Estimate model parameters"], [693, 3, 1, 0, "fix", "Allows an ARCHModelFixedResult to be constructed from fixed parameters."], [694, 3, 1, 0, "forecast", "Construct forecasts from estimated model"], [695, 5, 1, 0, "name", "The name of the model."], [696, 5, 1, 0, "num_params", "Returns the number of parameters"], [697, 3, 1, 0, "parameter_names", "List of parameters names"], [698, 3, 1, 0, "resids", "Compute model residuals"], [699, 3, 1, 0, "simulate", "Simulates data from a linear regression, AR or HAR models"], [700, 3, 1, 0, "starting_values", "Returns starting values for the mean model, often the same as the values returned from fit"], [701, 5, 1, 0, "volatility", "Set or gets the volatility process"], [702, 5, 1, 0, "x", "Gets the value of the exogenous regressors in the model"], [703, 5, 1, 0, "y", "Returns the dependent variable"]], "arch.univariate.LS.compute_param_cov": [[689, 2, 2, 0, "backcast", "Value to use for pre-sample observations"], [689, 2, 2, 0, "params", "Model parameters"], [689, 2, 2, 0, "robust", "Flag indicating whether to use robust standard errors (True) or classic MLE (False)"]], "arch.univariate.LS.fit": [[692, 2, 2, 0, "backcast", "Value to use as backcast. Should be measure \\sigma^2_0 since model-specific non-linear transformations are applied to value before computing the variance recursions."], [692, 2, 2, 0, "cov_type", "Estimation method of parameter covariance.  Supported options are 'robust', which does not assume the Information Matrix Equality holds and 'classic' which does.  In the ARCH literature, 'robust' corresponds to Bollerslev-Wooldridge covariance estimator."], [692, 2, 2, 0, "disp", "Either 'final' to print optimization result or 'off' to display nothing. If using a boolean, False is \"off\" and True is \"final\""], [692, 2, 2, 0, "first_obs", "First observation to use when estimating model"], [692, 2, 2, 0, "last_obs", "Last observation to use when estimating model"], [692, 2, 2, 0, "options", "Options to pass to scipy.optimize.minimize.  Valid entries include 'ftol', 'eps', 'disp', and 'maxiter'."], [692, 2, 2, 0, "show_warning", "Flag indicating whether convergence warnings should be shown."], [692, 2, 2, 0, "starting_values", "Array of starting values to use.  If not provided, starting values are constructed by the model components."], [692, 2, 2, 0, "tol", "Tolerance for termination."], [692, 2, 2, 0, "update_freq", "Frequency of iteration updates.  Output is generated every update_freq iterations. Set to 0 to disable iterative output."]], "arch.univariate.LS.fix": [[693, 2, 2, 0, "first_obs", "First observation to use when fixing model"], [693, 2, 2, 0, "last_obs", "Last observation to use when fixing model"], [693, 2, 2, 0, "params", "User specified parameters to use when generating the result. Must have the correct number of parameters for a given choice of mean model, volatility model and distribution."]], "arch.univariate.LS.forecast": [[694, 2, 2, 0, "align", "Either 'origin' or 'target'.  When set of 'origin', the t-th row of forecasts contains the forecasts for t+1, t+2, ..., t+h. When set to 'target', the t-th row contains the 1-step ahead forecast from time t-1, the 2 step from time t-2, ..., and the h-step from time t-h.  'target' simplified computing forecast errors since the realization and h-step forecast are aligned."], [694, 2, 2, 0, "horizon", "Number of steps to forecast"], [694, 2, 2, 0, "method", "Method to use when producing the forecast. The default is analytic. The method only affects the variance forecast generation.  Not all volatility models support all methods. In particular, volatility models that do not evolve in squares such as EGARCH or TARCH do not support the 'analytic' method for horizons > 1."], [694, 2, 2, 0, "params", "Parameters required to forecast. Must be identical in shape to the parameters computed by fitting the model."], [694, 2, 2, 0, "random_state", "NumPy RandomState instance to use when method is 'bootstrap'"], [694, 2, 2, 0, "reindex", "Whether to reindex the forecasts to have the same dimension as the series being forecast. Prior to 4.18 this was the default. As of 4.19 this is now optional. If not provided, a warning is raised about the future change in the default which will occur after September 2021."], [694, 2, 2, 0, "rng", "Custom random number generator to use in simulation-based forecasts. Must produce random samples using the syntax rng(size) where size the 2-element tuple (simulations, horizon)."], [694, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [694, 2, 2, 0, "start", "An integer, datetime or str indicating the first observation to produce the forecast for.  Datetimes can only be used with pandas inputs that have a datetime index. Strings must be convertible to a date time, such as in '1945-01-01'."], [694, 2, 2, 0, "x", "Values to use for exogenous regressors if any are included in the model. Three formats are accepted:"]], "arch.univariate.LS.resids": [[698, 2, 2, 0, "params", "Model parameters"], [698, 2, 2, 0, "regressors", "Alternative regressor values to use when computing model residuals"], [698, 2, 2, 0, "y", "Alternative values to use when computing model residuals"]], "arch.univariate.LS.simulate": [[699, 2, 2, 0, "burn", "Number of values to simulate to initialize the model and remove dependence on initial values."], [699, 2, 2, 0, "initial_value", "Either a scalar value or max(lags) array set of initial values to use when initializing the model.  If omitted, 0.0 is used."], [699, 2, 2, 0, "initial_value_vol", "An array or scalar to use when initializing the volatility process."], [699, 2, 2, 0, "nobs", "Length of series to simulate"], [699, 2, 2, 0, "params", "Parameters to use when simulating the model.  Parameter order is [mean volatility distribution] where the parameters of the mean model are ordered [constant lag[0] lag[1] ... lag[p] ex[0] ... ex[k-1]] where lag[j] indicates the coefficient on the jth lag in the model and ex[j] is the coefficient on the jth exogenous variable."], [699, 2, 2, 0, "x", "nobs + burn by k array of exogenous variables to include in the simulation."]], "arch.univariate.MIDASHyperbolic.__init__": [[440, 2, 2, 0, "asym", "Flag indicating whether to include an asymmetric term"], [440, 2, 2, 0, "m", "Length of maximum lag to include in the model"]], "arch.univariate.MIDASHyperbolic": [[704, 3, 1, 0, "backcast", "Construct values for backcasting to start the recursion"], [705, 3, 1, 0, "backcast_transform", "Transformation to apply to user-provided backcast values"], [706, 3, 1, 0, "bounds", "Returns bounds for parameters"], [707, 3, 1, 0, "compute_variance", "Compute the variance for the ARCH model"], [708, 3, 1, 0, "constraints", "Constraints"], [709, 3, 1, 0, "forecast", "Forecast volatility from the model"], [710, 5, 1, 0, "name", "The name of the volatility process"], [711, 5, 1, 0, "num_params", "The number of parameters in the model"], [712, 3, 1, 0, "parameter_names", "Names of model parameters"], [713, 3, 1, 0, "simulate", "Simulate data from the model"], [714, 5, 1, 0, "start", "Index to use to start variance subarray selection"], [715, 3, 1, 0, "starting_values", "Returns starting values for the ARCH model"], [716, 5, 1, 0, "stop", "Index to use to stop variance subarray selection"], [717, 3, 1, 0, "update", "Compute the variance for a single observation"], [718, 5, 1, 0, "updateable", "Flag indicating that the volatility process supports update"], [719, 3, 1, 0, "variance_bounds", "Construct loose bounds for conditional variances."], [720, 5, 1, 0, "volatility_updater", "Get the volatility updater associated with the volatility process"]], "arch.univariate.MIDASHyperbolic.backcast": [[704, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.MIDASHyperbolic.backcast_transform": [[705, 2, 2, 0, "backcast", "User-provided backcast that approximates sigma2[0]."]], "arch.univariate.MIDASHyperbolic.bounds": [[706, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.MIDASHyperbolic.compute_variance": [[707, 2, 2, 0, "backcast", "Value to use when initializing ARCH recursion. Can be an ndarray when the model contains multiple components."], [707, 2, 2, 0, "parameters", "Model parameters"], [707, 2, 2, 0, "resids", "Vector of mean zero residuals"], [707, 2, 2, 0, "sigma2", "Array with same size as resids to store the conditional variance"], [707, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.MIDASHyperbolic.forecast": [[709, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [709, 2, 2, 0, "horizon", "Forecast horizon.  Must be 1 or larger.  Forecasts are produced for horizons in [1, horizon]."], [709, 2, 2, 0, "method", "Method to use when producing the forecast. The default is analytic."], [709, 2, 2, 0, "parameters", "Parameters required to forecast the volatility model"], [709, 2, 2, 0, "random_state", "NumPy RandomState instance to use when method is 'bootstrap'"], [709, 2, 2, 0, "resids", "Residuals to use in the recursion"], [709, 2, 2, 0, "rng", "Callable random number generator required if method is 'simulation'. Must take a single shape input and return random samples numbers with that shape."], [709, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [709, 2, 2, 0, "start", "Index of the first observation to use as the starting point for the forecast.  Default is len(resids)."], [709, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.MIDASHyperbolic.simulate": [[713, 2, 2, 0, "burn", "Number of additional observations to generate when initializing the simulation"], [713, 2, 2, 0, "initial_value", "Scalar or array of initial values to use when initializing the simulation"], [713, 2, 2, 0, "nobs", "Number of data points to simulate"], [713, 2, 2, 0, "parameters", "Parameters required to simulate the volatility model"], [713, 2, 2, 0, "rng", "Callable function that takes a single integer input and returns a vector of random numbers"]], "arch.univariate.MIDASHyperbolic.starting_values": [[715, 2, 2, 0, "resids", "Array of (approximate) residuals to use when computing starting values"]], "arch.univariate.MIDASHyperbolic.update": [[717, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [717, 2, 2, 0, "index", "The numerical index of the variance to compute"], [717, 2, 2, 0, "parameters", "The variance model parameters"], [717, 2, 2, 0, "resids", "The residual array. Only uses resids[:index] when computing sigma2[index]"], [717, 2, 2, 0, "sigma2", "The array containing the variances. Only uses sigma2[:index] when computing sigma2[index]. The computed value is stored in sigma2[index]."], [717, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.MIDASHyperbolic.variance_bounds": [[719, 2, 2, 0, "power", "Power used in the model. 2.0, the default corresponds to standard ARCH models that evolve in squares."], [719, 2, 2, 0, "resids", "Approximate residuals to use to compute the lower and upper bounds on the conditional variance"]], "arch.univariate.Normal.__init__": [[441, 2, 2, 0, "random_state", "Deprecated since version 5.0: random_state is deprecated. Use seed instead."], [441, 2, 2, 0, "seed", "Random number generator instance or int to use. Set to ensure reproducibility. If using an int, the argument is passed to np.random.default_rng.  If not provided, default_rng is used with system-provided entropy."]], "arch.univariate.Normal": [[721, 3, 1, 0, "bounds", "Parameter bounds for use in optimization."], [722, 3, 1, 0, "cdf", "Cumulative distribution function"], [723, 3, 1, 0, "constraints", "Construct arrays to use in constrained optimization."], [724, 5, 1, 0, "generator", "The NumPy Generator or RandomState attached to the distribution"], [725, 3, 1, 0, "loglikelihood", "Computes the log-likelihood of assuming residuals are normally distributed, conditional on the variance"], [726, 3, 1, 0, "moment", "Moment of order n"], [727, 5, 1, 0, "name", "The name of the distribution"], [728, 3, 1, 0, "parameter_names", "Names of distribution shape parameters"], [729, 3, 1, 0, "partial_moment", "Order n lower partial moment from -inf to z"], [730, 3, 1, 0, "ppf", "Inverse cumulative density function (ICDF)"], [731, 5, 1, 0, "random_state", "The NumPy RandomState attached to the distribution"], [732, 3, 1, 0, "simulate", "Simulates i.i.d. draws from the distribution"], [733, 3, 1, 0, "starting_values", "Construct starting values for use in optimization."]], "arch.univariate.Normal.bounds": [[721, 2, 2, 0, "resids", "Residuals to use when computing the bounds"]], "arch.univariate.Normal.cdf": [[722, 2, 2, 0, "parameters", "Distribution parameters. Use None for parameterless distributions."], [722, 2, 2, 0, "resids", "Values at which to evaluate the cdf"]], "arch.univariate.Normal.loglikelihood": [[725, 2, 2, 0, "individual", "Flag indicating whether to return the vector of individual log likelihoods (True) or the sum (False)"], [725, 2, 2, 0, "parameters", "The normal likelihood has no shape parameters. Empty since the standard normal has no shape parameters."], [725, 2, 2, 0, "resids", "The residuals to use in the log-likelihood calculation"], [725, 2, 2, 0, "sigma2", "Conditional variances of resids"]], "arch.univariate.Normal.moment": [[726, 2, 2, 0, "n", "Order of moment"], [726, 2, 2, 0, "parameters", "Distribution parameters. Use None for parameterless distributions."]], "arch.univariate.Normal.partial_moment": [[729, 2, 2, 0, "n", "Order of partial moment"], [729, 2, 2, 0, "parameters", "Distribution parameters.  Use None for parameterless distributions."], [729, 2, 2, 0, "z", "Upper bound for partial moment integral"]], "arch.univariate.Normal.ppf": [[730, 2, 2, 0, "parameters", "Distribution parameters. Use None for parameterless distributions."], [730, 2, 2, 0, "pits", "Probability-integral-transformed values in the interval (0, 1)."]], "arch.univariate.Normal.simulate": [[732, 2, 2, 0, "parameters", "Distribution parameters"]], "arch.univariate.Normal.starting_values": [[733, 2, 2, 0, "std_resid", "Estimated standardized residuals to use in computing starting values for the shape parameter"]], "arch.univariate.RiskMetrics2006.__init__": [[442, 2, 2, 0, "kmax", "Number of components. Default is 14."], [442, 2, 2, 0, "rho", "Relative scale of adjacent cycles. Default is sqrt(2)"], [442, 2, 2, 0, "tau0", "Length of long cycle. Default is 1560."], [442, 2, 2, 0, "tau1", "Length of short cycle. Default is 4."]], "arch.univariate.RiskMetrics2006": [[734, 3, 1, 0, "backcast", "Construct values for backcasting to start the recursion"], [735, 3, 1, 0, "backcast_transform", "Transformation to apply to user-provided backcast values"], [736, 3, 1, 0, "bounds", "Returns bounds for parameters"], [737, 3, 1, 0, "compute_variance", "Compute the variance for the ARCH model"], [738, 3, 1, 0, "constraints", "Construct parameter constraints arrays for parameter estimation"], [739, 3, 1, 0, "forecast", "Forecast volatility from the model"], [740, 5, 1, 0, "name", "The name of the volatility process"], [741, 5, 1, 0, "num_params", "The number of parameters in the model"], [742, 3, 1, 0, "parameter_names", "Names of model parameters"], [743, 3, 1, 0, "simulate", "Simulate data from the model"], [744, 5, 1, 0, "start", "Index to use to start variance subarray selection"], [745, 3, 1, 0, "starting_values", "Returns starting values for the ARCH model"], [746, 5, 1, 0, "stop", "Index to use to stop variance subarray selection"], [747, 3, 1, 0, "update", "Compute the variance for a single observation"], [748, 5, 1, 0, "updateable", "Flag indicating that the volatility process supports update"], [749, 3, 1, 0, "variance_bounds", "Construct loose bounds for conditional variances."], [750, 5, 1, 0, "volatility_updater", "Get the volatility updater associated with the volatility process"]], "arch.univariate.RiskMetrics2006.backcast": [[734, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.RiskMetrics2006.backcast_transform": [[735, 2, 2, 0, "backcast", "User-provided backcast that approximates sigma2[0]."]], "arch.univariate.RiskMetrics2006.bounds": [[736, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.RiskMetrics2006.compute_variance": [[737, 2, 2, 0, "backcast", "Value to use when initializing ARCH recursion. Can be an ndarray when the model contains multiple components."], [737, 2, 2, 0, "parameters", "Model parameters"], [737, 2, 2, 0, "resids", "Vector of mean zero residuals"], [737, 2, 2, 0, "sigma2", "Array with same size as resids to store the conditional variance"], [737, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.RiskMetrics2006.forecast": [[739, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [739, 2, 2, 0, "horizon", "Forecast horizon.  Must be 1 or larger.  Forecasts are produced for horizons in [1, horizon]."], [739, 2, 2, 0, "method", "Method to use when producing the forecast. The default is analytic."], [739, 2, 2, 0, "parameters", "Parameters required to forecast the volatility model"], [739, 2, 2, 0, "random_state", "NumPy RandomState instance to use when method is 'bootstrap'"], [739, 2, 2, 0, "resids", "Residuals to use in the recursion"], [739, 2, 2, 0, "rng", "Callable random number generator required if method is 'simulation'. Must take a single shape input and return random samples numbers with that shape."], [739, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [739, 2, 2, 0, "start", "Index of the first observation to use as the starting point for the forecast.  Default is len(resids)."], [739, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.RiskMetrics2006.simulate": [[743, 2, 2, 0, "burn", "Number of additional observations to generate when initializing the simulation"], [743, 2, 2, 0, "initial_value", "Scalar or array of initial values to use when initializing the simulation"], [743, 2, 2, 0, "nobs", "Number of data points to simulate"], [743, 2, 2, 0, "parameters", "Parameters required to simulate the volatility model"], [743, 2, 2, 0, "rng", "Callable function that takes a single integer input and returns a vector of random numbers"]], "arch.univariate.RiskMetrics2006.starting_values": [[745, 2, 2, 0, "resids", "Array of (approximate) residuals to use when computing starting values"]], "arch.univariate.RiskMetrics2006.update": [[747, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [747, 2, 2, 0, "index", "The numerical index of the variance to compute"], [747, 2, 2, 0, "parameters", "The variance model parameters"], [747, 2, 2, 0, "resids", "The residual array. Only uses resids[:index] when computing sigma2[index]"], [747, 2, 2, 0, "sigma2", "The array containing the variances. Only uses sigma2[:index] when computing sigma2[index]. The computed value is stored in sigma2[index]."], [747, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.RiskMetrics2006.variance_bounds": [[749, 2, 2, 0, "power", "Power used in the model. 2.0, the default corresponds to standard ARCH models that evolve in squares."], [749, 2, 2, 0, "resids", "Approximate residuals to use to compute the lower and upper bounds on the conditional variance"]], "arch.univariate.SkewStudent.__init__": [[443, 2, 2, 0, "random_state", "Deprecated since version 5.0: random_state is deprecated. Use seed instead."], [443, 2, 2, 0, "seed", "Random number generator instance or int to use. Set to ensure reproducibility. If using an int, the argument is passed to np.random.default_rng.  If not provided, default_rng is used with system-provided entropy."]], "arch.univariate.SkewStudent": [[751, 3, 1, 0, "bounds", "Parameter bounds for use in optimization."], [752, 3, 1, 0, "cdf", "Cumulative distribution function"], [753, 3, 1, 0, "constraints", "Construct arrays to use in constrained optimization."], [754, 5, 1, 0, "generator", "The NumPy Generator or RandomState attached to the distribution"], [755, 3, 1, 0, "loglikelihood", "Computes the log-likelihood of assuming residuals are have a standardized (to have unit variance) Skew Student's t distribution, conditional on the variance."], [756, 3, 1, 0, "moment", "Moment of order n"], [757, 5, 1, 0, "name", "The name of the distribution"], [758, 3, 1, 0, "parameter_names", "Names of distribution shape parameters"], [759, 3, 1, 0, "partial_moment", "Order n lower partial moment from -inf to z"], [760, 3, 1, 0, "ppf", "Inverse cumulative density function (ICDF)"], [761, 5, 1, 0, "random_state", "The NumPy RandomState attached to the distribution"], [762, 3, 1, 0, "simulate", "Simulates i.i.d. draws from the distribution"], [763, 3, 1, 0, "starting_values", "Construct starting values for use in optimization."]], "arch.univariate.SkewStudent.bounds": [[751, 2, 2, 0, "resids", "Residuals to use when computing the bounds"]], "arch.univariate.SkewStudent.cdf": [[752, 2, 2, 0, "parameters", "Distribution parameters. Use None for parameterless distributions."], [752, 2, 2, 0, "resids", "Values at which to evaluate the cdf"]], "arch.univariate.SkewStudent.loglikelihood": [[755, 2, 2, 0, "individual", "Flag indicating whether to return the vector of individual log likelihoods (True) or the sum (False)"], [755, 2, 2, 0, "parameters", "Shape parameter of the skew-t distribution"], [755, 2, 2, 0, "resids", "The residuals to use in the log-likelihood calculation"], [755, 2, 2, 0, "sigma2", "Conditional variances of resids"]], "arch.univariate.SkewStudent.moment": [[756, 2, 2, 0, "n", "Order of moment"], [756, 2, 2, 0, "parameters", "Distribution parameters. Use None for parameterless distributions."]], "arch.univariate.SkewStudent.partial_moment": [[759, 2, 2, 0, "n", "Order of partial moment"], [759, 2, 2, 0, "parameters", "Distribution parameters.  Use None for parameterless distributions."], [759, 2, 2, 0, "z", "Upper bound for partial moment integral"]], "arch.univariate.SkewStudent.ppf": [[760, 2, 2, 0, "parameters", "Distribution parameters. Use None for parameterless distributions."], [760, 2, 2, 0, "pits", "Probability-integral-transformed values in the interval (0, 1)."]], "arch.univariate.SkewStudent.simulate": [[762, 2, 2, 0, "parameters", "Distribution parameters"]], "arch.univariate.SkewStudent.starting_values": [[763, 2, 2, 0, "std_resid", "Estimated standardized residuals to use in computing starting values for the shape parameter"]], "arch.univariate.StudentsT.__init__": [[444, 2, 2, 0, "random_state", "Deprecated since version 5.0: random_state is deprecated. Use seed instead."], [444, 2, 2, 0, "seed", "Random number generator instance or int to use. Set to ensure reproducibility. If using an int, the argument is passed to np.random.default_rng.  If not provided, default_rng is used with system-provided entropy."]], "arch.univariate.StudentsT": [[764, 3, 1, 0, "bounds", "Parameter bounds for use in optimization."], [765, 3, 1, 0, "cdf", "Cumulative distribution function"], [766, 3, 1, 0, "constraints", "Construct arrays to use in constrained optimization."], [767, 5, 1, 0, "generator", "The NumPy Generator or RandomState attached to the distribution"], [768, 3, 1, 0, "loglikelihood", "Computes the log-likelihood of assuming residuals are have a standardized (to have unit variance) Student's t distribution, conditional on the variance."], [769, 3, 1, 0, "moment", "Moment of order n"], [770, 5, 1, 0, "name", "The name of the distribution"], [771, 3, 1, 0, "parameter_names", "Names of distribution shape parameters"], [772, 3, 1, 0, "partial_moment", "Order n lower partial moment from -inf to z"], [773, 3, 1, 0, "ppf", "Inverse cumulative density function (ICDF)"], [774, 5, 1, 0, "random_state", "The NumPy RandomState attached to the distribution"], [775, 3, 1, 0, "simulate", "Simulates i.i.d. draws from the distribution"], [776, 3, 1, 0, "starting_values", "Construct starting values for use in optimization."]], "arch.univariate.StudentsT.bounds": [[764, 2, 2, 0, "resids", "Residuals to use when computing the bounds"]], "arch.univariate.StudentsT.cdf": [[765, 2, 2, 0, "parameters", "Distribution parameters. Use None for parameterless distributions."], [765, 2, 2, 0, "resids", "Values at which to evaluate the cdf"]], "arch.univariate.StudentsT.loglikelihood": [[768, 2, 2, 0, "individual", "Flag indicating whether to return the vector of individual log likelihoods (True) or the sum (False)"], [768, 2, 2, 0, "parameters", "Shape parameter of the t distribution"], [768, 2, 2, 0, "resids", "The residuals to use in the log-likelihood calculation"], [768, 2, 2, 0, "sigma2", "Conditional variances of resids"]], "arch.univariate.StudentsT.moment": [[769, 2, 2, 0, "n", "Order of moment"], [769, 2, 2, 0, "parameters", "Distribution parameters. Use None for parameterless distributions."]], "arch.univariate.StudentsT.partial_moment": [[772, 2, 2, 0, "n", "Order of partial moment"], [772, 2, 2, 0, "parameters", "Distribution parameters.  Use None for parameterless distributions."], [772, 2, 2, 0, "z", "Upper bound for partial moment integral"]], "arch.univariate.StudentsT.ppf": [[773, 2, 2, 0, "parameters", "Distribution parameters. Use None for parameterless distributions."], [773, 2, 2, 0, "pits", "Probability-integral-transformed values in the interval (0, 1)."]], "arch.univariate.StudentsT.simulate": [[775, 2, 2, 0, "parameters", "Distribution parameters"]], "arch.univariate.StudentsT.starting_values": [[776, 2, 2, 0, "std_resid", "Estimated standardized residuals to use in computing starting values for the shape parameter"]], "arch.univariate.ZeroMean.__init__": [[445, 2, 2, 0, "distribution", "Error distribution to use in the model"], [445, 2, 2, 0, "hold_back", "Number of observations at the start of the sample to exclude when estimating model parameters.  Used when comparing models with different lag lengths to estimate on the common sample."], [445, 2, 2, 0, "rescale", "Flag indicating whether to automatically rescale data if the scale of the data is likely to produce convergence issues when estimating model parameters. If False, the model is estimated on the data without transformation.  If True, than y is rescaled and the new scale is reported in the estimation results."], [445, 2, 2, 0, "volatility", "Volatility process to use in the model"], [445, 2, 2, 0, "y", "nobs element vector containing the dependent variable"]], "arch.univariate.ZeroMean": [[777, 3, 1, 0, "bounds", "Construct bounds for parameters to use in non-linear optimization"], [778, 3, 1, 0, "compute_param_cov", "Computes parameter covariances using numerical derivatives."], [779, 3, 1, 0, "constraints", "Construct linear constraint arrays  for use in non-linear optimization"], [780, 5, 1, 0, "distribution", "Set or gets the error distribution"], [781, 3, 1, 0, "fit", "Estimate model parameters"], [782, 3, 1, 0, "fix", "Allows an ARCHModelFixedResult to be constructed from fixed parameters."], [783, 3, 1, 0, "forecast", "Construct forecasts from estimated model"], [784, 5, 1, 0, "name", "The name of the model."], [785, 5, 1, 0, "num_params", "Returns the number of parameters"], [786, 3, 1, 0, "parameter_names", "List of parameters names"], [787, 3, 1, 0, "resids", "Compute model residuals"], [788, 3, 1, 0, "simulate", "Simulated data from a zero mean model"], [789, 3, 1, 0, "starting_values", "Returns starting values for the mean model, often the same as the values returned from fit"], [790, 5, 1, 0, "volatility", "Set or gets the volatility process"], [791, 5, 1, 0, "x", "Gets the value of the exogenous regressors in the model"], [792, 5, 1, 0, "y", "Returns the dependent variable"]], "arch.univariate.ZeroMean.compute_param_cov": [[778, 2, 2, 0, "backcast", "Value to use for pre-sample observations"], [778, 2, 2, 0, "params", "Model parameters"], [778, 2, 2, 0, "robust", "Flag indicating whether to use robust standard errors (True) or classic MLE (False)"]], "arch.univariate.ZeroMean.fit": [[781, 2, 2, 0, "backcast", "Value to use as backcast. Should be measure \\sigma^2_0 since model-specific non-linear transformations are applied to value before computing the variance recursions."], [781, 2, 2, 0, "cov_type", "Estimation method of parameter covariance.  Supported options are 'robust', which does not assume the Information Matrix Equality holds and 'classic' which does.  In the ARCH literature, 'robust' corresponds to Bollerslev-Wooldridge covariance estimator."], [781, 2, 2, 0, "disp", "Either 'final' to print optimization result or 'off' to display nothing. If using a boolean, False is \"off\" and True is \"final\""], [781, 2, 2, 0, "first_obs", "First observation to use when estimating model"], [781, 2, 2, 0, "last_obs", "Last observation to use when estimating model"], [781, 2, 2, 0, "options", "Options to pass to scipy.optimize.minimize.  Valid entries include 'ftol', 'eps', 'disp', and 'maxiter'."], [781, 2, 2, 0, "show_warning", "Flag indicating whether convergence warnings should be shown."], [781, 2, 2, 0, "starting_values", "Array of starting values to use.  If not provided, starting values are constructed by the model components."], [781, 2, 2, 0, "tol", "Tolerance for termination."], [781, 2, 2, 0, "update_freq", "Frequency of iteration updates.  Output is generated every update_freq iterations. Set to 0 to disable iterative output."]], "arch.univariate.ZeroMean.fix": [[782, 2, 2, 0, "first_obs", "First observation to use when fixing model"], [782, 2, 2, 0, "last_obs", "Last observation to use when fixing model"], [782, 2, 2, 0, "params", "User specified parameters to use when generating the result. Must have the correct number of parameters for a given choice of mean model, volatility model and distribution."]], "arch.univariate.ZeroMean.forecast": [[783, 2, 2, 0, "align", "Either 'origin' or 'target'.  When set of 'origin', the t-th row of forecasts contains the forecasts for t+1, t+2, ..., t+h. When set to 'target', the t-th row contains the 1-step ahead forecast from time t-1, the 2 step from time t-2, ..., and the h-step from time t-h.  'target' simplified computing forecast errors since the realization and h-step forecast are aligned."], [783, 2, 2, 0, "horizon", "Number of steps to forecast"], [783, 2, 2, 0, "method", "Method to use when producing the forecast. The default is analytic. The method only affects the variance forecast generation.  Not all volatility models support all methods. In particular, volatility models that do not evolve in squares such as EGARCH or TARCH do not support the 'analytic' method for horizons > 1."], [783, 2, 2, 0, "params", "Parameters required to forecast. Must be identical in shape to the parameters computed by fitting the model."], [783, 2, 2, 0, "random_state", "NumPy RandomState instance to use when method is 'bootstrap'"], [783, 2, 2, 0, "reindex", "Whether to reindex the forecasts to have the same dimension as the series being forecast. Prior to 4.18 this was the default. As of 4.19 this is now optional. If not provided, a warning is raised about the future change in the default which will occur after September 2021."], [783, 2, 2, 0, "rng", "Custom random number generator to use in simulation-based forecasts. Must produce random samples using the syntax rng(size) where size the 2-element tuple (simulations, horizon)."], [783, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [783, 2, 2, 0, "start", "An integer, datetime or str indicating the first observation to produce the forecast for.  Datetimes can only be used with pandas inputs that have a datetime index. Strings must be convertible to a date time, such as in '1945-01-01'."], [783, 2, 2, 0, "x", "Values to use for exogenous regressors if any are included in the model. Three formats are accepted:"]], "arch.univariate.ZeroMean.resids": [[787, 2, 2, 0, "params", "Model parameters"], [787, 2, 2, 0, "regressors", "Alternative regressor values to use when computing model residuals"], [787, 2, 2, 0, "y", "Alternative values to use when computing model residuals"]], "arch.univariate.ZeroMean.simulate": [[788, 2, 2, 0, "burn", "Number of values to simulate to initialize the model and remove dependence on initial values."], [788, 2, 2, 0, "initial_value", "This value is not used."], [788, 2, 2, 0, "initial_value_vol", "An array or scalar to use when initializing the volatility process."], [788, 2, 2, 0, "nobs", "Length of series to simulate"], [788, 2, 2, 0, "params", "Parameters to use when simulating the model.  Parameter order is [volatility distribution]. There are no mean parameters."], [788, 2, 2, 0, "x", "This value is not used."]], "arch.univariate.arch_model": [[891, 2, 2, 0, "dist", "Name of the error distribution.  Currently supported options are:"], [891, 2, 2, 0, "hold_back", "Number of observations at the start of the sample to exclude when estimating model parameters.  Used when comparing models with different lag lengths to estimate on the common sample."], [891, 2, 2, 0, "lags", "Either a scalar integer value indicating lag length or a list of integers specifying lag locations."], [891, 2, 2, 0, "mean", "Name of the mean model.  Currently supported options are: 'Constant', 'Zero', 'LS', 'AR', 'ARX', 'HAR' and  'HARX'"], [891, 2, 2, 0, "o", "Lag order of the asymmetric innovation"], [891, 2, 2, 0, "p", "Lag order of the symmetric innovation"], [891, 2, 2, 0, "power", "Power to use with GARCH and related models"], [891, 2, 2, 0, "q", "Lag order of lagged volatility or equivalent"], [891, 2, 2, 0, "rescale", "Flag indicating whether to automatically rescale data if the scale of the data is likely to produce convergence issues when estimating model parameters. If False, the model is estimated on the data without transformation.  If True, than y is rescaled and the new scale is reported in the estimation results."], [891, 2, 2, 0, "vol", "Name of the volatility model.  Currently supported options are: 'GARCH' (default), 'ARCH', 'EGARCH', 'FIGARCH', 'APARCH' and 'HARCH'"], [891, 2, 2, 0, "x", "Exogenous regressors.  Ignored if model does not permit exogenous regressors."], [891, 2, 2, 0, "y", "The dependent variable"]], "arch.univariate.base": [[446, 1, 1, 0, "ARCHModel", "Abstract base class for mean models in ARCH processes.  Specifies the conditional mean process."], [447, 1, 1, 0, "ARCHModelFixedResult", "Results for fixed parameters for an ARCHModel model"], [448, 1, 1, 0, "ARCHModelForecast", "Container for forecasts from an ARCH Model"], [449, 1, 1, 0, "ARCHModelForecastSimulation", "Container for a simulation or bootstrap-based forecasts from an ARCH Model"], [450, 1, 1, 0, "ARCHModelResult", "Results from estimation of an ARCHModel model"]], "arch.univariate.base.ARCHModel": [[793, 3, 1, 0, "bounds", "Construct bounds for parameters to use in non-linear optimization"], [794, 3, 1, 0, "compute_param_cov", "Computes parameter covariances using numerical derivatives."], [795, 3, 1, 0, "constraints", "Construct linear constraint arrays  for use in non-linear optimization"], [796, 5, 1, 0, "distribution", "Set or gets the error distribution"], [797, 3, 1, 0, "fit", "Estimate model parameters"], [798, 3, 1, 0, "fix", "Allows an ARCHModelFixedResult to be constructed from fixed parameters."], [799, 3, 1, 0, "forecast", "Construct forecasts from estimated model"], [800, 5, 1, 0, "name", "The name of the model."], [801, 5, 1, 0, "num_params", "Number of parameters in the model"], [802, 3, 1, 0, "parameter_names", "List of parameters names"], [803, 3, 1, 0, "resids", "Compute model residuals"], [804, 3, 1, 0, "simulate", ""], [805, 3, 1, 0, "starting_values", "Returns starting values for the mean model, often the same as the values returned from fit"], [806, 5, 1, 0, "volatility", "Set or gets the volatility process"], [807, 5, 1, 0, "y", "Returns the dependent variable"]], "arch.univariate.base.ARCHModel.compute_param_cov": [[794, 2, 2, 0, "backcast", "Value to use for pre-sample observations"], [794, 2, 2, 0, "params", "Model parameters"], [794, 2, 2, 0, "robust", "Flag indicating whether to use robust standard errors (True) or classic MLE (False)"]], "arch.univariate.base.ARCHModel.fit": [[797, 2, 2, 0, "backcast", "Value to use as backcast. Should be measure \\sigma^2_0 since model-specific non-linear transformations are applied to value before computing the variance recursions."], [797, 2, 2, 0, "cov_type", "Estimation method of parameter covariance.  Supported options are 'robust', which does not assume the Information Matrix Equality holds and 'classic' which does.  In the ARCH literature, 'robust' corresponds to Bollerslev-Wooldridge covariance estimator."], [797, 2, 2, 0, "disp", "Either 'final' to print optimization result or 'off' to display nothing. If using a boolean, False is \"off\" and True is \"final\""], [797, 2, 2, 0, "first_obs", "First observation to use when estimating model"], [797, 2, 2, 0, "last_obs", "Last observation to use when estimating model"], [797, 2, 2, 0, "options", "Options to pass to scipy.optimize.minimize.  Valid entries include 'ftol', 'eps', 'disp', and 'maxiter'."], [797, 2, 2, 0, "show_warning", "Flag indicating whether convergence warnings should be shown."], [797, 2, 2, 0, "starting_values", "Array of starting values to use.  If not provided, starting values are constructed by the model components."], [797, 2, 2, 0, "tol", "Tolerance for termination."], [797, 2, 2, 0, "update_freq", "Frequency of iteration updates.  Output is generated every update_freq iterations. Set to 0 to disable iterative output."]], "arch.univariate.base.ARCHModel.fix": [[798, 2, 2, 0, "first_obs", "First observation to use when fixing model"], [798, 2, 2, 0, "last_obs", "Last observation to use when fixing model"], [798, 2, 2, 0, "params", "User specified parameters to use when generating the result. Must have the correct number of parameters for a given choice of mean model, volatility model and distribution."]], "arch.univariate.base.ARCHModel.forecast": [[799, 2, 2, 0, "align", "Either 'origin' or 'target'.  When set of 'origin', the t-th row of forecasts contains the forecasts for t+1, t+2, ..., t+h. When set to 'target', the t-th row contains the 1-step ahead forecast from time t-1, the 2 step from time t-2, ..., and the h-step from time t-h.  'target' simplified computing forecast errors since the realization and h-step forecast are aligned."], [799, 2, 2, 0, "horizon", "Number of steps to forecast"], [799, 2, 2, 0, "method", "Method to use when producing the forecast. The default is analytic. The method only affects the variance forecast generation.  Not all volatility models support all methods. In particular, volatility models that do not evolve in squares such as EGARCH or TARCH do not support the 'analytic' method for horizons > 1."], [799, 2, 2, 0, "params", "Parameters required to forecast. Must be identical in shape to the parameters computed by fitting the model."], [799, 2, 2, 0, "random_state", "NumPy RandomState instance to use when method is 'bootstrap'"], [799, 2, 2, 0, "reindex", "Whether to reindex the forecasts to have the same dimension as the series being forecast. Prior to 4.18 this was the default. As of 4.19 this is now optional. If not provided, a warning is raised about the future change in the default which will occur after September 2021."], [799, 2, 2, 0, "rng", "Custom random number generator to use in simulation-based forecasts. Must produce random samples using the syntax rng(size) where size the 2-element tuple (simulations, horizon)."], [799, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [799, 2, 2, 0, "start", "An integer, datetime or str indicating the first observation to produce the forecast for.  Datetimes can only be used with pandas inputs that have a datetime index. Strings must be convertible to a date time, such as in '1945-01-01'."], [799, 2, 2, 0, "x", "Values to use for exogenous regressors if any are included in the model. Three formats are accepted:"]], "arch.univariate.base.ARCHModel.resids": [[803, 2, 2, 0, "params", "Model parameters"], [803, 2, 2, 0, "regressors", "Alternative regressor values to use when computing model residuals"], [803, 2, 2, 0, "y", "Alternative values to use when computing model residuals"]], "arch.univariate.base.ARCHModelFixedResult.__init__": [[447, 2, 2, 0, "dep_var", "Dependent variable"], [447, 2, 2, 0, "is_pandas", "Whether the original input was pandas"], [447, 2, 2, 0, "loglikelihood", "Loglikelihood at specified parameters"], [447, 2, 2, 0, "model", "The model object used to estimate the parameters"], [447, 2, 2, 0, "names", "Model parameter names"], [447, 2, 2, 0, "params", "Estimated parameters"], [447, 2, 2, 0, "resid", "Residuals from model.  Residuals have same shape as original data and contain nan-values in locations not used in estimation"], [447, 2, 2, 0, "volatility", "Conditional volatility from model"]], "arch.univariate.base.ARCHModelFixedResult": [[808, 5, 1, 0, "aic", "Akaike Information Criteria"], [809, 3, 1, 0, "arch_lm_test", "ARCH LM test for conditional heteroskedasticity"], [810, 5, 1, 0, "bic", "Schwarz/Bayesian Information Criteria"], [811, 5, 1, 0, "conditional_volatility", "Estimated conditional volatility"], [812, 3, 1, 0, "forecast", "Construct forecasts from estimated model"], [813, 3, 1, 0, "hedgehog_plot", "Plot forecasts from estimated model"], [814, 5, 1, 0, "loglikelihood", "Model loglikelihood"], [815, 5, 1, 0, "model", "Model instance used to produce the fit"], [816, 5, 1, 0, "nobs", "Number of data points used to estimate model"], [817, 5, 1, 0, "num_params", "Number of parameters in model"], [818, 5, 1, 0, "params", "Model Parameters"], [819, 3, 1, 0, "plot", "Plot standardized residuals and conditional volatility"], [820, 5, 1, 0, "resid", "Model residuals"], [821, 5, 1, 0, "std_resid", "Residuals standardized by conditional volatility"], [822, 3, 1, 0, "summary", "Constructs a summary of the results from a fit model."]], "arch.univariate.base.ARCHModelFixedResult.arch_lm_test": [[809, 2, 2, 0, "lags", "Number of lags to include in the model.  If not specified,"], [809, 2, 2, 0, "standardized", "Flag indicating to test the model residuals divided by their conditional standard deviations.  If False, directly tests the estimated residuals."]], "arch.univariate.base.ARCHModelFixedResult.forecast": [[812, 2, 2, 0, "align", "Either 'origin' or 'target'.  When set of 'origin', the t-th row of forecasts contains the forecasts for t+1, t+2, ..., t+h. When set to 'target', the t-th row contains the 1-step ahead forecast from time t-1, the 2 step from time t-2, ..., and the h-step from time t-h.  'target' simplified computing forecast errors since the realization and h-step forecast are aligned."], [812, 2, 2, 0, "horizon", "Number of steps to forecast"], [812, 2, 2, 0, "method", "Method to use when producing the forecast. The default is analytic. The method only affects the variance forecast generation.  Not all volatility models support all methods. In particular, volatility models that do not evolve in squares such as EGARCH or TARCH do not support the 'analytic' method for horizons > 1."], [812, 2, 2, 0, "params", "Alternative parameters to use.  If not provided, the parameters estimated when fitting the model are used.  Must be identical in shape to the parameters computed by fitting the model."], [812, 2, 2, 0, "random_state", "NumPy RandomState instance to use when method is 'bootstrap'"], [812, 2, 2, 0, "reindex", "Whether to reindex the forecasts to have the same dimension as the series being forecast."], [812, 2, 2, 0, "rng", "Custom random number generator to use in simulation-based forecasts. Must produce random samples using the syntax rng(size) where size the 2-element tuple (simulations, horizon)."], [812, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [812, 2, 2, 0, "start", "An integer, datetime or str indicating the first observation to produce the forecast for.  Datetimes can only be used with pandas inputs that have a datetime index. Strings must be convertible to a date time, such as in '1945-01-01'."], [812, 2, 2, 0, "x", "Values to use for exogenous regressors if any are included in the model. Three formats are accepted:"]], "arch.univariate.base.ARCHModelFixedResult.hedgehog_plot": [[813, 2, 2, 0, "horizon", "Number of steps to forecast"], [813, 2, 2, 0, "method", "Method to use when producing the forecast. The default is analytic. The method only affects the variance forecast generation.  Not all volatility models support all methods. In particular, volatility models that do not evolve in squares such as EGARCH or TARCH do not support the 'analytic' method for horizons > 1."], [813, 2, 2, 0, "params", "Alternative parameters to use.  If not provided, the parameters computed by fitting the model are used.  Must be 1-d and identical in shape to the parameters computed by fitting the model."], [813, 2, 2, 0, "plot_type", "Quantity to plot, the forecast volatility or the forecast mean"], [813, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [813, 2, 2, 0, "start", "An integer, datetime or str indicating the first observation to produce the forecast for.  Datetimes can only be used with pandas inputs that have a datetime index.  Strings must be convertible to a date time, such as in '1945-01-01'.  If not provided, the start is set to the earliest forecastable date."], [813, 2, 2, 0, "step", "Non-negative number of forecasts to skip between spines"]], "arch.univariate.base.ARCHModelFixedResult.plot": [[819, 2, 2, 0, "annualize", "String containing frequency of data that indicates plot should contain annualized volatility.  Supported values are 'D' (daily), 'W' (weekly) and 'M' (monthly), which scale variance by 252, 52, and 12, respectively."], [819, 2, 2, 0, "scale", "Value to use when scaling returns to annualize.  If scale is provided, annualize is ignored and the value in scale is used."]], "arch.univariate.base.ARCHModelForecast.__init__": [[448, 2, 2, 0, "align", ""], [448, 2, 2, 0, "index", ""], [448, 2, 2, 0, "mean", ""], [448, 2, 2, 0, "residual_variance", ""], [448, 2, 2, 0, "simulated_paths", ""], [448, 2, 2, 0, "simulated_residual_variances", ""], [448, 2, 2, 0, "simulated_residuals", ""], [448, 2, 2, 0, "simulated_variances", ""], [448, 2, 2, 0, "variance", ""]], "arch.univariate.base.ARCHModelForecast": [[823, 5, 1, 0, "mean", "Forecast values for the conditional mean of the process"], [824, 5, 1, 0, "residual_variance", "Forecast values for the conditional variance of the residuals"], [825, 5, 1, 0, "simulations", "Detailed simulation results if using a simulation-based method"], [826, 5, 1, 0, "variance", "Forecast values for the conditional variance of the process"]], "arch.univariate.base.ARCHModelForecastSimulation.__init__": [[449, 2, 2, 0, "index", ""], [449, 2, 2, 0, "residual_variances", ""], [449, 2, 2, 0, "residuals", ""], [449, 2, 2, 0, "values", ""], [449, 2, 2, 0, "variances", ""]], "arch.univariate.base.ARCHModelForecastSimulation": [[827, 5, 1, 0, "index", "The index aligned to dimension 0 of the simulation paths"], [828, 5, 1, 0, "residual_variances", "Simulated variance of the residuals"], [829, 5, 1, 0, "residuals", "Simulated residuals used to produce the values"], [830, 5, 1, 0, "values", "The values of the process"], [831, 5, 1, 0, "variances", "Simulated variances of the values"]], "arch.univariate.base.ARCHModelResult.__init__": [[450, 2, 2, 0, "cov_type", "String describing the covariance estimator used"], [450, 2, 2, 0, "dep_var", "Dependent variable"], [450, 2, 2, 0, "fit_start", "Integer index of the first observation used to fit the model"], [450, 2, 2, 0, "fit_stop", "Integer index of the last observation used to fit the model using slice notation fit_start:fit_stop"], [450, 2, 2, 0, "is_pandas", "Whether the original input was pandas"], [450, 2, 2, 0, "loglikelihood", "Loglikelihood at estimated parameters"], [450, 2, 2, 0, "model", "The model object used to estimate the parameters"], [450, 2, 2, 0, "names", "Model parameter names"], [450, 2, 2, 0, "optim_output", "Result of log-likelihood optimization"], [450, 2, 2, 0, "param_cov", "Estimated variance-covariance matrix of params.  If none, calls method to compute variance from model when parameter covariance is first used from result"], [450, 2, 2, 0, "params", "Estimated parameters"], [450, 2, 2, 0, "r2", "Model R-squared"], [450, 2, 2, 0, "resid", "Residuals from model.  Residuals have same shape as original data and contain nan-values in locations not used in estimation"], [450, 2, 2, 0, "volatility", "Conditional volatility from model"]], "arch.univariate.base.ARCHModelResult": [[832, 5, 1, 0, "aic", "Akaike Information Criteria"], [833, 3, 1, 0, "arch_lm_test", "ARCH LM test for conditional heteroskedasticity"], [834, 5, 1, 0, "bic", "Schwarz/Bayesian Information Criteria"], [835, 5, 1, 0, "conditional_volatility", "Estimated conditional volatility"], [836, 3, 1, 0, "conf_int", "Parameter confidence intervals"], [837, 5, 1, 0, "convergence_flag", "scipy.optimize.minimize result flag"], [838, 5, 1, 0, "fit_start", "Start of sample used to estimate parameters"], [839, 5, 1, 0, "fit_stop", "End of sample used to estimate parameters"], [840, 3, 1, 0, "forecast", "Construct forecasts from estimated model"], [841, 3, 1, 0, "hedgehog_plot", "Plot forecasts from estimated model"], [842, 5, 1, 0, "loglikelihood", "Model loglikelihood"], [843, 5, 1, 0, "model", "Model instance used to produce the fit"], [844, 5, 1, 0, "nobs", "Number of data points used to estimate model"], [845, 5, 1, 0, "num_params", "Number of parameters in model"], [846, 5, 1, 0, "optimization_result", "Information about the convergence of the loglikelihood optimization"], [847, 5, 1, 0, "param_cov", "Parameter covariance"], [848, 5, 1, 0, "params", "Model Parameters"], [849, 3, 1, 0, "plot", "Plot standardized residuals and conditional volatility"], [850, 5, 1, 0, "pvalues", "Array of p-values for the t-statistics"], [851, 5, 1, 0, "resid", "Model residuals"], [852, 5, 1, 0, "rsquared", "R-squared"], [853, 5, 1, 0, "rsquared_adj", "Degree of freedom adjusted R-squared"], [854, 5, 1, 0, "scale", "The scale applied to the original data before estimating the model."], [855, 5, 1, 0, "std_err", "Array of parameter standard errors"], [856, 5, 1, 0, "std_resid", "Residuals standardized by conditional volatility"], [857, 3, 1, 0, "summary", "Constructs a summary of the results from a fit model."], [858, 5, 1, 0, "tvalues", "Array of t-statistics testing the null that the coefficient are 0"]], "arch.univariate.base.ARCHModelResult.arch_lm_test": [[833, 2, 2, 0, "lags", "Number of lags to include in the model.  If not specified,"], [833, 2, 2, 0, "standardized", "Flag indicating to test the model residuals divided by their conditional standard deviations.  If False, directly tests the estimated residuals."]], "arch.univariate.base.ARCHModelResult.conf_int": [[836, 2, 2, 0, "alpha", "Size (prob.) to use when constructing the confidence interval."]], "arch.univariate.base.ARCHModelResult.forecast": [[840, 2, 2, 0, "align", "Either 'origin' or 'target'.  When set of 'origin', the t-th row of forecasts contains the forecasts for t+1, t+2, ..., t+h. When set to 'target', the t-th row contains the 1-step ahead forecast from time t-1, the 2 step from time t-2, ..., and the h-step from time t-h.  'target' simplified computing forecast errors since the realization and h-step forecast are aligned."], [840, 2, 2, 0, "horizon", "Number of steps to forecast"], [840, 2, 2, 0, "method", "Method to use when producing the forecast. The default is analytic. The method only affects the variance forecast generation.  Not all volatility models support all methods. In particular, volatility models that do not evolve in squares such as EGARCH or TARCH do not support the 'analytic' method for horizons > 1."], [840, 2, 2, 0, "params", "Alternative parameters to use.  If not provided, the parameters estimated when fitting the model are used.  Must be identical in shape to the parameters computed by fitting the model."], [840, 2, 2, 0, "random_state", "NumPy RandomState instance to use when method is 'bootstrap'"], [840, 2, 2, 0, "reindex", "Whether to reindex the forecasts to have the same dimension as the series being forecast."], [840, 2, 2, 0, "rng", "Custom random number generator to use in simulation-based forecasts. Must produce random samples using the syntax rng(size) where size the 2-element tuple (simulations, horizon)."], [840, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [840, 2, 2, 0, "start", "An integer, datetime or str indicating the first observation to produce the forecast for.  Datetimes can only be used with pandas inputs that have a datetime index. Strings must be convertible to a date time, such as in '1945-01-01'."], [840, 2, 2, 0, "x", "Values to use for exogenous regressors if any are included in the model. Three formats are accepted:"]], "arch.univariate.base.ARCHModelResult.hedgehog_plot": [[841, 2, 2, 0, "horizon", "Number of steps to forecast"], [841, 2, 2, 0, "method", "Method to use when producing the forecast. The default is analytic. The method only affects the variance forecast generation.  Not all volatility models support all methods. In particular, volatility models that do not evolve in squares such as EGARCH or TARCH do not support the 'analytic' method for horizons > 1."], [841, 2, 2, 0, "params", "Alternative parameters to use.  If not provided, the parameters computed by fitting the model are used.  Must be 1-d and identical in shape to the parameters computed by fitting the model."], [841, 2, 2, 0, "plot_type", "Quantity to plot, the forecast volatility or the forecast mean"], [841, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [841, 2, 2, 0, "start", "An integer, datetime or str indicating the first observation to produce the forecast for.  Datetimes can only be used with pandas inputs that have a datetime index.  Strings must be convertible to a date time, such as in '1945-01-01'.  If not provided, the start is set to the earliest forecastable date."], [841, 2, 2, 0, "step", "Non-negative number of forecasts to skip between spines"]], "arch.univariate.base.ARCHModelResult.plot": [[849, 2, 2, 0, "annualize", "String containing frequency of data that indicates plot should contain annualized volatility.  Supported values are 'D' (daily), 'W' (weekly) and 'M' (monthly), which scale variance by 252, 52, and 12, respectively."], [849, 2, 2, 0, "scale", "Value to use when scaling returns to annualize.  If scale is provided, annualize is ignored and the value in scale is used."]], "arch.univariate.distribution": [[451, 1, 1, 0, "Distribution", "Template for subclassing only"]], "arch.univariate.distribution.Distribution": [[859, 3, 1, 0, "bounds", "Parameter bounds for use in optimization."], [860, 3, 1, 0, "cdf", "Cumulative distribution function"], [861, 3, 1, 0, "constraints", "Construct arrays to use in constrained optimization."], [862, 5, 1, 0, "generator", "The NumPy Generator or RandomState attached to the distribution"], [863, 3, 1, 0, "loglikelihood", "Loglikelihood evaluation."], [864, 3, 1, 0, "moment", "Moment of order n"], [865, 5, 1, 0, "name", "The name of the distribution"], [866, 3, 1, 0, "parameter_names", "Names of distribution shape parameters"], [867, 3, 1, 0, "partial_moment", "Order n lower partial moment from -inf to z"], [868, 3, 1, 0, "ppf", "Inverse cumulative density function (ICDF)"], [869, 5, 1, 0, "random_state", "The NumPy RandomState attached to the distribution"], [870, 3, 1, 0, "simulate", "Simulates i.i.d. draws from the distribution"], [871, 3, 1, 0, "starting_values", "Construct starting values for use in optimization."]], "arch.univariate.distribution.Distribution.bounds": [[859, 2, 2, 0, "resids", "Residuals to use when computing the bounds"]], "arch.univariate.distribution.Distribution.cdf": [[860, 2, 2, 0, "parameters", "Distribution parameters. Use None for parameterless distributions."], [860, 2, 2, 0, "resids", "Values at which to evaluate the cdf"]], "arch.univariate.distribution.Distribution.loglikelihood": [[863, 2, 2, 0, "individual", "Flag indicating whether to return the vector of individual log likelihoods (True) or the sum (False)"], [863, 2, 2, 0, "parameters", "Distribution shape parameters"], [863, 2, 2, 0, "resids", "nobs array of model residuals"], [863, 2, 2, 0, "sigma2", "nobs array of conditional variances"]], "arch.univariate.distribution.Distribution.moment": [[864, 2, 2, 0, "n", "Order of moment"], [864, 2, 2, 0, "parameters", "Distribution parameters. Use None for parameterless distributions."]], "arch.univariate.distribution.Distribution.partial_moment": [[867, 2, 2, 0, "n", "Order of partial moment"], [867, 2, 2, 0, "parameters", "Distribution parameters.  Use None for parameterless distributions."], [867, 2, 2, 0, "z", "Upper bound for partial moment integral"]], "arch.univariate.distribution.Distribution.ppf": [[868, 2, 2, 0, "parameters", "Distribution parameters. Use None for parameterless distributions."], [868, 2, 2, 0, "pits", "Probability-integral-transformed values in the interval (0, 1)."]], "arch.univariate.distribution.Distribution.simulate": [[870, 2, 2, 0, "parameters", "Distribution parameters"]], "arch.univariate.distribution.Distribution.starting_values": [[871, 2, 2, 0, "std_resid", "Estimated standardized residuals to use in computing starting values for the shape parameter"]], "arch.univariate.recursions_python": [[452, 1, 1, 0, "VolatilityUpdater", "Base class that all volatility updaters must inherit from."]], "arch.univariate.recursions_python.VolatilityUpdater": [[872, 3, 1, 0, "initialize_update", "Initialize the recursion prior to calling update"], [873, 3, 1, 0, "update", "Update the current variance at location t"]], "arch.univariate.recursions_python.VolatilityUpdater.initialize_update": [[872, 2, 2, 0, "backcast", "The backcast value(s)."], [872, 2, 2, 0, "nobs", "The number of observations in the sample."], [872, 2, 2, 0, "parameters", "The model parameters."]], "arch.univariate.recursions_python.VolatilityUpdater.update": [[873, 2, 2, 0, "parameters", "Model parameters"], [873, 2, 2, 0, "resids", "Residuals to use in the recursion"], [873, 2, 2, 0, "sigma2", "Conditional variances with same shape as resids"], [873, 2, 2, 0, "t", "The index of the value of sigma2 to update. Assumes but does not check that update has been called recursively for 0,1,...,t-1."], [873, 2, 2, 0, "var_bounds", "nobs by 2-element array of upper and lower bounds for conditional variances for each time period"]], "arch.univariate.volatility": [[453, 1, 1, 0, "VolatilityProcess", "Abstract base class for ARCH models.  Allows the conditional mean model to be specified separately from the conditional variance, even though parameters are estimated jointly."]], "arch.univariate.volatility.VolatilityProcess": [[874, 3, 1, 0, "backcast", "Construct values for backcasting to start the recursion"], [875, 3, 1, 0, "backcast_transform", "Transformation to apply to user-provided backcast values"], [876, 3, 1, 0, "bounds", "Returns bounds for parameters"], [877, 3, 1, 0, "compute_variance", "Compute the variance for the ARCH model"], [878, 3, 1, 0, "constraints", "Construct parameter constraints arrays for parameter estimation"], [879, 3, 1, 0, "forecast", "Forecast volatility from the model"], [880, 5, 1, 0, "name", "The name of the volatility process"], [881, 5, 1, 0, "num_params", "The number of parameters in the model"], [882, 3, 1, 0, "parameter_names", "Names of model parameters"], [883, 3, 1, 0, "simulate", "Simulate data from the model"], [884, 5, 1, 0, "start", "Index to use to start variance subarray selection"], [885, 3, 1, 0, "starting_values", "Returns starting values for the ARCH model"], [886, 5, 1, 0, "stop", "Index to use to stop variance subarray selection"], [887, 3, 1, 0, "update", "Compute the variance for a single observation"], [888, 5, 1, 0, "updateable", "Flag indicating that the volatility process supports update"], [889, 3, 1, 0, "variance_bounds", "Construct loose bounds for conditional variances."], [890, 5, 1, 0, "volatility_updater", "Get the volatility updater associated with the volatility process"]], "arch.univariate.volatility.VolatilityProcess.backcast": [[874, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.volatility.VolatilityProcess.backcast_transform": [[875, 2, 2, 0, "backcast", "User-provided backcast that approximates sigma2[0]."]], "arch.univariate.volatility.VolatilityProcess.bounds": [[876, 2, 2, 0, "resids", "Vector of (approximate) residuals"]], "arch.univariate.volatility.VolatilityProcess.compute_variance": [[877, 2, 2, 0, "backcast", "Value to use when initializing ARCH recursion. Can be an ndarray when the model contains multiple components."], [877, 2, 2, 0, "parameters", "Model parameters"], [877, 2, 2, 0, "resids", "Vector of mean zero residuals"], [877, 2, 2, 0, "sigma2", "Array with same size as resids to store the conditional variance"], [877, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.volatility.VolatilityProcess.forecast": [[879, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [879, 2, 2, 0, "horizon", "Forecast horizon.  Must be 1 or larger.  Forecasts are produced for horizons in [1, horizon]."], [879, 2, 2, 0, "method", "Method to use when producing the forecast. The default is analytic."], [879, 2, 2, 0, "parameters", "Parameters required to forecast the volatility model"], [879, 2, 2, 0, "random_state", "NumPy RandomState instance to use when method is 'bootstrap'"], [879, 2, 2, 0, "resids", "Residuals to use in the recursion"], [879, 2, 2, 0, "rng", "Callable random number generator required if method is 'simulation'. Must take a single shape input and return random samples numbers with that shape."], [879, 2, 2, 0, "simulations", "Number of simulations to run when computing the forecast using either simulation or bootstrap."], [879, 2, 2, 0, "start", "Index of the first observation to use as the starting point for the forecast.  Default is len(resids)."], [879, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.volatility.VolatilityProcess.simulate": [[883, 2, 2, 0, "burn", "Number of additional observations to generate when initializing the simulation"], [883, 2, 2, 0, "initial_value", "Scalar or array of initial values to use when initializing the simulation"], [883, 2, 2, 0, "nobs", "Number of data points to simulate"], [883, 2, 2, 0, "parameters", "Parameters required to simulate the volatility model"], [883, 2, 2, 0, "rng", "Callable function that takes a single integer input and returns a vector of random numbers"]], "arch.univariate.volatility.VolatilityProcess.starting_values": [[885, 2, 2, 0, "resids", "Array of (approximate) residuals to use when computing starting values"]], "arch.univariate.volatility.VolatilityProcess.update": [[887, 2, 2, 0, "backcast", "Value to use when initializing the recursion"], [887, 2, 2, 0, "index", "The numerical index of the variance to compute"], [887, 2, 2, 0, "parameters", "The variance model parameters"], [887, 2, 2, 0, "resids", "The residual array. 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The computed value is stored in sigma2[index]."], [887, 2, 2, 0, "var_bounds", "Array containing columns of lower and upper bounds"]], "arch.univariate.volatility.VolatilityProcess.variance_bounds": [[889, 2, 2, 0, "power", "Power used in the model. 2.0, the default corresponds to standard ARCH models that evolve in squares."], [889, 2, 2, 0, "resids", "Approximate residuals to use to compute the lower and upper bounds on the conditional variance"]], "arch.utility": [[900, 0, 0, 1, "testing", ""]], "arch.utility.testing": [[900, 1, 1, 0, "WaldTestStatistic", "Test statistic holder for Wald-type tests"]], "arch.utility.testing.WaldTestStatistic.__init__": [[900, 2, 2, 0, "alternative", "A statement of the test's alternative hypothesis"], [900, 2, 2, 0, "df", "Degree of freedom."], [900, 2, 2, 0, "name", "Name of test"], [900, 2, 2, 0, "null", "A statement of the test's null hypothesis"], [900, 2, 2, 0, "stat", "The test statistic"]], "arch.utility.testing.WaldTestStatistic": [[900, 5, 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@@ -15,7 +15,7 @@
     
   
       
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@@ -47,7 +47,7 @@
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@@ -1102,12 +1102,12 @@
 <h1 id="arch-unitroot-adf">arch.unitroot.ADF<a class="headerlink" href="#arch-unitroot-adf" title="Link to this heading">¶</a></h1>
 <dl class="py class objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.unitroot.ADF">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.unitroot.</span></span><span class="sig-name descname"><span class="pre">ADF</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.ADF.__init__.y" title="arch.unitroot.ADF.__init__.y (Python parameter) — The data to test for a unit root"><span class="n"><span class="pre">y</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.ADF.__init__.lags" title="arch.unitroot.ADF.__init__.lags (Python parameter) — The number of lags to use in the ADF regression.  If omitted or None, method is used to automatically select the lag length with no more than max_lags are included."><span class="n"><span class="pre">lags</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.ADF.__init__.trend" title="arch.unitroot.ADF.__init__.trend (Python parameter) — The trend component to include in the test"><span class="n"><span class="pre">trend</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'n'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'c'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'ct'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'ctt'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;c&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.ADF.__init__.max_lags" title="arch.unitroot.ADF.__init__.max_lags (Python parameter) — The maximum number of lags to use when selecting lag length"><span class="n"><span class="pre">max_lags</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.ADF.__init__.method" title="arch.unitroot.ADF.__init__.method (Python parameter) — The method to use when selecting the lag length"><span class="n"><span class="pre">method</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'aic'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'bic'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'t-stat'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;aic&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.ADF.__init__.low_memory" title="arch.unitroot.ADF.__init__.low_memory (Python parameter) — Flag indicating whether to use a low memory implementation of the lag selection algorithm. The low memory algorithm is slower than the standard algorithm but will use 2-4% of the memory required for the standard algorithm. This options allows automatic lag selection to be used in very long time series. If None, use automatic selection of algorithm."><span class="n"><span class="pre">low_memory</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/unitroot/unitroot.html#ADF"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.unitroot.ADF" title="Link to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.unitroot.</span></span><span class="sig-name descname"><span class="pre">ADF</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.ADF.__init__.y" title="arch.unitroot.ADF.__init__.y (Python parameter) — The data to test for a unit root"><span class="n"><span class="pre">y</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.ADF.__init__.lags" title="arch.unitroot.ADF.__init__.lags (Python parameter) — The number of lags to use in the ADF regression.  If omitted or None, method is used to automatically select the lag length with no more than max_lags are included."><span class="n"><span class="pre">lags</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.ADF.__init__.trend" title="arch.unitroot.ADF.__init__.trend (Python parameter) — The trend component to include in the test"><span class="n"><span class="pre">trend</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'n'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'c'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'ct'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'ctt'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;c&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.ADF.__init__.max_lags" title="arch.unitroot.ADF.__init__.max_lags (Python parameter) — The maximum number of lags to use when selecting lag length"><span class="n"><span class="pre">max_lags</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.ADF.__init__.method" title="arch.unitroot.ADF.__init__.method (Python parameter) — The method to use when selecting the lag length"><span class="n"><span class="pre">method</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'aic'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'bic'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'t-stat'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;aic&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.ADF.__init__.low_memory" title="arch.unitroot.ADF.__init__.low_memory (Python parameter) — Flag indicating whether to use a low memory implementation of the lag selection algorithm. The low memory algorithm is slower than the standard algorithm but will use 2-4% of the memory required for the standard algorithm. This options allows automatic lag selection to be used in very long time series. If None, use automatic selection of algorithm."><span class="n"><span class="pre">low_memory</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/unitroot/unitroot.html#ADF"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.unitroot.ADF" title="Link to this definition">¶</a></dt>
 <dd><p>Augmented Dickey-Fuller unit root test</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.unitroot.ADF-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.unitroot.ADF-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.unitroot.ADF.__init__.y"><span class="n sig-name">y</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.unitroot.ADF.__init__.y" title="Permalink to this definition">¶</a></dt><dd><p>The data to test for a unit root</p>
+<dt id="arch.unitroot.ADF.__init__.y"><span class="n sig-name">y</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a></span></span><a class="headerlink" href="#arch.unitroot.ADF.__init__.y" title="Permalink to this definition">¶</a></dt><dd><p>The data to test for a unit root</p>
 </dd>
 <dt id="arch.unitroot.ADF.__init__.lags"><span class="n sig-name">lags</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.unitroot.ADF.__init__.lags" title="Permalink to this definition">¶</a></dt><dd><p>The number of lags to use in the ADF regression.  If omitted or None,
 <cite>method</cite> is used to automatically select the lag length with no more
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diff --git a/devel/unitroot/generated/arch.unitroot.DFGLS.html b/devel/unitroot/generated/arch.unitroot.DFGLS.html
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 <h1 id="arch-unitroot-dfgls">arch.unitroot.DFGLS<a class="headerlink" href="#arch-unitroot-dfgls" title="Link to this heading">¶</a></h1>
 <dl class="py class objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.unitroot.DFGLS">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.unitroot.</span></span><span class="sig-name descname"><span class="pre">DFGLS</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.DFGLS.__init__.y" title="arch.unitroot.DFGLS.__init__.y (Python parameter) — The data to test for a unit root"><span class="n"><span class="pre">y</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.DFGLS.__init__.lags" title="arch.unitroot.DFGLS.__init__.lags (Python parameter) — The number of lags to use in the ADF regression.  If omitted or None, method is used to automatically select the lag length with no more than max_lags are included."><span class="n"><span class="pre">lags</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.DFGLS.__init__.trend" title="arch.unitroot.DFGLS.__init__.trend (Python parameter) — The trend component to include in the test"><span class="n"><span class="pre">trend</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'c'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'ct'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;c&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.DFGLS.__init__.max_lags" title="arch.unitroot.DFGLS.__init__.max_lags (Python parameter) — The maximum number of lags to use when selecting lag length. When using automatic lag length selection, the lag is selected using OLS detrending rather than GLS detrending (2)."><span class="n"><span class="pre">max_lags</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.DFGLS.__init__.method" title="arch.unitroot.DFGLS.__init__.method (Python parameter) — The method to use when selecting the lag length"><span class="n"><span class="pre">method</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'aic'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'bic'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'t-stat'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;aic&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">low_memory</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/unitroot/unitroot.html#DFGLS"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.unitroot.DFGLS" title="Link to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.unitroot.</span></span><span class="sig-name descname"><span class="pre">DFGLS</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.DFGLS.__init__.y" title="arch.unitroot.DFGLS.__init__.y (Python parameter) — The data to test for a unit root"><span class="n"><span class="pre">y</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.DFGLS.__init__.lags" title="arch.unitroot.DFGLS.__init__.lags (Python parameter) — The number of lags to use in the ADF regression.  If omitted or None, method is used to automatically select the lag length with no more than max_lags are included."><span class="n"><span class="pre">lags</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.DFGLS.__init__.trend" title="arch.unitroot.DFGLS.__init__.trend (Python parameter) — The trend component to include in the test"><span class="n"><span class="pre">trend</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'c'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'ct'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;c&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.DFGLS.__init__.max_lags" title="arch.unitroot.DFGLS.__init__.max_lags (Python parameter) — The maximum number of lags to use when selecting lag length. When using automatic lag length selection, the lag is selected using OLS detrending rather than GLS detrending (2)."><span class="n"><span class="pre">max_lags</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.DFGLS.__init__.method" title="arch.unitroot.DFGLS.__init__.method (Python parameter) — The method to use when selecting the lag length"><span class="n"><span class="pre">method</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'aic'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'bic'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'t-stat'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;aic&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">low_memory</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/unitroot/unitroot.html#DFGLS"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.unitroot.DFGLS" title="Link to this definition">¶</a></dt>
 <dd><p>Elliott, Rothenberg and Stock’s (<a class="footnote-reference brackets" href="#id3" id="id1" role="doc-noteref"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></a>) GLS detrended Dickey-Fuller</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.unitroot.DFGLS-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.unitroot.DFGLS-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.unitroot.DFGLS.__init__.y"><span class="n sig-name">y</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.unitroot.DFGLS.__init__.y" title="Permalink to this definition">¶</a></dt><dd><p>The data to test for a unit root</p>
+<dt id="arch.unitroot.DFGLS.__init__.y"><span class="n sig-name">y</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a></span></span><a class="headerlink" href="#arch.unitroot.DFGLS.__init__.y" title="Permalink to this definition">¶</a></dt><dd><p>The data to test for a unit root</p>
 </dd>
 <dt id="arch.unitroot.DFGLS.__init__.lags"><span class="n sig-name">lags</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.unitroot.DFGLS.__init__.lags" title="Permalink to this definition">¶</a></dt><dd><p>The number of lags to use in the ADF regression.  If omitted or None,
 <cite>method</cite> is used to automatically select the lag length with no more
@@ -1329,7 +1329,7 @@ <h1 id="arch-unitroot-dfgls">arch.unitroot.DFGLS<a class="headerlink" href="#arc
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diff --git a/devel/unitroot/generated/arch.unitroot.KPSS.html b/devel/unitroot/generated/arch.unitroot.KPSS.html
index 7b0f93479e..2370c450d9 100644
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 <h1 id="arch-unitroot-kpss">arch.unitroot.KPSS<a class="headerlink" href="#arch-unitroot-kpss" title="Link to this heading">¶</a></h1>
 <dl class="py class objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.unitroot.KPSS">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.unitroot.</span></span><span class="sig-name descname"><span class="pre">KPSS</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.KPSS.__init__.y" title="arch.unitroot.KPSS.__init__.y (Python parameter) — The data to test for stationarity"><span class="n"><span class="pre">y</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.KPSS.__init__.lags" title="arch.unitroot.KPSS.__init__.lags (Python parameter) — The number of lags to use in the Newey-West estimator of the long-run covariance.  If omitted or None, the number of lags is calculated with the data-dependent method of Hobijn et al. (1998). See also Andrews (1991), Newey &amp; West (1994), and Schwert (1989). Set lags=-1 to use the old method that only depends on the sample size, 12 * (nobs/100) ** (1/4)."><span class="n"><span class="pre">lags</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.KPSS.__init__.trend" title="arch.unitroot.KPSS.__init__.trend (Python parameter) — &quot;c&quot; - Include a constant (Default) &quot;ct&quot; - Include a constant and linear time trend"><span class="n"><span class="pre">trend</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'c'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'ct'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;c&#39;</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/unitroot/unitroot.html#KPSS"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.unitroot.KPSS" title="Link to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.unitroot.</span></span><span class="sig-name descname"><span class="pre">KPSS</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.KPSS.__init__.y" title="arch.unitroot.KPSS.__init__.y (Python parameter) — The data to test for stationarity"><span class="n"><span class="pre">y</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.KPSS.__init__.lags" title="arch.unitroot.KPSS.__init__.lags (Python parameter) — The number of lags to use in the Newey-West estimator of the long-run covariance.  If omitted or None, the number of lags is calculated with the data-dependent method of Hobijn et al. (1998). See also Andrews (1991), Newey &amp; West (1994), and Schwert (1989). Set lags=-1 to use the old method that only depends on the sample size, 12 * (nobs/100) ** (1/4)."><span class="n"><span class="pre">lags</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.KPSS.__init__.trend" title="arch.unitroot.KPSS.__init__.trend (Python parameter) — &quot;c&quot; - Include a constant (Default) &quot;ct&quot; - Include a constant and linear time trend"><span class="n"><span class="pre">trend</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'c'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'ct'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;c&#39;</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/unitroot/unitroot.html#KPSS"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.unitroot.KPSS" title="Link to this definition">¶</a></dt>
 <dd><p>Kwiatkowski, Phillips, Schmidt and Shin (KPSS) stationarity test</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.unitroot.KPSS-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.unitroot.KPSS-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.unitroot.KPSS.__init__.y"><span class="n sig-name">y</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.unitroot.KPSS.__init__.y" title="Permalink to this definition">¶</a></dt><dd><p>The data to test for stationarity</p>
+<dt id="arch.unitroot.KPSS.__init__.y"><span class="n sig-name">y</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a></span></span><a class="headerlink" href="#arch.unitroot.KPSS.__init__.y" title="Permalink to this definition">¶</a></dt><dd><p>The data to test for stationarity</p>
 </dd>
 <dt id="arch.unitroot.KPSS.__init__.lags"><span class="n sig-name">lags</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.unitroot.KPSS.__init__.lags" title="Permalink to this definition">¶</a></dt><dd><p>The number of lags to use in the Newey-West estimator of the long-run
 covariance.  If omitted or None, the number of lags is calculated
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 <h1 id="arch-unitroot-phillipsperron">arch.unitroot.PhillipsPerron<a class="headerlink" href="#arch-unitroot-phillipsperron" title="Link to this heading">¶</a></h1>
 <dl class="py class objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.unitroot.PhillipsPerron">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.unitroot.</span></span><span class="sig-name descname"><span class="pre">PhillipsPerron</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.PhillipsPerron.__init__.y" title="arch.unitroot.PhillipsPerron.__init__.y (Python parameter) — The data to test for a unit root"><span class="n"><span class="pre">y</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.PhillipsPerron.__init__.lags" title="arch.unitroot.PhillipsPerron.__init__.lags (Python parameter) — The number of lags to use in the Newey-West estimator of the long-run covariance.  If omitted or None, the lag length is set automatically to 12 * (nobs/100) ** (1/4)"><span class="n"><span class="pre">lags</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.PhillipsPerron.__init__.trend" title="arch.unitroot.PhillipsPerron.__init__.trend (Python parameter) — The trend component to include in the test"><span class="n"><span class="pre">trend</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'n'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'c'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'ct'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;c&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.PhillipsPerron.__init__.test_type" title="arch.unitroot.PhillipsPerron.__init__.test_type (Python parameter) — The test to use when computing the test statistic. &quot;tau&quot; is based on the t-stat and &quot;rho&quot; uses a test based on nobs times the re-centered regression coefficient"><span class="n"><span class="pre">test_type</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'tau'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'rho'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;tau&#39;</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/unitroot/unitroot.html#PhillipsPerron"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.unitroot.PhillipsPerron" title="Link to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.unitroot.</span></span><span class="sig-name descname"><span class="pre">PhillipsPerron</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.PhillipsPerron.__init__.y" title="arch.unitroot.PhillipsPerron.__init__.y (Python parameter) — The data to test for a unit root"><span class="n"><span class="pre">y</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.PhillipsPerron.__init__.lags" title="arch.unitroot.PhillipsPerron.__init__.lags (Python parameter) — The number of lags to use in the Newey-West estimator of the long-run covariance.  If omitted or None, the lag length is set automatically to 12 * (nobs/100) ** (1/4)"><span class="n"><span class="pre">lags</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.PhillipsPerron.__init__.trend" title="arch.unitroot.PhillipsPerron.__init__.trend (Python parameter) — The trend component to include in the test"><span class="n"><span class="pre">trend</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'n'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'c'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'ct'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;c&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.PhillipsPerron.__init__.test_type" title="arch.unitroot.PhillipsPerron.__init__.test_type (Python parameter) — The test to use when computing the test statistic. &quot;tau&quot; is based on the t-stat and &quot;rho&quot; uses a test based on nobs times the re-centered regression coefficient"><span class="n"><span class="pre">test_type</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'tau'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'rho'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;tau&#39;</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/unitroot/unitroot.html#PhillipsPerron"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.unitroot.PhillipsPerron" title="Link to this definition">¶</a></dt>
 <dd><p>Phillips-Perron unit root test</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.unitroot.PhillipsPerron-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.unitroot.PhillipsPerron-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.unitroot.PhillipsPerron.__init__.y"><span class="n sig-name">y</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.unitroot.PhillipsPerron.__init__.y" title="Permalink to this definition">¶</a></dt><dd><p>The data to test for a unit root</p>
+<dt id="arch.unitroot.PhillipsPerron.__init__.y"><span class="n sig-name">y</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a></span></span><a class="headerlink" href="#arch.unitroot.PhillipsPerron.__init__.y" title="Permalink to this definition">¶</a></dt><dd><p>The data to test for a unit root</p>
 </dd>
 <dt id="arch.unitroot.PhillipsPerron.__init__.lags"><span class="n sig-name">lags</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.unitroot.PhillipsPerron.__init__.lags" title="Permalink to this definition">¶</a></dt><dd><p>The number of lags to use in the Newey-West estimator of the long-run
 covariance.  If omitted or None, the lag length is set automatically to
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diff --git a/devel/unitroot/generated/arch.unitroot.VarianceRatio.html b/devel/unitroot/generated/arch.unitroot.VarianceRatio.html
index 2c6a09687e..b90878ecca 100644
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 <h1 id="arch-unitroot-varianceratio">arch.unitroot.VarianceRatio<a class="headerlink" href="#arch-unitroot-varianceratio" title="Link to this heading">¶</a></h1>
 <dl class="py class objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.unitroot.VarianceRatio">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.unitroot.</span></span><span class="sig-name descname"><span class="pre">VarianceRatio</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.VarianceRatio.__init__.y" title="arch.unitroot.VarianceRatio.__init__.y (Python parameter) — The data to test for a random walk"><span class="n"><span class="pre">y</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.VarianceRatio.__init__.lags" title="arch.unitroot.VarianceRatio.__init__.lags (Python parameter) — The number of periods to used in the multi-period variance, which is the numerator of the test statistic.  Must be at least 2"><span class="n"><span class="pre">lags</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">2</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.VarianceRatio.__init__.trend" title="arch.unitroot.VarianceRatio.__init__.trend (Python parameter) — &quot;c&quot; allows for a non-zero drift in the random walk, while &quot;n&quot; requires that the increments to y are mean 0"><span class="n"><span class="pre">trend</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'n'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'c'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;c&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.VarianceRatio.__init__.debiased" title="arch.unitroot.VarianceRatio.__init__.debiased (Python parameter) — Indicates whether to use a debiased version of the test. Default is True. Only applicable if overlap is True."><span class="n"><span class="pre">debiased</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.VarianceRatio.__init__.robust" title="arch.unitroot.VarianceRatio.__init__.robust (Python parameter) — Indicates whether to use heteroskedasticity robust inference. Default is True."><span class="n"><span class="pre">robust</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.VarianceRatio.__init__.overlap" title="arch.unitroot.VarianceRatio.__init__.overlap (Python parameter) — Indicates whether to use all overlapping blocks.  Default is True.  If False, the number of observations in y minus 1 must be an exact multiple of lags.  If this condition is not satisfied, some values at the end of y will be discarded."><span class="n"><span class="pre">overlap</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/unitroot/unitroot.html#VarianceRatio"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.unitroot.VarianceRatio" title="Link to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.unitroot.</span></span><span class="sig-name descname"><span class="pre">VarianceRatio</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.VarianceRatio.__init__.y" title="arch.unitroot.VarianceRatio.__init__.y (Python parameter) — The data to test for a random walk"><span class="n"><span class="pre">y</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.VarianceRatio.__init__.lags" title="arch.unitroot.VarianceRatio.__init__.lags (Python parameter) — The number of periods to used in the multi-period variance, which is the numerator of the test statistic.  Must be at least 2"><span class="n"><span class="pre">lags</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">2</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.VarianceRatio.__init__.trend" title="arch.unitroot.VarianceRatio.__init__.trend (Python parameter) — &quot;c&quot; allows for a non-zero drift in the random walk, while &quot;n&quot; requires that the increments to y are mean 0"><span class="n"><span class="pre">trend</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'n'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'c'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;c&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.VarianceRatio.__init__.debiased" title="arch.unitroot.VarianceRatio.__init__.debiased (Python parameter) — Indicates whether to use a debiased version of the test. Default is True. Only applicable if overlap is True."><span class="n"><span class="pre">debiased</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.VarianceRatio.__init__.robust" title="arch.unitroot.VarianceRatio.__init__.robust (Python parameter) — Indicates whether to use heteroskedasticity robust inference. Default is True."><span class="n"><span class="pre">robust</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.VarianceRatio.__init__.overlap" title="arch.unitroot.VarianceRatio.__init__.overlap (Python parameter) — Indicates whether to use all overlapping blocks.  Default is True.  If False, the number of observations in y minus 1 must be an exact multiple of lags.  If this condition is not satisfied, some values at the end of y will be discarded."><span class="n"><span class="pre">overlap</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/unitroot/unitroot.html#VarianceRatio"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.unitroot.VarianceRatio" title="Link to this definition">¶</a></dt>
 <dd><p>Variance Ratio test of a random walk.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.unitroot.VarianceRatio-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.unitroot.VarianceRatio-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.unitroot.VarianceRatio.__init__.y"><span class="n sig-name">y</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.unitroot.VarianceRatio.__init__.y" title="Permalink to this definition">¶</a></dt><dd><p>The data to test for a random walk</p>
+<dt id="arch.unitroot.VarianceRatio.__init__.y"><span class="n sig-name">y</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a></span></span><a class="headerlink" href="#arch.unitroot.VarianceRatio.__init__.y" title="Permalink to this definition">¶</a></dt><dd><p>The data to test for a random walk</p>
 </dd>
 <dt id="arch.unitroot.VarianceRatio.__init__.lags"><span class="n sig-name">lags</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">2</span></code><a class="headerlink" href="#arch.unitroot.VarianceRatio.__init__.lags" title="Permalink to this definition">¶</a></dt><dd><p>The number of periods to used in the multi-period variance, which is
 the numerator of the test statistic.  Must be at least 2</p>
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diff --git a/devel/unitroot/generated/arch.unitroot.ZivotAndrews.html b/devel/unitroot/generated/arch.unitroot.ZivotAndrews.html
index cea0ed9969..b0e6766b47 100644
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@@ -1058,7 +1058,7 @@
 <h1 id="arch-unitroot-zivotandrews">arch.unitroot.ZivotAndrews<a class="headerlink" href="#arch-unitroot-zivotandrews" title="Link to this heading">¶</a></h1>
 <dl class="py class objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.unitroot.ZivotAndrews">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.unitroot.</span></span><span class="sig-name descname"><span class="pre">ZivotAndrews</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.ZivotAndrews.__init__.y" title="arch.unitroot.ZivotAndrews.__init__.y (Python parameter) — data series"><span class="n"><span class="pre">y</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.ZivotAndrews.__init__.lags" title="arch.unitroot.ZivotAndrews.__init__.lags (Python parameter) — The number of lags to use in the ADF regression.  If omitted or None, method is used to automatically select the lag length with no more than max_lags are included."><span class="n"><span class="pre">lags</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.ZivotAndrews.__init__.trend" title="arch.unitroot.ZivotAndrews.__init__.trend (Python parameter) — The trend component to include in the test"><span class="n"><span class="pre">trend</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'c'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'ct'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'t'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;c&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.ZivotAndrews.__init__.trim" title="arch.unitroot.ZivotAndrews.__init__.trim (Python parameter) — percentage of series at begin/end to exclude from break-period calculation in range [0, 0.333] (default=0.15)"><span class="n"><span class="pre">trim</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mf">0.15</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.ZivotAndrews.__init__.max_lags" title="arch.unitroot.ZivotAndrews.__init__.max_lags (Python parameter) — The maximum number of lags to use when selecting lag length"><span class="n"><span class="pre">max_lags</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.ZivotAndrews.__init__.method" title="arch.unitroot.ZivotAndrews.__init__.method (Python parameter) — The method to use when selecting the lag length"><span class="n"><span class="pre">method</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'aic'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'bic'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'t-stat'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;aic&#39;</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/unitroot/unitroot.html#ZivotAndrews"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.unitroot.ZivotAndrews" title="Link to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.unitroot.</span></span><span class="sig-name descname"><span class="pre">ZivotAndrews</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.ZivotAndrews.__init__.y" title="arch.unitroot.ZivotAndrews.__init__.y (Python parameter) — data series"><span class="n"><span class="pre">y</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.ZivotAndrews.__init__.lags" title="arch.unitroot.ZivotAndrews.__init__.lags (Python parameter) — The number of lags to use in the ADF regression.  If omitted or None, method is used to automatically select the lag length with no more than max_lags are included."><span class="n"><span class="pre">lags</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.ZivotAndrews.__init__.trend" title="arch.unitroot.ZivotAndrews.__init__.trend (Python parameter) — The trend component to include in the test"><span class="n"><span class="pre">trend</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'c'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'ct'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'t'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;c&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.ZivotAndrews.__init__.trim" title="arch.unitroot.ZivotAndrews.__init__.trim (Python parameter) — percentage of series at begin/end to exclude from break-period calculation in range [0, 0.333] (default=0.15)"><span class="n"><span class="pre">trim</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mf">0.15</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.ZivotAndrews.__init__.max_lags" title="arch.unitroot.ZivotAndrews.__init__.max_lags (Python parameter) — The maximum number of lags to use when selecting lag length"><span class="n"><span class="pre">max_lags</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.ZivotAndrews.__init__.method" title="arch.unitroot.ZivotAndrews.__init__.method (Python parameter) — The method to use when selecting the lag length"><span class="n"><span class="pre">method</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'aic'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'bic'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'t-stat'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;aic&#39;</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/unitroot/unitroot.html#ZivotAndrews"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.unitroot.ZivotAndrews" title="Link to this definition">¶</a></dt>
 <dd><p>Zivot-Andrews structural-break unit-root test</p>
 <p>The Zivot-Andrews test can be used to test for a unit root in a
 univariate process in the presence of serial correlation and a
@@ -1066,7 +1066,7 @@ <h1 id="arch-unitroot-zivotandrews">arch.unitroot.ZivotAndrews<a class="headerli
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.unitroot.ZivotAndrews-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.unitroot.ZivotAndrews-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.unitroot.ZivotAndrews.__init__.y"><span class="n sig-name">y</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.unitroot.ZivotAndrews.__init__.y" title="Permalink to this definition">¶</a></dt><dd><p>data series</p>
+<dt id="arch.unitroot.ZivotAndrews.__init__.y"><span class="n sig-name">y</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a></span></span><a class="headerlink" href="#arch.unitroot.ZivotAndrews.__init__.y" title="Permalink to this definition">¶</a></dt><dd><p>data series</p>
 </dd>
 <dt id="arch.unitroot.ZivotAndrews.__init__.lags"><span class="n sig-name">lags</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.unitroot.ZivotAndrews.__init__.lags" title="Permalink to this definition">¶</a></dt><dd><p>The number of lags to use in the ADF regression.  If omitted or None,
 <cite>method</cite> is used to automatically select the lag length with no more
@@ -1287,7 +1287,7 @@ <h1 id="arch-unitroot-zivotandrews">arch.unitroot.ZivotAndrews<a class="headerli
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diff --git a/devel/unitroot/generated/arch.unitroot.auto_bandwidth.html b/devel/unitroot/generated/arch.unitroot.auto_bandwidth.html
index 33384021d9..728637b52a 100644
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@@ -760,12 +760,12 @@
 <h1 id="arch-unitroot-auto-bandwidth">arch.unitroot.auto_bandwidth<a class="headerlink" href="#arch-unitroot-auto-bandwidth" title="Link to this heading">¶</a></h1>
 <dl class="py function objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.unitroot.auto_bandwidth">
-<span class="sig-prename descclassname"><span class="pre">arch.unitroot.</span></span><span class="sig-name descname"><span class="pre">auto_bandwidth</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.auto_bandwidth.y" title="arch.unitroot.auto_bandwidth.y (Python parameter) — Data on which to apply the bandwidth selection"><span class="n"><span class="pre">y</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.auto_bandwidth.kernel" title="arch.unitroot.auto_bandwidth.kernel (Python parameter) — The kernel function to use for selecting the bandwidth"><span class="n"><span class="pre">kernel</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'ba'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'bartlett'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'nw'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'pa'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'parzen'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'gallant'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'qs'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'andrews'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;ba&#39;</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a></span></span></span><a class="reference internal" href="../../_modules/arch/unitroot/unitroot.html#auto_bandwidth"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.unitroot.auto_bandwidth" title="Link to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">arch.unitroot.</span></span><span class="sig-name descname"><span class="pre">auto_bandwidth</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.auto_bandwidth.y" title="arch.unitroot.auto_bandwidth.y (Python parameter) — Data on which to apply the bandwidth selection"><span class="n"><span class="pre">y</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.auto_bandwidth.kernel" title="arch.unitroot.auto_bandwidth.kernel (Python parameter) — The kernel function to use for selecting the bandwidth"><span class="n"><span class="pre">kernel</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'ba'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'bartlett'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'nw'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'pa'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'parzen'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'gallant'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'qs'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'andrews'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;ba&#39;</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a></span></span></span><a class="reference internal" href="../../_modules/arch/unitroot/unitroot.html#auto_bandwidth"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.unitroot.auto_bandwidth" title="Link to this definition">¶</a></dt>
 <dd><p>Automatic bandwidth selection of Andrews (1991) and Newey &amp; West (1994).</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.unitroot.auto_bandwidth-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.unitroot.auto_bandwidth-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.unitroot.auto_bandwidth.y"><span class="n sig-name">y</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.unitroot.auto_bandwidth.y" title="Permalink to this definition">¶</a></dt><dd><p>Data on which to apply the bandwidth selection</p>
+<dt id="arch.unitroot.auto_bandwidth.y"><span class="n sig-name">y</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a></span></span><a class="headerlink" href="#arch.unitroot.auto_bandwidth.y" title="Permalink to this definition">¶</a></dt><dd><p>Data on which to apply the bandwidth selection</p>
 </dd>
 <dt id="arch.unitroot.auto_bandwidth.kernel"><span class="n sig-name">kernel</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><span class="s">'ba'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'bartlett'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'nw'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'pa'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'parzen'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'gallant'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'qs'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'andrews'</span></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;ba&#39;</span></code><a class="headerlink" href="#arch.unitroot.auto_bandwidth.kernel" title="Permalink to this definition">¶</a></dt><dd><p><p>The kernel function to use for selecting the bandwidth</p>
 <ul class="simple">
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diff --git a/devel/unitroot/generated/arch.unitroot.cointegration.CanonicalCointegratingReg.html b/devel/unitroot/generated/arch.unitroot.cointegration.CanonicalCointegratingReg.html
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@@ -15,7 +15,7 @@
     
   
       
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@@ -765,14 +765,14 @@
 <h1 id="arch-unitroot-cointegration-canonicalcointegratingreg">arch.unitroot.cointegration.CanonicalCointegratingReg<a class="headerlink" href="#arch-unitroot-cointegration-canonicalcointegratingreg" title="Link to this heading">¶</a></h1>
 <dl class="py class objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.unitroot.cointegration.CanonicalCointegratingReg">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.unitroot.cointegration.</span></span><span class="sig-name descname"><span class="pre">CanonicalCointegratingReg</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.CanonicalCointegratingReg.__init__.y" title="arch.unitroot.cointegration.CanonicalCointegratingReg.__init__.y (Python parameter) — The left-hand-side variable in the cointegrating regression."><span class="n"><span class="pre">y</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.CanonicalCointegratingReg.__init__.x" title="arch.unitroot.cointegration.CanonicalCointegratingReg.__init__.x (Python parameter) — The right-hand-side variables in the cointegrating regression."><span class="n"><span class="pre">x</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.CanonicalCointegratingReg.__init__.trend" title="arch.unitroot.cointegration.CanonicalCointegratingReg.__init__.trend (Python parameter) — Trend to include in the cointegrating regression. Trends are:"><span class="n"><span class="pre">trend</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'n'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'c'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'ct'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'ctt'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;c&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.CanonicalCointegratingReg.__init__.x_trend" title="arch.unitroot.cointegration.CanonicalCointegratingReg.__init__.x_trend (Python parameter) — Trends that affects affect the x-data but do not appear in the cointegrating regression. x_trend must be at least as large as trend, so that if trend is &quot;ct&quot;, x_trend must be either &quot;ct&quot; or &quot;ctt&quot;."><span class="n"><span class="pre">x_trend</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'n'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'c'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'ct'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'ctt'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/unitroot/cointegration.html#CanonicalCointegratingReg"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.unitroot.cointegration.CanonicalCointegratingReg" title="Link to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.unitroot.cointegration.</span></span><span class="sig-name descname"><span class="pre">CanonicalCointegratingReg</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.CanonicalCointegratingReg.__init__.y" title="arch.unitroot.cointegration.CanonicalCointegratingReg.__init__.y (Python parameter) — The left-hand-side variable in the cointegrating regression."><span class="n"><span class="pre">y</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.CanonicalCointegratingReg.__init__.x" title="arch.unitroot.cointegration.CanonicalCointegratingReg.__init__.x (Python parameter) — The right-hand-side variables in the cointegrating regression."><span class="n"><span class="pre">x</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><span class="pre">DataFrame</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.CanonicalCointegratingReg.__init__.trend" title="arch.unitroot.cointegration.CanonicalCointegratingReg.__init__.trend (Python parameter) — Trend to include in the cointegrating regression. Trends are:"><span class="n"><span class="pre">trend</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'n'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'c'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'ct'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'ctt'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;c&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.CanonicalCointegratingReg.__init__.x_trend" title="arch.unitroot.cointegration.CanonicalCointegratingReg.__init__.x_trend (Python parameter) — Trends that affects affect the x-data but do not appear in the cointegrating regression. x_trend must be at least as large as trend, so that if trend is &quot;ct&quot;, x_trend must be either &quot;ct&quot; or &quot;ctt&quot;."><span class="n"><span class="pre">x_trend</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'n'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'c'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'ct'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'ctt'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/unitroot/cointegration.html#CanonicalCointegratingReg"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.unitroot.cointegration.CanonicalCointegratingReg" title="Link to this definition">¶</a></dt>
 <dd><p>Canonical Cointegrating Regression cointegrating vector estimation.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.unitroot.cointegration.CanonicalCointegratingReg-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.unitroot.cointegration.CanonicalCointegratingReg-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.unitroot.cointegration.CanonicalCointegratingReg.__init__.y"><span class="n sig-name">y</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.unitroot.cointegration.CanonicalCointegratingReg.__init__.y" title="Permalink to this definition">¶</a></dt><dd><p>The left-hand-side variable in the cointegrating regression.</p>
+<dt id="arch.unitroot.cointegration.CanonicalCointegratingReg.__init__.y"><span class="n sig-name">y</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a></span></span><a class="headerlink" href="#arch.unitroot.cointegration.CanonicalCointegratingReg.__init__.y" title="Permalink to this definition">¶</a></dt><dd><p>The left-hand-side variable in the cointegrating regression.</p>
 </dd>
-<dt id="arch.unitroot.cointegration.CanonicalCointegratingReg.__init__.x"><span class="n sig-name">x</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a></span></span><a class="headerlink" href="#arch.unitroot.cointegration.CanonicalCointegratingReg.__init__.x" title="Permalink to this definition">¶</a></dt><dd><p>The right-hand-side variables in the cointegrating regression.</p>
+<dt id="arch.unitroot.cointegration.CanonicalCointegratingReg.__init__.x"><span class="n sig-name">x</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)">DataFrame</a></span></span><a class="headerlink" href="#arch.unitroot.cointegration.CanonicalCointegratingReg.__init__.x" title="Permalink to this definition">¶</a></dt><dd><p>The right-hand-side variables in the cointegrating regression.</p>
 </dd>
 <dt id="arch.unitroot.cointegration.CanonicalCointegratingReg.__init__.trend"><span class="n sig-name">trend</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><span class="s">'n'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'c'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'ct'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'ctt'</span></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;c&#39;</span></code><a class="headerlink" href="#arch.unitroot.cointegration.CanonicalCointegratingReg.__init__.trend" title="Permalink to this definition">¶</a></dt><dd><p><p>Trend to include in the cointegrating regression. Trends are:</p>
 <ul class="simple">
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index 4dec45b5f7..0dd43b92bb 100644
--- a/devel/unitroot/generated/arch.unitroot.cointegration.CointegrationAnalysisResults.html
+++ b/devel/unitroot/generated/arch.unitroot.cointegration.CointegrationAnalysisResults.html
@@ -15,7 +15,7 @@
     
   
       
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@@ -102,7 +102,7 @@
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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@@ -1026,7 +1026,7 @@
 <h1 id="arch-unitroot-cointegration-cointegrationanalysisresults">arch.unitroot.cointegration.CointegrationAnalysisResults<a class="headerlink" href="#arch-unitroot-cointegration-cointegrationanalysisresults" title="Link to this heading">¶</a></h1>
 <dl class="py class objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.unitroot.cointegration.CointegrationAnalysisResults">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.unitroot.cointegration.</span></span><span class="sig-name descname"><span class="pre">CointegrationAnalysisResults</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">params</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">cov</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">resid</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">omega_112</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">kernel_est</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="pre">CovarianceEstimator</span></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">num_x</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">trend</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'n'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'c'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'ct'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'ctt'</span></span></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">df_adjust</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">rsquared</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">rsquared_adj</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">estimator_type</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a></span></span></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/unitroot/cointegration.html#CointegrationAnalysisResults"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.unitroot.cointegration.CointegrationAnalysisResults" title="Link to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.unitroot.cointegration.</span></span><span class="sig-name descname"><span class="pre">CointegrationAnalysisResults</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">params</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">cov</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><span class="pre">DataFrame</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">resid</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">omega_112</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">kernel_est</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="pre">CovarianceEstimator</span></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">num_x</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">trend</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'n'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'c'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'ct'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'ctt'</span></span></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">df_adjust</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">rsquared</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">rsquared_adj</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">estimator_type</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a></span></span></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/unitroot/cointegration.html#CointegrationAnalysisResults"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.unitroot.cointegration.CointegrationAnalysisResults" title="Link to this definition">¶</a></dt>
 <dd><p class="rubric">Methods</p>
 <table class="autosummary longtable docutils data align-default">
 <tbody>
@@ -1192,7 +1192,7 @@ <h1 id="arch-unitroot-cointegration-cointegrationanalysisresults">arch.unitroot.
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diff --git a/devel/unitroot/generated/arch.unitroot.cointegration.DynamicOLS.html b/devel/unitroot/generated/arch.unitroot.cointegration.DynamicOLS.html
index 08e691c376..69bcdc165a 100644
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+++ b/devel/unitroot/generated/arch.unitroot.cointegration.DynamicOLS.html
@@ -15,7 +15,7 @@
     
   
       
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@@ -47,7 +47,7 @@
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@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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@@ -835,14 +835,14 @@
 <h1 id="arch-unitroot-cointegration-dynamicols">arch.unitroot.cointegration.DynamicOLS<a class="headerlink" href="#arch-unitroot-cointegration-dynamicols" title="Link to this heading">¶</a></h1>
 <dl class="py class objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.unitroot.cointegration.DynamicOLS">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.unitroot.cointegration.</span></span><span class="sig-name descname"><span class="pre">DynamicOLS</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.DynamicOLS.__init__.y" title="arch.unitroot.cointegration.DynamicOLS.__init__.y (Python parameter) — The left-hand-side variable in the cointegrating regression."><span class="n"><span class="pre">y</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.DynamicOLS.__init__.x" title="arch.unitroot.cointegration.DynamicOLS.__init__.x (Python parameter) — The right-hand-side variables in the cointegrating regression."><span class="n"><span class="pre">x</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.DynamicOLS.__init__.trend" title="arch.unitroot.cointegration.DynamicOLS.__init__.trend (Python parameter) — Trend to include in the cointegrating regression. Trends are:"><span class="n"><span class="pre">trend</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'n'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'c'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'ct'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'ctt'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;c&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.DynamicOLS.__init__.lags" title="arch.unitroot.cointegration.DynamicOLS.__init__.lags (Python parameter) — The number of lags to include in the model.  If None, the optimal number of lags is chosen using method."><span class="n"><span class="pre">lags</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.DynamicOLS.__init__.leads" title="arch.unitroot.cointegration.DynamicOLS.__init__.leads (Python parameter) — The number of leads to include in the model.  If None, the optimal number of leads is chosen using method."><span class="n"><span class="pre">leads</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.DynamicOLS.__init__.common" title="arch.unitroot.cointegration.DynamicOLS.__init__.common (Python parameter) — Flag indicating that lags and leads should be restricted to the same value. When common is None, lags must equal leads and max_lag must equal max_lead."><span class="n"><span class="pre">common</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.DynamicOLS.__init__.max_lag" title="arch.unitroot.cointegration.DynamicOLS.__init__.max_lag (Python parameter) — The maximum lag to consider. See Notes for value used when None."><span class="n"><span class="pre">max_lag</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.DynamicOLS.__init__.max_lead" title="arch.unitroot.cointegration.DynamicOLS.__init__.max_lead (Python parameter) — The maximum lead to consider. See Notes for value used when None."><span class="n"><span class="pre">max_lead</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.DynamicOLS.__init__.method" title="arch.unitroot.cointegration.DynamicOLS.__init__.method (Python parameter) — The method used to select lag length when lags or leads is None."><span class="n"><span class="pre">method</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'aic'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'bic'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'hqic'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;bic&#39;</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/unitroot/cointegration.html#DynamicOLS"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.unitroot.cointegration.DynamicOLS" title="Link to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.unitroot.cointegration.</span></span><span class="sig-name descname"><span class="pre">DynamicOLS</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.DynamicOLS.__init__.y" title="arch.unitroot.cointegration.DynamicOLS.__init__.y (Python parameter) — The left-hand-side variable in the cointegrating regression."><span class="n"><span class="pre">y</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.DynamicOLS.__init__.x" title="arch.unitroot.cointegration.DynamicOLS.__init__.x (Python parameter) — The right-hand-side variables in the cointegrating regression."><span class="n"><span class="pre">x</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><span class="pre">DataFrame</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.DynamicOLS.__init__.trend" title="arch.unitroot.cointegration.DynamicOLS.__init__.trend (Python parameter) — Trend to include in the cointegrating regression. Trends are:"><span class="n"><span class="pre">trend</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'n'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'c'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'ct'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'ctt'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;c&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.DynamicOLS.__init__.lags" title="arch.unitroot.cointegration.DynamicOLS.__init__.lags (Python parameter) — The number of lags to include in the model.  If None, the optimal number of lags is chosen using method."><span class="n"><span class="pre">lags</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.DynamicOLS.__init__.leads" title="arch.unitroot.cointegration.DynamicOLS.__init__.leads (Python parameter) — The number of leads to include in the model.  If None, the optimal number of leads is chosen using method."><span class="n"><span class="pre">leads</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.DynamicOLS.__init__.common" title="arch.unitroot.cointegration.DynamicOLS.__init__.common (Python parameter) — Flag indicating that lags and leads should be restricted to the same value. When common is None, lags must equal leads and max_lag must equal max_lead."><span class="n"><span class="pre">common</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.DynamicOLS.__init__.max_lag" title="arch.unitroot.cointegration.DynamicOLS.__init__.max_lag (Python parameter) — The maximum lag to consider. See Notes for value used when None."><span class="n"><span class="pre">max_lag</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.DynamicOLS.__init__.max_lead" title="arch.unitroot.cointegration.DynamicOLS.__init__.max_lead (Python parameter) — The maximum lead to consider. See Notes for value used when None."><span class="n"><span class="pre">max_lead</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.DynamicOLS.__init__.method" title="arch.unitroot.cointegration.DynamicOLS.__init__.method (Python parameter) — The method used to select lag length when lags or leads is None."><span class="n"><span class="pre">method</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'aic'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'bic'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'hqic'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;bic&#39;</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/unitroot/cointegration.html#DynamicOLS"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.unitroot.cointegration.DynamicOLS" title="Link to this definition">¶</a></dt>
 <dd><p>Dynamic OLS (DOLS) cointegrating vector estimation</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.unitroot.cointegration.DynamicOLS-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.unitroot.cointegration.DynamicOLS-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.unitroot.cointegration.DynamicOLS.__init__.y"><span class="n sig-name">y</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.unitroot.cointegration.DynamicOLS.__init__.y" title="Permalink to this definition">¶</a></dt><dd><p>The left-hand-side variable in the cointegrating regression.</p>
+<dt id="arch.unitroot.cointegration.DynamicOLS.__init__.y"><span class="n sig-name">y</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a></span></span><a class="headerlink" href="#arch.unitroot.cointegration.DynamicOLS.__init__.y" title="Permalink to this definition">¶</a></dt><dd><p>The left-hand-side variable in the cointegrating regression.</p>
 </dd>
-<dt id="arch.unitroot.cointegration.DynamicOLS.__init__.x"><span class="n sig-name">x</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a></span></span><a class="headerlink" href="#arch.unitroot.cointegration.DynamicOLS.__init__.x" title="Permalink to this definition">¶</a></dt><dd><p>The right-hand-side variables in the cointegrating regression.</p>
+<dt id="arch.unitroot.cointegration.DynamicOLS.__init__.x"><span class="n sig-name">x</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)">DataFrame</a></span></span><a class="headerlink" href="#arch.unitroot.cointegration.DynamicOLS.__init__.x" title="Permalink to this definition">¶</a></dt><dd><p>The right-hand-side variables in the cointegrating regression.</p>
 </dd>
 <dt id="arch.unitroot.cointegration.DynamicOLS.__init__.trend"><span class="n sig-name">trend</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><span class="s">'n'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'c'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'ct'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'ctt'</span></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;c&#39;</span></code><a class="headerlink" href="#arch.unitroot.cointegration.DynamicOLS.__init__.trend" title="Permalink to this definition">¶</a></dt><dd><p><p>Trend to include in the cointegrating regression. Trends are:</p>
 <ul class="simple">
@@ -1033,7 +1033,7 @@ <h1 id="arch-unitroot-cointegration-dynamicols">arch.unitroot.cointegration.Dyna
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@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration.DynamicOLSResults - arch v6.2.1 (+21)</title>
+        <title>arch.unitroot.cointegration.DynamicOLSResults - arch v6.2.1 (+23)</title>
       
     
 
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@@ -102,7 +102,7 @@
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-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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@@ -1294,16 +1294,16 @@
 <h1 id="arch-unitroot-cointegration-dynamicolsresults">arch.unitroot.cointegration.DynamicOLSResults<a class="headerlink" href="#arch-unitroot-cointegration-dynamicolsresults" title="Link to this heading">¶</a></h1>
 <dl class="py class objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.unitroot.cointegration.DynamicOLSResults">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.unitroot.cointegration.</span></span><span class="sig-name descname"><span class="pre">DynamicOLSResults</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.DynamicOLSResults.__init__.params" title="arch.unitroot.cointegration.DynamicOLSResults.__init__.params (Python parameter) — The estimated model parameters."><span class="n"><span class="pre">params</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.DynamicOLSResults.__init__.cov" title="arch.unitroot.cointegration.DynamicOLSResults.__init__.cov (Python parameter) — The estimated parameter covariance."><span class="n"><span class="pre">cov</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.DynamicOLSResults.__init__.resid" title="arch.unitroot.cointegration.DynamicOLSResults.__init__.resid (Python parameter) — The model residuals."><span class="n"><span class="pre">resid</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.DynamicOLSResults.__init__.lags" title="arch.unitroot.cointegration.DynamicOLSResults.__init__.lags (Python parameter) — The number of lags included in the model."><span class="n"><span class="pre">lags</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.DynamicOLSResults.__init__.leads" title="arch.unitroot.cointegration.DynamicOLSResults.__init__.leads (Python parameter) — The number of leads included in the model."><span class="n"><span class="pre">leads</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.DynamicOLSResults.__init__.cov_type" title="arch.unitroot.cointegration.DynamicOLSResults.__init__.cov_type (Python parameter) — The type of the parameter covariance estimator used."><span class="n"><span class="pre">cov_type</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.DynamicOLSResults.__init__.kernel_est" title="arch.unitroot.cointegration.DynamicOLSResults.__init__.kernel_est (Python parameter) — The covariance estimator instance used to estimate the parameter covariance."><span class="n"><span class="pre">kernel_est</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="pre">CovarianceEstimator</span></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">num_x</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">trend</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'n'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'c'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'ct'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'ctt'</span></span></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.DynamicOLSResults.__init__.reg_results" title="arch.unitroot.cointegration.DynamicOLSResults.__init__.reg_results (Python parameter) — Regression results from fitting statsmodels OLS."><span class="n"><span class="pre">reg_results</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://www.statsmodels.org/dev/generated/statsmodels.regression.linear_model.RegressionResults.html#statsmodels.regression.linear_model.RegressionResults" title="(in statsmodels 0.15.0 (+59) v0.15.0 (+59))"><span class="pre">RegressionResults</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.DynamicOLSResults.__init__.df_adjust" title="arch.unitroot.cointegration.DynamicOLSResults.__init__.df_adjust (Python parameter) — Whether to degree of freedom adjust the estimator."><span class="n"><span class="pre">df_adjust</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span></span></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/unitroot/cointegration.html#DynamicOLSResults"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.unitroot.cointegration.DynamicOLSResults" title="Link to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.unitroot.cointegration.</span></span><span class="sig-name descname"><span class="pre">DynamicOLSResults</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.DynamicOLSResults.__init__.params" title="arch.unitroot.cointegration.DynamicOLSResults.__init__.params (Python parameter) — The estimated model parameters."><span class="n"><span class="pre">params</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.DynamicOLSResults.__init__.cov" title="arch.unitroot.cointegration.DynamicOLSResults.__init__.cov (Python parameter) — The estimated parameter covariance."><span class="n"><span class="pre">cov</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><span class="pre">DataFrame</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.DynamicOLSResults.__init__.resid" title="arch.unitroot.cointegration.DynamicOLSResults.__init__.resid (Python parameter) — The model residuals."><span class="n"><span class="pre">resid</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.DynamicOLSResults.__init__.lags" title="arch.unitroot.cointegration.DynamicOLSResults.__init__.lags (Python parameter) — The number of lags included in the model."><span class="n"><span class="pre">lags</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.DynamicOLSResults.__init__.leads" title="arch.unitroot.cointegration.DynamicOLSResults.__init__.leads (Python parameter) — The number of leads included in the model."><span class="n"><span class="pre">leads</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.DynamicOLSResults.__init__.cov_type" title="arch.unitroot.cointegration.DynamicOLSResults.__init__.cov_type (Python parameter) — The type of the parameter covariance estimator used."><span class="n"><span class="pre">cov_type</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.DynamicOLSResults.__init__.kernel_est" title="arch.unitroot.cointegration.DynamicOLSResults.__init__.kernel_est (Python parameter) — The covariance estimator instance used to estimate the parameter covariance."><span class="n"><span class="pre">kernel_est</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="pre">CovarianceEstimator</span></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">num_x</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">trend</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'n'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'c'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'ct'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'ctt'</span></span></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.DynamicOLSResults.__init__.reg_results" title="arch.unitroot.cointegration.DynamicOLSResults.__init__.reg_results (Python parameter) — Regression results from fitting statsmodels OLS."><span class="n"><span class="pre">reg_results</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://www.statsmodels.org/dev/generated/statsmodels.regression.linear_model.RegressionResults.html#statsmodels.regression.linear_model.RegressionResults" title="(in statsmodels 0.15.0 (+132) v0.15.0 (+132))"><span class="pre">RegressionResults</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.DynamicOLSResults.__init__.df_adjust" title="arch.unitroot.cointegration.DynamicOLSResults.__init__.df_adjust (Python parameter) — Whether to degree of freedom adjust the estimator."><span class="n"><span class="pre">df_adjust</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span></span></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/unitroot/cointegration.html#DynamicOLSResults"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.unitroot.cointegration.DynamicOLSResults" title="Link to this definition">¶</a></dt>
 <dd><p>Estimation results for Dynamic OLS models</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.unitroot.cointegration.DynamicOLSResults-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.unitroot.cointegration.DynamicOLSResults-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.unitroot.cointegration.DynamicOLSResults.__init__.params"><span class="n sig-name">params</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.unitroot.cointegration.DynamicOLSResults.__init__.params" title="Permalink to this definition">¶</a></dt><dd><p>The estimated model parameters.</p>
+<dt id="arch.unitroot.cointegration.DynamicOLSResults.__init__.params"><span class="n sig-name">params</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a></span></span><a class="headerlink" href="#arch.unitroot.cointegration.DynamicOLSResults.__init__.params" title="Permalink to this definition">¶</a></dt><dd><p>The estimated model parameters.</p>
 </dd>
-<dt id="arch.unitroot.cointegration.DynamicOLSResults.__init__.cov"><span class="n sig-name">cov</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a></span></span><a class="headerlink" href="#arch.unitroot.cointegration.DynamicOLSResults.__init__.cov" title="Permalink to this definition">¶</a></dt><dd><p>The estimated parameter covariance.</p>
+<dt id="arch.unitroot.cointegration.DynamicOLSResults.__init__.cov"><span class="n sig-name">cov</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)">DataFrame</a></span></span><a class="headerlink" href="#arch.unitroot.cointegration.DynamicOLSResults.__init__.cov" title="Permalink to this definition">¶</a></dt><dd><p>The estimated parameter covariance.</p>
 </dd>
-<dt id="arch.unitroot.cointegration.DynamicOLSResults.__init__.resid"><span class="n sig-name">resid</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.unitroot.cointegration.DynamicOLSResults.__init__.resid" title="Permalink to this definition">¶</a></dt><dd><p>The model residuals.</p>
+<dt id="arch.unitroot.cointegration.DynamicOLSResults.__init__.resid"><span class="n sig-name">resid</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a></span></span><a class="headerlink" href="#arch.unitroot.cointegration.DynamicOLSResults.__init__.resid" title="Permalink to this definition">¶</a></dt><dd><p>The model residuals.</p>
 </dd>
 <dt id="arch.unitroot.cointegration.DynamicOLSResults.__init__.lags"><span class="n sig-name">lags</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a></span></span><a class="headerlink" href="#arch.unitroot.cointegration.DynamicOLSResults.__init__.lags" title="Permalink to this definition">¶</a></dt><dd><p>The number of lags included in the model.</p>
 </dd>
@@ -1314,7 +1314,7 @@ <h1 id="arch-unitroot-cointegration-dynamicolsresults">arch.unitroot.cointegrati
 <dt id="arch.unitroot.cointegration.DynamicOLSResults.__init__.kernel_est"><span class="n sig-name">kernel_est</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype">CovarianceEstimator</span></span><a class="headerlink" href="#arch.unitroot.cointegration.DynamicOLSResults.__init__.kernel_est" title="Permalink to this definition">¶</a></dt><dd><p>The covariance estimator instance used to estimate the parameter
 covariance.</p>
 </dd>
-<dt id="arch.unitroot.cointegration.DynamicOLSResults.__init__.reg_results"><span class="n sig-name">reg_results</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://www.statsmodels.org/dev/generated/statsmodels.regression.linear_model.RegressionResults.html#statsmodels.regression.linear_model.RegressionResults" title="(in statsmodels 0.15.0 (+59) v0.15.0 (+59))">RegressionResults</a></span></span><a class="headerlink" href="#arch.unitroot.cointegration.DynamicOLSResults.__init__.reg_results" title="Permalink to this definition">¶</a></dt><dd><p>Regression results from fitting statsmodels OLS.</p>
+<dt id="arch.unitroot.cointegration.DynamicOLSResults.__init__.reg_results"><span class="n sig-name">reg_results</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://www.statsmodels.org/dev/generated/statsmodels.regression.linear_model.RegressionResults.html#statsmodels.regression.linear_model.RegressionResults" title="(in statsmodels 0.15.0 (+132) v0.15.0 (+132))">RegressionResults</a></span></span><a class="headerlink" href="#arch.unitroot.cointegration.DynamicOLSResults.__init__.reg_results" title="Permalink to this definition">¶</a></dt><dd><p>Regression results from fitting statsmodels OLS.</p>
 </dd>
 <dt id="arch.unitroot.cointegration.DynamicOLSResults.__init__.df_adjust"><span class="n sig-name">df_adjust</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)">bool</a></span></span><a class="headerlink" href="#arch.unitroot.cointegration.DynamicOLSResults.__init__.df_adjust" title="Permalink to this definition">¶</a></dt><dd><p>Whether to degree of freedom adjust the estimator.</p>
 </dd>
@@ -1501,7 +1501,7 @@ <h1 id="arch-unitroot-cointegration-dynamicolsresults">arch.unitroot.cointegrati
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diff --git a/devel/unitroot/generated/arch.unitroot.cointegration.EngleGrangerTestResults.html b/devel/unitroot/generated/arch.unitroot.cointegration.EngleGrangerTestResults.html
index b82c9d61c4..e95a6bce74 100644
--- a/devel/unitroot/generated/arch.unitroot.cointegration.EngleGrangerTestResults.html
+++ b/devel/unitroot/generated/arch.unitroot.cointegration.EngleGrangerTestResults.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration.EngleGrangerTestResults - arch v6.2.1 (+21)</title>
+        <title>arch.unitroot.cointegration.EngleGrangerTestResults - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
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         <link rel="stylesheet" type="text/css" href="../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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@@ -1228,7 +1228,7 @@
 <h1 id="arch-unitroot-cointegration-englegrangertestresults">arch.unitroot.cointegration.EngleGrangerTestResults<a class="headerlink" href="#arch-unitroot-cointegration-englegrangertestresults" title="Link to this heading">¶</a></h1>
 <dl class="py class objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.unitroot.cointegration.EngleGrangerTestResults">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.unitroot.cointegration.</span></span><span class="sig-name descname"><span class="pre">EngleGrangerTestResults</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.EngleGrangerTestResults.__init__.stat" title="arch.unitroot.cointegration.EngleGrangerTestResults.__init__.stat (Python parameter) — The Engle-Granger test statistic."><span class="n"><span class="pre">stat</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.EngleGrangerTestResults.__init__.pvalue" title="arch.unitroot.cointegration.EngleGrangerTestResults.__init__.pvalue (Python parameter) — The pvalue of the Engle-Granger test statistic."><span class="n"><span class="pre">pvalue</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.EngleGrangerTestResults.__init__.crit_vals" title="arch.unitroot.cointegration.EngleGrangerTestResults.__init__.crit_vals (Python parameter) — The critical values of the Engle-Granger specific to the sample size and model dimension."><span class="n"><span class="pre">crit_vals</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.EngleGrangerTestResults.__init__.null" title="arch.unitroot.cointegration.EngleGrangerTestResults.__init__.null (Python parameter) — The null hypothesis."><span class="n"><span class="pre">null</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;No Cointegration&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.EngleGrangerTestResults.__init__.alternative" title="arch.unitroot.cointegration.EngleGrangerTestResults.__init__.alternative (Python parameter) — The alternative hypothesis."><span class="n"><span class="pre">alternative</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;Cointegration&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.EngleGrangerTestResults.__init__.trend" title="arch.unitroot.cointegration.EngleGrangerTestResults.__init__.trend (Python parameter) — The model's trend description."><span class="n"><span class="pre">trend</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;c&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.EngleGrangerTestResults.__init__.order" title="arch.unitroot.cointegration.EngleGrangerTestResults.__init__.order (Python parameter) — The number of stochastic trends in the null distribution."><span class="n"><span class="pre">order</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">2</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.EngleGrangerTestResults.__init__.adf" title="arch.unitroot.cointegration.EngleGrangerTestResults.__init__.adf (Python parameter) — The ADF instance used to perform the test and lag selection."><span class="n"><span class="pre">adf</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference internal" href="arch.unitroot.ADF.html#arch.unitroot.ADF" title="arch.unitroot.unitroot.ADF (Python class)"><span class="pre">ADF</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.EngleGrangerTestResults.__init__.xsection" title="arch.unitroot.cointegration.EngleGrangerTestResults.__init__.xsection (Python parameter) — The OLS results used in the cross-sectional regression."><span class="n"><span class="pre">xsection</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://www.statsmodels.org/dev/generated/statsmodels.regression.linear_model.RegressionResults.html#statsmodels.regression.linear_model.RegressionResults" title="(in statsmodels 0.15.0 (+59) v0.15.0 (+59))"><span class="pre">RegressionResults</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/unitroot/_engle_granger.html#EngleGrangerTestResults"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.unitroot.cointegration.EngleGrangerTestResults" title="Link to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.unitroot.cointegration.</span></span><span class="sig-name descname"><span class="pre">EngleGrangerTestResults</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.EngleGrangerTestResults.__init__.stat" title="arch.unitroot.cointegration.EngleGrangerTestResults.__init__.stat (Python parameter) — The Engle-Granger test statistic."><span class="n"><span class="pre">stat</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.EngleGrangerTestResults.__init__.pvalue" title="arch.unitroot.cointegration.EngleGrangerTestResults.__init__.pvalue (Python parameter) — The pvalue of the Engle-Granger test statistic."><span class="n"><span class="pre">pvalue</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.EngleGrangerTestResults.__init__.crit_vals" title="arch.unitroot.cointegration.EngleGrangerTestResults.__init__.crit_vals (Python parameter) — The critical values of the Engle-Granger specific to the sample size and model dimension."><span class="n"><span class="pre">crit_vals</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.EngleGrangerTestResults.__init__.null" title="arch.unitroot.cointegration.EngleGrangerTestResults.__init__.null (Python parameter) — The null hypothesis."><span class="n"><span class="pre">null</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;No Cointegration&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.EngleGrangerTestResults.__init__.alternative" title="arch.unitroot.cointegration.EngleGrangerTestResults.__init__.alternative (Python parameter) — The alternative hypothesis."><span class="n"><span class="pre">alternative</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;Cointegration&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.EngleGrangerTestResults.__init__.trend" title="arch.unitroot.cointegration.EngleGrangerTestResults.__init__.trend (Python parameter) — The model's trend description."><span class="n"><span class="pre">trend</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;c&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.EngleGrangerTestResults.__init__.order" title="arch.unitroot.cointegration.EngleGrangerTestResults.__init__.order (Python parameter) — The number of stochastic trends in the null distribution."><span class="n"><span class="pre">order</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">2</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.EngleGrangerTestResults.__init__.adf" title="arch.unitroot.cointegration.EngleGrangerTestResults.__init__.adf (Python parameter) — The ADF instance used to perform the test and lag selection."><span class="n"><span class="pre">adf</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference internal" href="arch.unitroot.ADF.html#arch.unitroot.ADF" title="arch.unitroot.unitroot.ADF (Python class)"><span class="pre">ADF</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.EngleGrangerTestResults.__init__.xsection" title="arch.unitroot.cointegration.EngleGrangerTestResults.__init__.xsection (Python parameter) — The OLS results used in the cross-sectional regression."><span class="n"><span class="pre">xsection</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://www.statsmodels.org/dev/generated/statsmodels.regression.linear_model.RegressionResults.html#statsmodels.regression.linear_model.RegressionResults" title="(in statsmodels 0.15.0 (+132) v0.15.0 (+132))"><span class="pre">RegressionResults</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/unitroot/_engle_granger.html#EngleGrangerTestResults"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.unitroot.cointegration.EngleGrangerTestResults" title="Link to this definition">¶</a></dt>
 <dd><p>Results class for Engle-Granger cointegration tests.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.unitroot.cointegration.EngleGrangerTestResults-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.unitroot.cointegration.EngleGrangerTestResults-parameters" title="Permalink to this headline">¶</a></dt>
@@ -1237,7 +1237,7 @@ <h1 id="arch-unitroot-cointegration-englegrangertestresults">arch.unitroot.coint
 </dd>
 <dt id="arch.unitroot.cointegration.EngleGrangerTestResults.__init__.pvalue"><span class="n sig-name">pvalue</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a></span></span><a class="headerlink" href="#arch.unitroot.cointegration.EngleGrangerTestResults.__init__.pvalue" title="Permalink to this definition">¶</a></dt><dd><p>The pvalue of the Engle-Granger test statistic.</p>
 </dd>
-<dt id="arch.unitroot.cointegration.EngleGrangerTestResults.__init__.crit_vals"><span class="n sig-name">crit_vals</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.unitroot.cointegration.EngleGrangerTestResults.__init__.crit_vals" title="Permalink to this definition">¶</a></dt><dd><p>The critical values of the Engle-Granger specific to the sample size
+<dt id="arch.unitroot.cointegration.EngleGrangerTestResults.__init__.crit_vals"><span class="n sig-name">crit_vals</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a></span></span><a class="headerlink" href="#arch.unitroot.cointegration.EngleGrangerTestResults.__init__.crit_vals" title="Permalink to this definition">¶</a></dt><dd><p>The critical values of the Engle-Granger specific to the sample size
 and model dimension.</p>
 </dd>
 <dt id="arch.unitroot.cointegration.EngleGrangerTestResults.__init__.null"><span class="n sig-name">null</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)">str</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;No Cointegration&#39;</span></code><a class="headerlink" href="#arch.unitroot.cointegration.EngleGrangerTestResults.__init__.null" title="Permalink to this definition">¶</a></dt><dd><p>The null hypothesis.</p>
@@ -1250,7 +1250,7 @@ <h1 id="arch-unitroot-cointegration-englegrangertestresults">arch.unitroot.coint
 </dd>
 <dt id="arch.unitroot.cointegration.EngleGrangerTestResults.__init__.adf"><span class="n sig-name">adf</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference internal" href="arch.unitroot.ADF.html#arch.unitroot.ADF" title="arch.unitroot.unitroot.ADF (Python class)">ADF</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.unitroot.cointegration.EngleGrangerTestResults.__init__.adf" title="Permalink to this definition">¶</a></dt><dd><p>The ADF instance used to perform the test and lag selection.</p>
 </dd>
-<dt id="arch.unitroot.cointegration.EngleGrangerTestResults.__init__.xsection"><span class="n sig-name">xsection</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://www.statsmodels.org/dev/generated/statsmodels.regression.linear_model.RegressionResults.html#statsmodels.regression.linear_model.RegressionResults" title="(in statsmodels 0.15.0 (+59) v0.15.0 (+59))">RegressionResults</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.unitroot.cointegration.EngleGrangerTestResults.__init__.xsection" title="Permalink to this definition">¶</a></dt><dd><p>The OLS results used in the cross-sectional regression.</p>
+<dt id="arch.unitroot.cointegration.EngleGrangerTestResults.__init__.xsection"><span class="n sig-name">xsection</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://www.statsmodels.org/dev/generated/statsmodels.regression.linear_model.RegressionResults.html#statsmodels.regression.linear_model.RegressionResults" title="(in statsmodels 0.15.0 (+132) v0.15.0 (+132))">RegressionResults</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.unitroot.cointegration.EngleGrangerTestResults.__init__.xsection" title="Permalink to this definition">¶</a></dt><dd><p>The OLS results used in the cross-sectional regression.</p>
 </dd>
 </dl>
 </dd>
@@ -1426,7 +1426,7 @@ <h1 id="arch-unitroot-cointegration-englegrangertestresults">arch.unitroot.coint
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diff --git a/devel/unitroot/generated/arch.unitroot.cointegration.FullyModifiedOLS.html b/devel/unitroot/generated/arch.unitroot.cointegration.FullyModifiedOLS.html
index 707765afb2..a309f823ae 100644
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@@ -765,14 +765,14 @@
 <h1 id="arch-unitroot-cointegration-fullymodifiedols">arch.unitroot.cointegration.FullyModifiedOLS<a class="headerlink" href="#arch-unitroot-cointegration-fullymodifiedols" title="Link to this heading">¶</a></h1>
 <dl class="py class objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.unitroot.cointegration.FullyModifiedOLS">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.unitroot.cointegration.</span></span><span class="sig-name descname"><span class="pre">FullyModifiedOLS</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.FullyModifiedOLS.__init__.y" title="arch.unitroot.cointegration.FullyModifiedOLS.__init__.y (Python parameter) — The left-hand-side variable in the cointegrating regression."><span class="n"><span class="pre">y</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.FullyModifiedOLS.__init__.x" title="arch.unitroot.cointegration.FullyModifiedOLS.__init__.x (Python parameter) — The right-hand-side variables in the cointegrating regression."><span class="n"><span class="pre">x</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.FullyModifiedOLS.__init__.trend" title="arch.unitroot.cointegration.FullyModifiedOLS.__init__.trend (Python parameter) — Trend to include in the cointegrating regression. Trends are:"><span class="n"><span class="pre">trend</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'n'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'c'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'ct'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'ctt'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;c&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.FullyModifiedOLS.__init__.x_trend" title="arch.unitroot.cointegration.FullyModifiedOLS.__init__.x_trend (Python parameter) — Trends that affects affect the x-data but do not appear in the cointegrating regression. x_trend must be at least as large as trend, so that if trend is &quot;ct&quot;, x_trend must be either &quot;ct&quot; or &quot;ctt&quot;."><span class="n"><span class="pre">x_trend</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'n'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'c'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'ct'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'ctt'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/unitroot/cointegration.html#FullyModifiedOLS"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.unitroot.cointegration.FullyModifiedOLS" title="Link to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.unitroot.cointegration.</span></span><span class="sig-name descname"><span class="pre">FullyModifiedOLS</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.FullyModifiedOLS.__init__.y" title="arch.unitroot.cointegration.FullyModifiedOLS.__init__.y (Python parameter) — The left-hand-side variable in the cointegrating regression."><span class="n"><span class="pre">y</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.FullyModifiedOLS.__init__.x" title="arch.unitroot.cointegration.FullyModifiedOLS.__init__.x (Python parameter) — The right-hand-side variables in the cointegrating regression."><span class="n"><span class="pre">x</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><span class="pre">DataFrame</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.FullyModifiedOLS.__init__.trend" title="arch.unitroot.cointegration.FullyModifiedOLS.__init__.trend (Python parameter) — Trend to include in the cointegrating regression. Trends are:"><span class="n"><span class="pre">trend</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'n'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'c'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'ct'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'ctt'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;c&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.FullyModifiedOLS.__init__.x_trend" title="arch.unitroot.cointegration.FullyModifiedOLS.__init__.x_trend (Python parameter) — Trends that affects affect the x-data but do not appear in the cointegrating regression. x_trend must be at least as large as trend, so that if trend is &quot;ct&quot;, x_trend must be either &quot;ct&quot; or &quot;ctt&quot;."><span class="n"><span class="pre">x_trend</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'n'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'c'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'ct'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'ctt'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/unitroot/cointegration.html#FullyModifiedOLS"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.unitroot.cointegration.FullyModifiedOLS" title="Link to this definition">¶</a></dt>
 <dd><p>Fully Modified OLS cointegrating vector estimation.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.unitroot.cointegration.FullyModifiedOLS-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.unitroot.cointegration.FullyModifiedOLS-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.unitroot.cointegration.FullyModifiedOLS.__init__.y"><span class="n sig-name">y</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.unitroot.cointegration.FullyModifiedOLS.__init__.y" title="Permalink to this definition">¶</a></dt><dd><p>The left-hand-side variable in the cointegrating regression.</p>
+<dt id="arch.unitroot.cointegration.FullyModifiedOLS.__init__.y"><span class="n sig-name">y</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a></span></span><a class="headerlink" href="#arch.unitroot.cointegration.FullyModifiedOLS.__init__.y" title="Permalink to this definition">¶</a></dt><dd><p>The left-hand-side variable in the cointegrating regression.</p>
 </dd>
-<dt id="arch.unitroot.cointegration.FullyModifiedOLS.__init__.x"><span class="n sig-name">x</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a></span></span><a class="headerlink" href="#arch.unitroot.cointegration.FullyModifiedOLS.__init__.x" title="Permalink to this definition">¶</a></dt><dd><p>The right-hand-side variables in the cointegrating regression.</p>
+<dt id="arch.unitroot.cointegration.FullyModifiedOLS.__init__.x"><span class="n sig-name">x</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)">DataFrame</a></span></span><a class="headerlink" href="#arch.unitroot.cointegration.FullyModifiedOLS.__init__.x" title="Permalink to this definition">¶</a></dt><dd><p>The right-hand-side variables in the cointegrating regression.</p>
 </dd>
 <dt id="arch.unitroot.cointegration.FullyModifiedOLS.__init__.trend"><span class="n sig-name">trend</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><span class="s">'n'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'c'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'ct'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'ctt'</span></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;c&#39;</span></code><a class="headerlink" href="#arch.unitroot.cointegration.FullyModifiedOLS.__init__.trend" title="Permalink to this definition">¶</a></dt><dd><p><p>Trend to include in the cointegrating regression. Trends are:</p>
 <ul class="simple">
@@ -962,7 +962,7 @@ <h1 id="arch-unitroot-cointegration-fullymodifiedols">arch.unitroot.cointegratio
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diff --git a/devel/unitroot/generated/arch.unitroot.cointegration.PhillipsOuliarisTestResults.html b/devel/unitroot/generated/arch.unitroot.cointegration.PhillipsOuliarisTestResults.html
index 67df041d87..4fda3cc6c1 100644
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@@ -15,7 +15,7 @@
     
   
       
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@@ -1048,7 +1048,7 @@
 <h1 id="arch-unitroot-cointegration-phillipsouliaristestresults">arch.unitroot.cointegration.PhillipsOuliarisTestResults<a class="headerlink" href="#arch-unitroot-cointegration-phillipsouliaristestresults" title="Link to this heading">¶</a></h1>
 <dl class="py class objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.unitroot.cointegration.PhillipsOuliarisTestResults">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.unitroot.cointegration.</span></span><span class="sig-name descname"><span class="pre">PhillipsOuliarisTestResults</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">stat</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">pvalue</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">crit_vals</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">null</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;No Cointegration&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">alternative</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;Cointegration&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">trend</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;c&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">order</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">2</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">xsection</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://www.statsmodels.org/dev/generated/statsmodels.regression.linear_model.RegressionResults.html#statsmodels.regression.linear_model.RegressionResults" title="(in statsmodels 0.15.0 (+59) v0.15.0 (+59))"><span class="pre">RegressionResults</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">test_type</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;Za&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">kernel_est</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="pre">CovarianceEstimator</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">rho</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mf">0.0</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/unitroot/_phillips_ouliaris.html#PhillipsOuliarisTestResults"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.unitroot.cointegration.PhillipsOuliarisTestResults" title="Link to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.unitroot.cointegration.</span></span><span class="sig-name descname"><span class="pre">PhillipsOuliarisTestResults</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">stat</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">pvalue</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">crit_vals</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">null</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;No Cointegration&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">alternative</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;Cointegration&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">trend</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;c&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">order</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">2</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">xsection</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://www.statsmodels.org/dev/generated/statsmodels.regression.linear_model.RegressionResults.html#statsmodels.regression.linear_model.RegressionResults" title="(in statsmodels 0.15.0 (+132) v0.15.0 (+132))"><span class="pre">RegressionResults</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">test_type</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;Za&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">kernel_est</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="pre">CovarianceEstimator</span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">rho</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mf">0.0</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/unitroot/_phillips_ouliaris.html#PhillipsOuliarisTestResults"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.unitroot.cointegration.PhillipsOuliarisTestResults" title="Link to this definition">¶</a></dt>
 <dd><p class="rubric">Methods</p>
 <table class="autosummary longtable docutils data align-default">
 <tbody>
@@ -1217,7 +1217,7 @@ <h1 id="arch-unitroot-cointegration-phillipsouliaristestresults">arch.unitroot.c
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diff --git a/devel/unitroot/generated/arch.unitroot.cointegration.engle_granger.html b/devel/unitroot/generated/arch.unitroot.cointegration.engle_granger.html
index 179c98ed62..81f7de28ad 100644
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 <h1 id="arch-unitroot-cointegration-engle-granger">arch.unitroot.cointegration.engle_granger<a class="headerlink" href="#arch-unitroot-cointegration-engle-granger" title="Link to this heading">¶</a></h1>
 <dl class="py function objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.unitroot.cointegration.engle_granger">
-<span class="sig-prename descclassname"><span class="pre">arch.unitroot.cointegration.</span></span><span class="sig-name descname"><span class="pre">engle_granger</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.engle_granger.y" title="arch.unitroot.cointegration.engle_granger.y (Python parameter) — The left-hand-side variable in the cointegrating regression."><span class="n"><span class="pre">y</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.engle_granger.x" title="arch.unitroot.cointegration.engle_granger.x (Python parameter) — The right-hand-side variables in the cointegrating regression."><span class="n"><span class="pre">x</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.engle_granger.trend" title="arch.unitroot.cointegration.engle_granger.trend (Python parameter) — Trend to include in the cointegrating regression. Trends are:"><span class="n"><span class="pre">trend</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'n'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'c'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'ct'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'ctt'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;c&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.engle_granger.lags" title="arch.unitroot.cointegration.engle_granger.lags (Python parameter) — The number of lagged differences to include in the Augmented Dickey-Fuller test used on the residuals of the"><span class="n"><span class="pre">lags</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.engle_granger.max_lags" title="arch.unitroot.cointegration.engle_granger.max_lags (Python parameter) — The maximum number of lags to consider when using automatic lag-length in the Augmented Dickey-Fuller regression."><span class="n"><span class="pre">max_lags</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.engle_granger.method" title="arch.unitroot.cointegration.engle_granger.method (Python parameter) — The method used to select the number of lags included in the Augmented Dickey-Fuller regression."><span class="n"><span class="pre">method</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'aic'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'bic'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'t-stat'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;bic&#39;</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference internal" href="arch.unitroot.cointegration.EngleGrangerTestResults.html#arch.unitroot.cointegration.EngleGrangerTestResults" title="arch.unitroot._engle_granger.EngleGrangerTestResults (Python class)"><span class="pre">EngleGrangerTestResults</span></a></span></span></span><a class="reference internal" href="../../_modules/arch/unitroot/_engle_granger.html#engle_granger"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.unitroot.cointegration.engle_granger" title="Link to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">arch.unitroot.cointegration.</span></span><span class="sig-name descname"><span class="pre">engle_granger</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.engle_granger.y" title="arch.unitroot.cointegration.engle_granger.y (Python parameter) — The left-hand-side variable in the cointegrating regression."><span class="n"><span class="pre">y</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.engle_granger.x" title="arch.unitroot.cointegration.engle_granger.x (Python parameter) — The right-hand-side variables in the cointegrating regression."><span class="n"><span class="pre">x</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><span class="pre">DataFrame</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.engle_granger.trend" title="arch.unitroot.cointegration.engle_granger.trend (Python parameter) — Trend to include in the cointegrating regression. Trends are:"><span class="n"><span class="pre">trend</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'n'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'c'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'ct'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'ctt'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;c&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.engle_granger.lags" title="arch.unitroot.cointegration.engle_granger.lags (Python parameter) — The number of lagged differences to include in the Augmented Dickey-Fuller test used on the residuals of the"><span class="n"><span class="pre">lags</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.engle_granger.max_lags" title="arch.unitroot.cointegration.engle_granger.max_lags (Python parameter) — The maximum number of lags to consider when using automatic lag-length in the Augmented Dickey-Fuller regression."><span class="n"><span class="pre">max_lags</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.engle_granger.method" title="arch.unitroot.cointegration.engle_granger.method (Python parameter) — The method used to select the number of lags included in the Augmented Dickey-Fuller regression."><span class="n"><span class="pre">method</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'aic'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'bic'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'t-stat'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;bic&#39;</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference internal" href="arch.unitroot.cointegration.EngleGrangerTestResults.html#arch.unitroot.cointegration.EngleGrangerTestResults" title="arch.unitroot._engle_granger.EngleGrangerTestResults (Python class)"><span class="pre">EngleGrangerTestResults</span></a></span></span></span><a class="reference internal" href="../../_modules/arch/unitroot/_engle_granger.html#engle_granger"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.unitroot.cointegration.engle_granger" title="Link to this definition">¶</a></dt>
 <dd><p>Test for cointegration within a set of time series.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.unitroot.cointegration.engle_granger-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.unitroot.cointegration.engle_granger-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.unitroot.cointegration.engle_granger.y"><span class="n sig-name">y</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.unitroot.cointegration.engle_granger.y" title="Permalink to this definition">¶</a></dt><dd><p>The left-hand-side variable in the cointegrating regression.</p>
+<dt id="arch.unitroot.cointegration.engle_granger.y"><span class="n sig-name">y</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a></span></span><a class="headerlink" href="#arch.unitroot.cointegration.engle_granger.y" title="Permalink to this definition">¶</a></dt><dd><p>The left-hand-side variable in the cointegrating regression.</p>
 </dd>
-<dt id="arch.unitroot.cointegration.engle_granger.x"><span class="n sig-name">x</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a></span></span><a class="headerlink" href="#arch.unitroot.cointegration.engle_granger.x" title="Permalink to this definition">¶</a></dt><dd><p>The right-hand-side variables in the cointegrating regression.</p>
+<dt id="arch.unitroot.cointegration.engle_granger.x"><span class="n sig-name">x</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)">DataFrame</a></span></span><a class="headerlink" href="#arch.unitroot.cointegration.engle_granger.x" title="Permalink to this definition">¶</a></dt><dd><p>The right-hand-side variables in the cointegrating regression.</p>
 </dd>
 <dt id="arch.unitroot.cointegration.engle_granger.trend"><span class="n sig-name">trend</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><span class="s">'n'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'c'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'ct'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'ctt'</span></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;c&#39;</span></code><a class="headerlink" href="#arch.unitroot.cointegration.engle_granger.trend" title="Permalink to this definition">¶</a></dt><dd><p><p>Trend to include in the cointegrating regression. Trends are:</p>
 <ul class="simple">
@@ -876,7 +876,7 @@ <h1 id="arch-unitroot-cointegration-engle-granger">arch.unitroot.cointegration.e
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 <h1 id="arch-unitroot-cointegration-phillips-ouliaris">arch.unitroot.cointegration.phillips_ouliaris<a class="headerlink" href="#arch-unitroot-cointegration-phillips-ouliaris" title="Link to this heading">¶</a></h1>
 <dl class="py function objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.unitroot.cointegration.phillips_ouliaris">
-<span class="sig-prename descclassname"><span class="pre">arch.unitroot.cointegration.</span></span><span class="sig-name descname"><span class="pre">phillips_ouliaris</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.phillips_ouliaris.y" title="arch.unitroot.cointegration.phillips_ouliaris.y (Python parameter) — The left-hand-side variable in the cointegrating regression."><span class="n"><span class="pre">y</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.phillips_ouliaris.x" title="arch.unitroot.cointegration.phillips_ouliaris.x (Python parameter) — The right-hand-side variables in the cointegrating regression."><span class="n"><span class="pre">x</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.phillips_ouliaris.trend" title="arch.unitroot.cointegration.phillips_ouliaris.trend (Python parameter) — Trend to include in the cointegrating regression. Trends are:"><span class="n"><span class="pre">trend</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'n'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'c'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'ct'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'ctt'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;c&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.phillips_ouliaris.test_type" title="arch.unitroot.cointegration.phillips_ouliaris.test_type (Python parameter) — The test statistic to compute. Supported options are:"><span class="n"><span class="pre">test_type</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'Za'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'Zt'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'Pu'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'Pz'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;Zt&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.phillips_ouliaris.kernel" title="arch.unitroot.cointegration.phillips_ouliaris.kernel (Python parameter) — The string name of any of any known kernel-based long-run covariance estimators. Common choices are &quot;bartlett&quot; for the Bartlett kernel (Newey-West), &quot;parzen&quot; for the Parzen kernel and &quot;quadratic-spectral&quot; for the Quadratic Spectral kernel."><span class="n"><span class="pre">kernel</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;bartlett&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.phillips_ouliaris.bandwidth" title="arch.unitroot.cointegration.phillips_ouliaris.bandwidth (Python parameter) — The bandwidth to use. If not provided, the optimal bandwidth is estimated from the data. Setting the bandwidth to 0 and using &quot;unadjusted&quot; produces the classic OLS covariance estimator. Setting the bandwidth to 0 and using &quot;robust&quot; produces White's covariance estimator."><span class="n"><span class="pre">bandwidth</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.phillips_ouliaris.force_int" title="arch.unitroot.cointegration.phillips_ouliaris.force_int (Python parameter) — Whether the force the estimated optimal bandwidth to be an integer."><span class="n"><span class="pre">force_int</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference internal" href="arch.unitroot.cointegration.PhillipsOuliarisTestResults.html#arch.unitroot.cointegration.PhillipsOuliarisTestResults" title="arch.unitroot._phillips_ouliaris.PhillipsOuliarisTestResults (Python class)"><span class="pre">PhillipsOuliarisTestResults</span></a></span></span></span><a class="reference internal" href="../../_modules/arch/unitroot/_phillips_ouliaris.html#phillips_ouliaris"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.unitroot.cointegration.phillips_ouliaris" title="Link to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">arch.unitroot.cointegration.</span></span><span class="sig-name descname"><span class="pre">phillips_ouliaris</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.phillips_ouliaris.y" title="arch.unitroot.cointegration.phillips_ouliaris.y (Python parameter) — The left-hand-side variable in the cointegrating regression."><span class="n"><span class="pre">y</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.phillips_ouliaris.x" title="arch.unitroot.cointegration.phillips_ouliaris.x (Python parameter) — The right-hand-side variables in the cointegrating regression."><span class="n"><span class="pre">x</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><span class="pre">DataFrame</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.phillips_ouliaris.trend" title="arch.unitroot.cointegration.phillips_ouliaris.trend (Python parameter) — Trend to include in the cointegrating regression. Trends are:"><span class="n"><span class="pre">trend</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'n'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'c'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'ct'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'ctt'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;c&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.phillips_ouliaris.test_type" title="arch.unitroot.cointegration.phillips_ouliaris.test_type (Python parameter) — The test statistic to compute. Supported options are:"><span class="n"><span class="pre">test_type</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'Za'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'Zt'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'Pu'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'Pz'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;Zt&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.phillips_ouliaris.kernel" title="arch.unitroot.cointegration.phillips_ouliaris.kernel (Python parameter) — The string name of any of any known kernel-based long-run covariance estimators. Common choices are &quot;bartlett&quot; for the Bartlett kernel (Newey-West), &quot;parzen&quot; for the Parzen kernel and &quot;quadratic-spectral&quot; for the Quadratic Spectral kernel."><span class="n"><span class="pre">kernel</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;bartlett&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.phillips_ouliaris.bandwidth" title="arch.unitroot.cointegration.phillips_ouliaris.bandwidth (Python parameter) — The bandwidth to use. If not provided, the optimal bandwidth is estimated from the data. Setting the bandwidth to 0 and using &quot;unadjusted&quot; produces the classic OLS covariance estimator. Setting the bandwidth to 0 and using &quot;robust&quot; produces White's covariance estimator."><span class="n"><span class="pre">bandwidth</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.phillips_ouliaris.force_int" title="arch.unitroot.cointegration.phillips_ouliaris.force_int (Python parameter) — Whether the force the estimated optimal bandwidth to be an integer."><span class="n"><span class="pre">force_int</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference internal" href="arch.unitroot.cointegration.PhillipsOuliarisTestResults.html#arch.unitroot.cointegration.PhillipsOuliarisTestResults" title="arch.unitroot._phillips_ouliaris.PhillipsOuliarisTestResults (Python class)"><span class="pre">PhillipsOuliarisTestResults</span></a></span></span></span><a class="reference internal" href="../../_modules/arch/unitroot/_phillips_ouliaris.html#phillips_ouliaris"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.unitroot.cointegration.phillips_ouliaris" title="Link to this definition">¶</a></dt>
 <dd><p>Test for cointegration within a set of time series.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.unitroot.cointegration.phillips_ouliaris-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.unitroot.cointegration.phillips_ouliaris-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.unitroot.cointegration.phillips_ouliaris.y"><span class="n sig-name">y</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.unitroot.cointegration.phillips_ouliaris.y" title="Permalink to this definition">¶</a></dt><dd><p>The left-hand-side variable in the cointegrating regression.</p>
+<dt id="arch.unitroot.cointegration.phillips_ouliaris.y"><span class="n sig-name">y</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a></span></span><a class="headerlink" href="#arch.unitroot.cointegration.phillips_ouliaris.y" title="Permalink to this definition">¶</a></dt><dd><p>The left-hand-side variable in the cointegrating regression.</p>
 </dd>
-<dt id="arch.unitroot.cointegration.phillips_ouliaris.x"><span class="n sig-name">x</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a></span></span><a class="headerlink" href="#arch.unitroot.cointegration.phillips_ouliaris.x" title="Permalink to this definition">¶</a></dt><dd><p>The right-hand-side variables in the cointegrating regression.</p>
+<dt id="arch.unitroot.cointegration.phillips_ouliaris.x"><span class="n sig-name">x</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)">DataFrame</a></span></span><a class="headerlink" href="#arch.unitroot.cointegration.phillips_ouliaris.x" title="Permalink to this definition">¶</a></dt><dd><p>The right-hand-side variables in the cointegrating regression.</p>
 </dd>
 <dt id="arch.unitroot.cointegration.phillips_ouliaris.trend"><span class="n sig-name">trend</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><span class="s">'n'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'c'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'ct'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'ctt'</span></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;c&#39;</span></code><a class="headerlink" href="#arch.unitroot.cointegration.phillips_ouliaris.trend" title="Permalink to this definition">¶</a></dt><dd><p><p>Trend to include in the cointegrating regression. Trends are:</p>
 <ul class="simple">
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index a88811b97a..2e8eb0f32b 100644
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@@ -15,7 +15,7 @@
     
   
       
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+            arch v6.2.1 (+23)
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diff --git a/devel/unitroot/generated/generated/arch.unitroot.ADF.critical_values.html b/devel/unitroot/generated/generated/arch.unitroot.ADF.critical_values.html
index 7ae952f577..ef08b386a6 100644
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+++ b/devel/unitroot/generated/generated/arch.unitroot.ADF.critical_values.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.ADF.critical_values - arch v6.2.1 (+21)</title>
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@@ -47,7 +47,7 @@
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-            arch v6.2.1 (+21)
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@@ -189,10 +189,10 @@
 
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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diff --git a/devel/unitroot/generated/generated/arch.unitroot.ADF.lags.html b/devel/unitroot/generated/generated/arch.unitroot.ADF.lags.html
index d9de260ab6..60e89a6d93 100644
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+++ b/devel/unitroot/generated/generated/arch.unitroot.ADF.lags.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.ADF.lags - arch v6.2.1 (+21)</title>
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@@ -47,7 +47,7 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
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@@ -92,7 +92,7 @@
 
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@@ -102,7 +102,7 @@
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+            arch v6.2.1 (+23)
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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+++ b/devel/unitroot/generated/generated/arch.unitroot.ADF.max_lags.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.ADF.max_lags - arch v6.2.1 (+21)</title>
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@@ -47,7 +47,7 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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diff --git a/devel/unitroot/generated/generated/arch.unitroot.ADF.nobs.html b/devel/unitroot/generated/generated/arch.unitroot.ADF.nobs.html
index 6c94a6e204..b4bef7f41f 100644
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+++ b/devel/unitroot/generated/generated/arch.unitroot.ADF.nobs.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.ADF.nobs - arch v6.2.1 (+21)</title>
+        <title>arch.unitroot.ADF.nobs - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
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@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
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   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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     <div class="md-nav__source">
@@ -1005,7 +1005,7 @@ <h1 id="arch-unitroot-adf-nobs">arch.unitroot.ADF.nobs<a class="headerlink" href
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/devel/unitroot/generated/generated/arch.unitroot.ADF.null_hypothesis.html b/devel/unitroot/generated/generated/arch.unitroot.ADF.null_hypothesis.html
index bc522749de..609901c403 100644
--- a/devel/unitroot/generated/generated/arch.unitroot.ADF.null_hypothesis.html
+++ b/devel/unitroot/generated/generated/arch.unitroot.ADF.null_hypothesis.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.ADF.null_hypothesis - arch v6.2.1 (+21)</title>
+        <title>arch.unitroot.ADF.null_hypothesis - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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     <div class="md-nav__source">
@@ -1004,7 +1004,7 @@ <h1 id="arch-unitroot-adf-null-hypothesis">arch.unitroot.ADF.null_hypothesis<a c
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/devel/unitroot/generated/generated/arch.unitroot.ADF.pvalue.html b/devel/unitroot/generated/generated/arch.unitroot.ADF.pvalue.html
index 720d41187c..24a77a10d4 100644
--- a/devel/unitroot/generated/generated/arch.unitroot.ADF.pvalue.html
+++ b/devel/unitroot/generated/generated/arch.unitroot.ADF.pvalue.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.ADF.pvalue - arch v6.2.1 (+21)</title>
+        <title>arch.unitroot.ADF.pvalue - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -1004,7 +1004,7 @@ <h1 id="arch-unitroot-adf-pvalue">arch.unitroot.ADF.pvalue<a class="headerlink"
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
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   </body>
diff --git a/devel/unitroot/generated/generated/arch.unitroot.ADF.regression.html b/devel/unitroot/generated/generated/arch.unitroot.ADF.regression.html
index 9a7192f4da..2dc2733562 100644
--- a/devel/unitroot/generated/generated/arch.unitroot.ADF.regression.html
+++ b/devel/unitroot/generated/generated/arch.unitroot.ADF.regression.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.ADF.regression - arch v6.2.1 (+21)</title>
+        <title>arch.unitroot.ADF.regression - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -886,7 +886,7 @@
 <h1 id="arch-unitroot-adf-regression">arch.unitroot.ADF.regression<a class="headerlink" href="#arch-unitroot-adf-regression" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.unitroot.ADF.regression">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ADF.</span></span><span class="sig-name descname"><span class="pre">regression</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://www.statsmodels.org/dev/generated/statsmodels.regression.linear_model.RegressionResults.html#statsmodels.regression.linear_model.RegressionResults" title="(in statsmodels 0.15.0 (+59) v0.15.0 (+59))"><span class="pre">RegressionResults</span></a></span><a class="headerlink" href="#arch.unitroot.ADF.regression" title="Link to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ADF.</span></span><span class="sig-name descname"><span class="pre">regression</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://www.statsmodels.org/dev/generated/statsmodels.regression.linear_model.RegressionResults.html#statsmodels.regression.linear_model.RegressionResults" title="(in statsmodels 0.15.0 (+132) v0.15.0 (+132))"><span class="pre">RegressionResults</span></a></span><a class="headerlink" href="#arch.unitroot.ADF.regression" title="Link to this definition">¶</a></dt>
 <dd><p>Returns the OLS regression results from the ADF model estimated</p>
 </dd></dl>
 
@@ -1004,7 +1004,7 @@ <h1 id="arch-unitroot-adf-regression">arch.unitroot.ADF.regression<a class="head
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
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   </body>
diff --git a/devel/unitroot/generated/generated/arch.unitroot.ADF.stat.html b/devel/unitroot/generated/generated/arch.unitroot.ADF.stat.html
index 3b79acdc3c..48033fa202 100644
--- a/devel/unitroot/generated/generated/arch.unitroot.ADF.stat.html
+++ b/devel/unitroot/generated/generated/arch.unitroot.ADF.stat.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.ADF.stat - arch v6.2.1 (+21)</title>
+        <title>arch.unitroot.ADF.stat - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
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diff --git a/devel/unitroot/generated/generated/arch.unitroot.ADF.summary.html b/devel/unitroot/generated/generated/arch.unitroot.ADF.summary.html
index 8f2669c74a..77ed7e03f4 100644
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+++ b/devel/unitroot/generated/generated/arch.unitroot.ADF.summary.html
@@ -15,7 +15,7 @@
     
   
       
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+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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@@ -886,7 +886,7 @@
 <h1 id="arch-unitroot-adf-summary">arch.unitroot.ADF.summary<a class="headerlink" href="#arch-unitroot-adf-summary" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight py" id="arch.unitroot.ADF.summary">
-<span class="sig-prename descclassname"><span class="pre">ADF.</span></span><span class="sig-name descname"><span class="pre">summary</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://www.statsmodels.org/dev/generated/statsmodels.iolib.summary.Summary.html#statsmodels.iolib.summary.Summary" title="(in statsmodels 0.15.0 (+59) v0.15.0 (+59))"><span class="pre">Summary</span></a></span></span></span><a class="headerlink" href="#arch.unitroot.ADF.summary" title="Link to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">ADF.</span></span><span class="sig-name descname"><span class="pre">summary</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://www.statsmodels.org/dev/generated/statsmodels.iolib.summary.Summary.html#statsmodels.iolib.summary.Summary" title="(in statsmodels 0.15.0 (+132) v0.15.0 (+132))"><span class="pre">Summary</span></a></span></span></span><a class="headerlink" href="#arch.unitroot.ADF.summary" title="Link to this definition">¶</a></dt>
 <dd><p>Summary of test, containing statistic, p-value and critical values</p>
 </dd></dl>
 
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diff --git a/devel/unitroot/generated/generated/arch.unitroot.ADF.trend.html b/devel/unitroot/generated/generated/arch.unitroot.ADF.trend.html
index 466094aaf1..e421d7587b 100644
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+++ b/devel/unitroot/generated/generated/arch.unitroot.ADF.trend.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.ADF.trend - arch v6.2.1 (+21)</title>
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@@ -47,7 +47,7 @@
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+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
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-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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@@ -1005,7 +1005,7 @@ <h1 id="arch-unitroot-adf-trend">arch.unitroot.ADF.trend<a class="headerlink" hr
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-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
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   </body>
diff --git a/devel/unitroot/generated/generated/arch.unitroot.ADF.valid_trends.html b/devel/unitroot/generated/generated/arch.unitroot.ADF.valid_trends.html
index 59532659f0..9345a0dedd 100644
--- a/devel/unitroot/generated/generated/arch.unitroot.ADF.valid_trends.html
+++ b/devel/unitroot/generated/generated/arch.unitroot.ADF.valid_trends.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.ADF.valid_trends - arch v6.2.1 (+21)</title>
+        <title>arch.unitroot.ADF.valid_trends - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
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-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
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@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
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-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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diff --git a/devel/unitroot/generated/generated/arch.unitroot.ADF.y.html b/devel/unitroot/generated/generated/arch.unitroot.ADF.y.html
index 628bb43c9a..23669f10b5 100644
--- a/devel/unitroot/generated/generated/arch.unitroot.ADF.y.html
+++ b/devel/unitroot/generated/generated/arch.unitroot.ADF.y.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.ADF.y - arch v6.2.1 (+21)</title>
+        <title>arch.unitroot.ADF.y - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
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   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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     <div class="md-nav__source">
@@ -886,7 +886,7 @@
 <h1 id="arch-unitroot-adf-y">arch.unitroot.ADF.y<a class="headerlink" href="#arch-unitroot-adf-y" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.unitroot.ADF.y">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ADF.</span></span><span class="sig-name descname"><span class="pre">y</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span><a class="headerlink" href="#arch.unitroot.ADF.y" title="Link to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ADF.</span></span><span class="sig-name descname"><span class="pre">y</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a></span><a class="headerlink" href="#arch.unitroot.ADF.y" title="Link to this definition">¶</a></dt>
 <dd><p>Returns the data used in the test statistic</p>
 </dd></dl>
 
@@ -1004,7 +1004,7 @@ <h1 id="arch-unitroot-adf-y">arch.unitroot.ADF.y<a class="headerlink" href="#arc
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diff --git a/devel/unitroot/generated/generated/arch.unitroot.DFGLS.alternative_hypothesis.html b/devel/unitroot/generated/generated/arch.unitroot.DFGLS.alternative_hypothesis.html
index f564bbc3ca..c6f85c9b17 100644
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+++ b/devel/unitroot/generated/generated/arch.unitroot.DFGLS.alternative_hypothesis.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.DFGLS.alternative_hypothesis - arch v6.2.1 (+21)</title>
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@@ -47,7 +47,7 @@
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@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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@@ -189,10 +189,10 @@
 
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+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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     <div class="md-nav__source">
@@ -1004,7 +1004,7 @@ <h1 id="arch-unitroot-dfgls-alternative-hypothesis">arch.unitroot.DFGLS.alternat
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diff --git a/devel/unitroot/generated/generated/arch.unitroot.DFGLS.critical_values.html b/devel/unitroot/generated/generated/arch.unitroot.DFGLS.critical_values.html
index 4d288dfce6..a56b6ad30e 100644
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+++ b/devel/unitroot/generated/generated/arch.unitroot.DFGLS.critical_values.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.DFGLS.critical_values - arch v6.2.1 (+21)</title>
+        <title>arch.unitroot.DFGLS.critical_values - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
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+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
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-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
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diff --git a/devel/unitroot/generated/generated/arch.unitroot.DFGLS.lags.html b/devel/unitroot/generated/generated/arch.unitroot.DFGLS.lags.html
index 4401e12cb0..9d197527d3 100644
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+++ b/devel/unitroot/generated/generated/arch.unitroot.DFGLS.lags.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.DFGLS.lags - arch v6.2.1 (+21)</title>
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@@ -47,7 +47,7 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
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@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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@@ -189,10 +189,10 @@
 
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+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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-    arch v6.2.1 (+21)
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index 0e2da5db3e..b03ad9f49f 100644
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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@@ -102,7 +102,7 @@
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+            arch v6.2.1 (+23)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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-    arch v6.2.1 (+21)
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   </body>
diff --git a/devel/unitroot/generated/generated/arch.unitroot.DFGLS.nobs.html b/devel/unitroot/generated/generated/arch.unitroot.DFGLS.nobs.html
index bc50386bd1..457e3da620 100644
--- a/devel/unitroot/generated/generated/arch.unitroot.DFGLS.nobs.html
+++ b/devel/unitroot/generated/generated/arch.unitroot.DFGLS.nobs.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.DFGLS.nobs - arch v6.2.1 (+21)</title>
+        <title>arch.unitroot.DFGLS.nobs - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
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-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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@@ -189,10 +189,10 @@
 
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   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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     <div class="md-nav__source">
@@ -1005,7 +1005,7 @@ <h1 id="arch-unitroot-dfgls-nobs">arch.unitroot.DFGLS.nobs<a class="headerlink"
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-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/devel/unitroot/generated/generated/arch.unitroot.DFGLS.null_hypothesis.html b/devel/unitroot/generated/generated/arch.unitroot.DFGLS.null_hypothesis.html
index 06f77f7ef0..7f08bb534a 100644
--- a/devel/unitroot/generated/generated/arch.unitroot.DFGLS.null_hypothesis.html
+++ b/devel/unitroot/generated/generated/arch.unitroot.DFGLS.null_hypothesis.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.DFGLS.null_hypothesis - arch v6.2.1 (+21)</title>
+        <title>arch.unitroot.DFGLS.null_hypothesis - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -1004,7 +1004,7 @@ <h1 id="arch-unitroot-dfgls-null-hypothesis">arch.unitroot.DFGLS.null_hypothesis
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-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
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   </body>
diff --git a/devel/unitroot/generated/generated/arch.unitroot.DFGLS.pvalue.html b/devel/unitroot/generated/generated/arch.unitroot.DFGLS.pvalue.html
index 45dde16b40..1b9dac0526 100644
--- a/devel/unitroot/generated/generated/arch.unitroot.DFGLS.pvalue.html
+++ b/devel/unitroot/generated/generated/arch.unitroot.DFGLS.pvalue.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.DFGLS.pvalue - arch v6.2.1 (+21)</title>
+        <title>arch.unitroot.DFGLS.pvalue - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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     <div class="md-nav__source">
@@ -1004,7 +1004,7 @@ <h1 id="arch-unitroot-dfgls-pvalue">arch.unitroot.DFGLS.pvalue<a class="headerli
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
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   </body>
diff --git a/devel/unitroot/generated/generated/arch.unitroot.DFGLS.regression.html b/devel/unitroot/generated/generated/arch.unitroot.DFGLS.regression.html
index c0e9056cc0..eb477339bd 100644
--- a/devel/unitroot/generated/generated/arch.unitroot.DFGLS.regression.html
+++ b/devel/unitroot/generated/generated/arch.unitroot.DFGLS.regression.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.DFGLS.regression - arch v6.2.1 (+21)</title>
+        <title>arch.unitroot.DFGLS.regression - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -886,7 +886,7 @@
 <h1 id="arch-unitroot-dfgls-regression">arch.unitroot.DFGLS.regression<a class="headerlink" href="#arch-unitroot-dfgls-regression" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.unitroot.DFGLS.regression">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">DFGLS.</span></span><span class="sig-name descname"><span class="pre">regression</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://www.statsmodels.org/dev/generated/statsmodels.regression.linear_model.RegressionResults.html#statsmodels.regression.linear_model.RegressionResults" title="(in statsmodels 0.15.0 (+59) v0.15.0 (+59))"><span class="pre">RegressionResults</span></a></span><a class="headerlink" href="#arch.unitroot.DFGLS.regression" title="Link to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">DFGLS.</span></span><span class="sig-name descname"><span class="pre">regression</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://www.statsmodels.org/dev/generated/statsmodels.regression.linear_model.RegressionResults.html#statsmodels.regression.linear_model.RegressionResults" title="(in statsmodels 0.15.0 (+132) v0.15.0 (+132))"><span class="pre">RegressionResults</span></a></span><a class="headerlink" href="#arch.unitroot.DFGLS.regression" title="Link to this definition">¶</a></dt>
 <dd><p>Returns the OLS regression results from the ADF model estimated</p>
 </dd></dl>
 
@@ -1004,7 +1004,7 @@ <h1 id="arch-unitroot-dfgls-regression">arch.unitroot.DFGLS.regression<a class="
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
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   </body>
diff --git a/devel/unitroot/generated/generated/arch.unitroot.DFGLS.stat.html b/devel/unitroot/generated/generated/arch.unitroot.DFGLS.stat.html
index b6409e433a..3db08e56a5 100644
--- a/devel/unitroot/generated/generated/arch.unitroot.DFGLS.stat.html
+++ b/devel/unitroot/generated/generated/arch.unitroot.DFGLS.stat.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.DFGLS.stat - arch v6.2.1 (+21)</title>
+        <title>arch.unitroot.DFGLS.stat - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
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@@ -92,7 +92,7 @@
 
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+            arch v6.2.1 (+23)
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diff --git a/devel/unitroot/generated/generated/arch.unitroot.DFGLS.summary.html b/devel/unitroot/generated/generated/arch.unitroot.DFGLS.summary.html
index 0b4dbed7f9..6f84f73955 100644
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+++ b/devel/unitroot/generated/generated/arch.unitroot.DFGLS.summary.html
@@ -15,7 +15,7 @@
     
   
       
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@@ -47,7 +47,7 @@
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+    arch v6.2.1 (+23)
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@@ -886,7 +886,7 @@
 <h1 id="arch-unitroot-dfgls-summary">arch.unitroot.DFGLS.summary<a class="headerlink" href="#arch-unitroot-dfgls-summary" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight py" id="arch.unitroot.DFGLS.summary">
-<span class="sig-prename descclassname"><span class="pre">DFGLS.</span></span><span class="sig-name descname"><span class="pre">summary</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://www.statsmodels.org/dev/generated/statsmodels.iolib.summary.Summary.html#statsmodels.iolib.summary.Summary" title="(in statsmodels 0.15.0 (+59) v0.15.0 (+59))"><span class="pre">Summary</span></a></span></span></span><a class="headerlink" href="#arch.unitroot.DFGLS.summary" title="Link to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">DFGLS.</span></span><span class="sig-name descname"><span class="pre">summary</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://www.statsmodels.org/dev/generated/statsmodels.iolib.summary.Summary.html#statsmodels.iolib.summary.Summary" title="(in statsmodels 0.15.0 (+132) v0.15.0 (+132))"><span class="pre">Summary</span></a></span></span></span><a class="headerlink" href="#arch.unitroot.DFGLS.summary" title="Link to this definition">¶</a></dt>
 <dd><p>Summary of test, containing statistic, p-value and critical values</p>
 </dd></dl>
 
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diff --git a/devel/unitroot/generated/generated/arch.unitroot.DFGLS.trend.html b/devel/unitroot/generated/generated/arch.unitroot.DFGLS.trend.html
index 5967cef43d..e31e800999 100644
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+++ b/devel/unitroot/generated/generated/arch.unitroot.DFGLS.trend.html
@@ -15,7 +15,7 @@
     
   
       
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@@ -47,7 +47,7 @@
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+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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+            arch v6.2.1 (+23)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
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+    arch v6.2.1 (+23)
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diff --git a/devel/unitroot/generated/generated/arch.unitroot.DFGLS.valid_trends.html b/devel/unitroot/generated/generated/arch.unitroot.DFGLS.valid_trends.html
index 695397606f..32d4a7ea62 100644
--- a/devel/unitroot/generated/generated/arch.unitroot.DFGLS.valid_trends.html
+++ b/devel/unitroot/generated/generated/arch.unitroot.DFGLS.valid_trends.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.DFGLS.valid_trends - arch v6.2.1 (+21)</title>
+        <title>arch.unitroot.DFGLS.valid_trends - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
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@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
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       <div class="md-header__ellipsis">
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           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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     <div class="md-nav__source">
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-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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   </body>
diff --git a/devel/unitroot/generated/generated/arch.unitroot.DFGLS.y.html b/devel/unitroot/generated/generated/arch.unitroot.DFGLS.y.html
index 9a05931094..cc66c5ffb6 100644
--- a/devel/unitroot/generated/generated/arch.unitroot.DFGLS.y.html
+++ b/devel/unitroot/generated/generated/arch.unitroot.DFGLS.y.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.DFGLS.y - arch v6.2.1 (+21)</title>
+        <title>arch.unitroot.DFGLS.y - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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     <div class="md-nav__source">
@@ -886,7 +886,7 @@
 <h1 id="arch-unitroot-dfgls-y">arch.unitroot.DFGLS.y<a class="headerlink" href="#arch-unitroot-dfgls-y" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.unitroot.DFGLS.y">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">DFGLS.</span></span><span class="sig-name descname"><span class="pre">y</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span><a class="headerlink" href="#arch.unitroot.DFGLS.y" title="Link to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">DFGLS.</span></span><span class="sig-name descname"><span class="pre">y</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a></span><a class="headerlink" href="#arch.unitroot.DFGLS.y" title="Link to this definition">¶</a></dt>
 <dd><p>Returns the data used in the test statistic</p>
 </dd></dl>
 
@@ -1004,7 +1004,7 @@ <h1 id="arch-unitroot-dfgls-y">arch.unitroot.DFGLS.y<a class="headerlink" href="
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diff --git a/devel/unitroot/generated/generated/arch.unitroot.KPSS.alternative_hypothesis.html b/devel/unitroot/generated/generated/arch.unitroot.KPSS.alternative_hypothesis.html
index df5c9205e0..9d4a27df15 100644
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@@ -15,7 +15,7 @@
     
   
       
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@@ -47,7 +47,7 @@
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-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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diff --git a/devel/unitroot/generated/generated/arch.unitroot.KPSS.critical_values.html b/devel/unitroot/generated/generated/arch.unitroot.KPSS.critical_values.html
index 05886bdbca..dcd65bac9e 100644
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+++ b/devel/unitroot/generated/generated/arch.unitroot.KPSS.critical_values.html
@@ -15,7 +15,7 @@
     
   
       
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@@ -47,7 +47,7 @@
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-    arch v6.2.1 (+21)
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diff --git a/devel/unitroot/generated/generated/arch.unitroot.KPSS.lags.html b/devel/unitroot/generated/generated/arch.unitroot.KPSS.lags.html
index 03adca64c1..fa14b97b01 100644
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+++ b/devel/unitroot/generated/generated/arch.unitroot.KPSS.lags.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.KPSS.lags - arch v6.2.1 (+21)</title>
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@@ -47,7 +47,7 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
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+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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index 55ea1f7d1c..4967dd358a 100644
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diff --git a/devel/unitroot/generated/generated/arch.unitroot.KPSS.stat.html b/devel/unitroot/generated/generated/arch.unitroot.KPSS.stat.html
index c502711211..50cb93b345 100644
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diff --git a/devel/unitroot/generated/generated/arch.unitroot.KPSS.summary.html b/devel/unitroot/generated/generated/arch.unitroot.KPSS.summary.html
index d16167c300..00922000d4 100644
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 <h1 id="arch-unitroot-kpss-summary">arch.unitroot.KPSS.summary<a class="headerlink" href="#arch-unitroot-kpss-summary" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight py" id="arch.unitroot.KPSS.summary">
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+<span class="sig-prename descclassname"><span class="pre">KPSS.</span></span><span class="sig-name descname"><span class="pre">summary</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://www.statsmodels.org/dev/generated/statsmodels.iolib.summary.Summary.html#statsmodels.iolib.summary.Summary" title="(in statsmodels 0.15.0 (+132) v0.15.0 (+132))"><span class="pre">Summary</span></a></span></span></span><a class="headerlink" href="#arch.unitroot.KPSS.summary" title="Link to this definition">¶</a></dt>
 <dd><p>Summary of test, containing statistic, p-value and critical values</p>
 </dd></dl>
 
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index b798672d6e..bd79acd083 100644
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@@ -102,7 +102,7 @@
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-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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@@ -189,10 +189,10 @@
 
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+    arch v6.2.1 (+23)
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diff --git a/devel/unitroot/generated/generated/arch.unitroot.KPSS.valid_trends.html b/devel/unitroot/generated/generated/arch.unitroot.KPSS.valid_trends.html
index 590ed42553..e40e8a3ca4 100644
--- a/devel/unitroot/generated/generated/arch.unitroot.KPSS.valid_trends.html
+++ b/devel/unitroot/generated/generated/arch.unitroot.KPSS.valid_trends.html
@@ -15,7 +15,7 @@
     
   
       
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+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
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+    arch v6.2.1 (+23)
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diff --git a/devel/unitroot/generated/generated/arch.unitroot.KPSS.y.html b/devel/unitroot/generated/generated/arch.unitroot.KPSS.y.html
index 70c3c11102..14c064b08c 100644
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@@ -856,7 +856,7 @@
 <h1 id="arch-unitroot-kpss-y">arch.unitroot.KPSS.y<a class="headerlink" href="#arch-unitroot-kpss-y" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.unitroot.KPSS.y">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">KPSS.</span></span><span class="sig-name descname"><span class="pre">y</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span><a class="headerlink" href="#arch.unitroot.KPSS.y" title="Link to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">KPSS.</span></span><span class="sig-name descname"><span class="pre">y</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a></span><a class="headerlink" href="#arch.unitroot.KPSS.y" title="Link to this definition">¶</a></dt>
 <dd><p>Returns the data used in the test statistic</p>
 </dd></dl>
 
@@ -974,7 +974,7 @@ <h1 id="arch-unitroot-kpss-y">arch.unitroot.KPSS.y<a class="headerlink" href="#a
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diff --git a/devel/unitroot/generated/generated/arch.unitroot.PhillipsPerron.alternative_hypothesis.html b/devel/unitroot/generated/generated/arch.unitroot.PhillipsPerron.alternative_hypothesis.html
index fd614a29ee..b0ec8c59a0 100644
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+++ b/devel/unitroot/generated/generated/arch.unitroot.PhillipsPerron.alternative_hypothesis.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.PhillipsPerron.alternative_hypothesis - arch v6.2.1 (+21)</title>
+        <title>arch.unitroot.PhillipsPerron.alternative_hypothesis - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
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@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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@@ -1004,7 +1004,7 @@ <h1 id="arch-unitroot-phillipsperron-alternative-hypothesis">arch.unitroot.Phill
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+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
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diff --git a/devel/unitroot/generated/generated/arch.unitroot.PhillipsPerron.critical_values.html b/devel/unitroot/generated/generated/arch.unitroot.PhillipsPerron.critical_values.html
index 6cf0dd694c..ac45ff6198 100644
--- a/devel/unitroot/generated/generated/arch.unitroot.PhillipsPerron.critical_values.html
+++ b/devel/unitroot/generated/generated/arch.unitroot.PhillipsPerron.critical_values.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.PhillipsPerron.critical_values - arch v6.2.1 (+21)</title>
+        <title>arch.unitroot.PhillipsPerron.critical_values - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
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@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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@@ -189,10 +189,10 @@
 
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index a75c7d8a43..ef6b84d538 100644
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@@ -15,7 +15,7 @@
     
   
       
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index 2438664e50..55537ef69b 100644
--- a/devel/unitroot/generated/generated/arch.unitroot.PhillipsPerron.nobs.html
+++ b/devel/unitroot/generated/generated/arch.unitroot.PhillipsPerron.nobs.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.PhillipsPerron.nobs - arch v6.2.1 (+21)</title>
+        <title>arch.unitroot.PhillipsPerron.nobs - arch v6.2.1 (+23)</title>
       
     
 
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@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
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-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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diff --git a/devel/unitroot/generated/generated/arch.unitroot.PhillipsPerron.null_hypothesis.html b/devel/unitroot/generated/generated/arch.unitroot.PhillipsPerron.null_hypothesis.html
index dbf0a23d84..eede7d3fcc 100644
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+++ b/devel/unitroot/generated/generated/arch.unitroot.PhillipsPerron.null_hypothesis.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.PhillipsPerron.null_hypothesis - arch v6.2.1 (+21)</title>
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@@ -47,7 +47,7 @@
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@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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+    arch v6.2.1 (+23)
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-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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index 033ae1a377..b340e254f8 100644
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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+            arch v6.2.1 (+23)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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+    arch v6.2.1 (+23)
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-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
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index 075c0ae956..6c158c1636 100644
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+++ b/devel/unitroot/generated/generated/arch.unitroot.PhillipsPerron.regression.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.PhillipsPerron.regression - arch v6.2.1 (+21)</title>
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+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
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     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -886,7 +886,7 @@
 <h1 id="arch-unitroot-phillipsperron-regression">arch.unitroot.PhillipsPerron.regression<a class="headerlink" href="#arch-unitroot-phillipsperron-regression" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.unitroot.PhillipsPerron.regression">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">PhillipsPerron.</span></span><span class="sig-name descname"><span class="pre">regression</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://www.statsmodels.org/dev/generated/statsmodels.regression.linear_model.RegressionResults.html#statsmodels.regression.linear_model.RegressionResults" title="(in statsmodels 0.15.0 (+59) v0.15.0 (+59))"><span class="pre">RegressionResults</span></a></span><a class="headerlink" href="#arch.unitroot.PhillipsPerron.regression" title="Link to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">PhillipsPerron.</span></span><span class="sig-name descname"><span class="pre">regression</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://www.statsmodels.org/dev/generated/statsmodels.regression.linear_model.RegressionResults.html#statsmodels.regression.linear_model.RegressionResults" title="(in statsmodels 0.15.0 (+132) v0.15.0 (+132))"><span class="pre">RegressionResults</span></a></span><a class="headerlink" href="#arch.unitroot.PhillipsPerron.regression" title="Link to this definition">¶</a></dt>
 <dd><p>Returns OLS regression results for the specification used in the test</p>
 <p>The results returned use a Newey-West covariance matrix with the same
 number of lags as are used in the test statistic.</p>
@@ -1006,7 +1006,7 @@ <h1 id="arch-unitroot-phillipsperron-regression">arch.unitroot.PhillipsPerron.re
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/devel/unitroot/generated/generated/arch.unitroot.PhillipsPerron.stat.html b/devel/unitroot/generated/generated/arch.unitroot.PhillipsPerron.stat.html
index cdcb784542..3c7205762c 100644
--- a/devel/unitroot/generated/generated/arch.unitroot.PhillipsPerron.stat.html
+++ b/devel/unitroot/generated/generated/arch.unitroot.PhillipsPerron.stat.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.PhillipsPerron.stat - arch v6.2.1 (+21)</title>
+        <title>arch.unitroot.PhillipsPerron.stat - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -1004,7 +1004,7 @@ <h1 id="arch-unitroot-phillipsperron-stat">arch.unitroot.PhillipsPerron.stat<a c
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/devel/unitroot/generated/generated/arch.unitroot.PhillipsPerron.summary.html b/devel/unitroot/generated/generated/arch.unitroot.PhillipsPerron.summary.html
index 1d7e58ab2c..7468c14e85 100644
--- a/devel/unitroot/generated/generated/arch.unitroot.PhillipsPerron.summary.html
+++ b/devel/unitroot/generated/generated/arch.unitroot.PhillipsPerron.summary.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.PhillipsPerron.summary - arch v6.2.1 (+21)</title>
+        <title>arch.unitroot.PhillipsPerron.summary - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -886,7 +886,7 @@
 <h1 id="arch-unitroot-phillipsperron-summary">arch.unitroot.PhillipsPerron.summary<a class="headerlink" href="#arch-unitroot-phillipsperron-summary" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight py" id="arch.unitroot.PhillipsPerron.summary">
-<span class="sig-prename descclassname"><span class="pre">PhillipsPerron.</span></span><span class="sig-name descname"><span class="pre">summary</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://www.statsmodels.org/dev/generated/statsmodels.iolib.summary.Summary.html#statsmodels.iolib.summary.Summary" title="(in statsmodels 0.15.0 (+59) v0.15.0 (+59))"><span class="pre">Summary</span></a></span></span></span><a class="headerlink" href="#arch.unitroot.PhillipsPerron.summary" title="Link to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">PhillipsPerron.</span></span><span class="sig-name descname"><span class="pre">summary</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://www.statsmodels.org/dev/generated/statsmodels.iolib.summary.Summary.html#statsmodels.iolib.summary.Summary" title="(in statsmodels 0.15.0 (+132) v0.15.0 (+132))"><span class="pre">Summary</span></a></span></span></span><a class="headerlink" href="#arch.unitroot.PhillipsPerron.summary" title="Link to this definition">¶</a></dt>
 <dd><p>Summary of test, containing statistic, p-value and critical values</p>
 </dd></dl>
 
@@ -1004,7 +1004,7 @@ <h1 id="arch-unitroot-phillipsperron-summary">arch.unitroot.PhillipsPerron.summa
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/devel/unitroot/generated/generated/arch.unitroot.PhillipsPerron.test_type.html b/devel/unitroot/generated/generated/arch.unitroot.PhillipsPerron.test_type.html
index 9668b2febe..46542f1b7e 100644
--- a/devel/unitroot/generated/generated/arch.unitroot.PhillipsPerron.test_type.html
+++ b/devel/unitroot/generated/generated/arch.unitroot.PhillipsPerron.test_type.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.PhillipsPerron.test_type - arch v6.2.1 (+21)</title>
+        <title>arch.unitroot.PhillipsPerron.test_type - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -1005,7 +1005,7 @@ <h1 id="arch-unitroot-phillipsperron-test-type">arch.unitroot.PhillipsPerron.tes
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/devel/unitroot/generated/generated/arch.unitroot.PhillipsPerron.trend.html b/devel/unitroot/generated/generated/arch.unitroot.PhillipsPerron.trend.html
index 9ebb6b14ab..b2f145aaa9 100644
--- a/devel/unitroot/generated/generated/arch.unitroot.PhillipsPerron.trend.html
+++ b/devel/unitroot/generated/generated/arch.unitroot.PhillipsPerron.trend.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.PhillipsPerron.trend - arch v6.2.1 (+21)</title>
+        <title>arch.unitroot.PhillipsPerron.trend - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
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@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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@@ -1005,7 +1005,7 @@ <h1 id="arch-unitroot-phillipsperron-trend">arch.unitroot.PhillipsPerron.trend<a
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+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
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diff --git a/devel/unitroot/generated/generated/arch.unitroot.PhillipsPerron.valid_trends.html b/devel/unitroot/generated/generated/arch.unitroot.PhillipsPerron.valid_trends.html
index 2adbcdd25f..68b071ce87 100644
--- a/devel/unitroot/generated/generated/arch.unitroot.PhillipsPerron.valid_trends.html
+++ b/devel/unitroot/generated/generated/arch.unitroot.PhillipsPerron.valid_trends.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.PhillipsPerron.valid_trends - arch v6.2.1 (+21)</title>
+        <title>arch.unitroot.PhillipsPerron.valid_trends - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
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-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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     <div class="md-nav__source">
@@ -1004,7 +1004,7 @@ <h1 id="arch-unitroot-phillipsperron-valid-trends">arch.unitroot.PhillipsPerron.
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diff --git a/devel/unitroot/generated/generated/arch.unitroot.PhillipsPerron.y.html b/devel/unitroot/generated/generated/arch.unitroot.PhillipsPerron.y.html
index 89f18b493d..a08bcbec0b 100644
--- a/devel/unitroot/generated/generated/arch.unitroot.PhillipsPerron.y.html
+++ b/devel/unitroot/generated/generated/arch.unitroot.PhillipsPerron.y.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.PhillipsPerron.y - arch v6.2.1 (+21)</title>
+        <title>arch.unitroot.PhillipsPerron.y - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
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@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
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+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
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-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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@@ -189,10 +189,10 @@
 
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   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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     <div class="md-nav__source">
@@ -886,7 +886,7 @@
 <h1 id="arch-unitroot-phillipsperron-y">arch.unitroot.PhillipsPerron.y<a class="headerlink" href="#arch-unitroot-phillipsperron-y" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.unitroot.PhillipsPerron.y">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">PhillipsPerron.</span></span><span class="sig-name descname"><span class="pre">y</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span><a class="headerlink" href="#arch.unitroot.PhillipsPerron.y" title="Link to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">PhillipsPerron.</span></span><span class="sig-name descname"><span class="pre">y</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a></span><a class="headerlink" href="#arch.unitroot.PhillipsPerron.y" title="Link to this definition">¶</a></dt>
 <dd><p>Returns the data used in the test statistic</p>
 </dd></dl>
 
@@ -1004,7 +1004,7 @@ <h1 id="arch-unitroot-phillipsperron-y">arch.unitroot.PhillipsPerron.y<a class="
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diff --git a/devel/unitroot/generated/generated/arch.unitroot.VarianceRatio.alternative_hypothesis.html b/devel/unitroot/generated/generated/arch.unitroot.VarianceRatio.alternative_hypothesis.html
index c1e023a0e9..08a428adf3 100644
--- a/devel/unitroot/generated/generated/arch.unitroot.VarianceRatio.alternative_hypothesis.html
+++ b/devel/unitroot/generated/generated/arch.unitroot.VarianceRatio.alternative_hypothesis.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.VarianceRatio.alternative_hypothesis - arch v6.2.1 (+21)</title>
+        <title>arch.unitroot.VarianceRatio.alternative_hypothesis - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -1034,7 +1034,7 @@ <h1 id="arch-unitroot-varianceratio-alternative-hypothesis">arch.unitroot.Varian
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-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
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diff --git a/devel/unitroot/generated/generated/arch.unitroot.VarianceRatio.critical_values.html b/devel/unitroot/generated/generated/arch.unitroot.VarianceRatio.critical_values.html
index 19e5efa012..a9a2c5a704 100644
--- a/devel/unitroot/generated/generated/arch.unitroot.VarianceRatio.critical_values.html
+++ b/devel/unitroot/generated/generated/arch.unitroot.VarianceRatio.critical_values.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.VarianceRatio.critical_values - arch v6.2.1 (+21)</title>
+        <title>arch.unitroot.VarianceRatio.critical_values - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
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@@ -92,7 +92,7 @@
 
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 <h1 id="arch-unitroot-varianceratio-summary">arch.unitroot.VarianceRatio.summary<a class="headerlink" href="#arch-unitroot-varianceratio-summary" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight py" id="arch.unitroot.VarianceRatio.summary">
-<span class="sig-prename descclassname"><span class="pre">VarianceRatio.</span></span><span class="sig-name descname"><span class="pre">summary</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://www.statsmodels.org/dev/generated/statsmodels.iolib.summary.Summary.html#statsmodels.iolib.summary.Summary" title="(in statsmodels 0.15.0 (+59) v0.15.0 (+59))"><span class="pre">Summary</span></a></span></span></span><a class="headerlink" href="#arch.unitroot.VarianceRatio.summary" title="Link to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">VarianceRatio.</span></span><span class="sig-name descname"><span class="pre">summary</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://www.statsmodels.org/dev/generated/statsmodels.iolib.summary.Summary.html#statsmodels.iolib.summary.Summary" title="(in statsmodels 0.15.0 (+132) v0.15.0 (+132))"><span class="pre">Summary</span></a></span></span></span><a class="headerlink" href="#arch.unitroot.VarianceRatio.summary" title="Link to this definition">¶</a></dt>
 <dd><p>Summary of test, containing statistic, p-value and critical values</p>
 </dd></dl>
 
@@ -1034,7 +1034,7 @@ <h1 id="arch-unitroot-varianceratio-summary">arch.unitroot.VarianceRatio.summary
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diff --git a/devel/unitroot/generated/generated/arch.unitroot.VarianceRatio.trend.html b/devel/unitroot/generated/generated/arch.unitroot.VarianceRatio.trend.html
index a69fd3e2ec..9d988f00a4 100644
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@@ -15,7 +15,7 @@
     
   
       
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@@ -47,7 +47,7 @@
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-            arch v6.2.1 (+21)
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+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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@@ -1035,7 +1035,7 @@ <h1 id="arch-unitroot-varianceratio-trend">arch.unitroot.VarianceRatio.trend<a c
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diff --git a/devel/unitroot/generated/generated/arch.unitroot.VarianceRatio.valid_trends.html b/devel/unitroot/generated/generated/arch.unitroot.VarianceRatio.valid_trends.html
index ffdd68e188..5cd40d5c29 100644
--- a/devel/unitroot/generated/generated/arch.unitroot.VarianceRatio.valid_trends.html
+++ b/devel/unitroot/generated/generated/arch.unitroot.VarianceRatio.valid_trends.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.VarianceRatio.valid_trends - arch v6.2.1 (+21)</title>
+        <title>arch.unitroot.VarianceRatio.valid_trends - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
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+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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@@ -1034,7 +1034,7 @@ <h1 id="arch-unitroot-varianceratio-valid-trends">arch.unitroot.VarianceRatio.va
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-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
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diff --git a/devel/unitroot/generated/generated/arch.unitroot.VarianceRatio.vr.html b/devel/unitroot/generated/generated/arch.unitroot.VarianceRatio.vr.html
index d99e2d82ac..b9b68c7a79 100644
--- a/devel/unitroot/generated/generated/arch.unitroot.VarianceRatio.vr.html
+++ b/devel/unitroot/generated/generated/arch.unitroot.VarianceRatio.vr.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.VarianceRatio.vr - arch v6.2.1 (+21)</title>
+        <title>arch.unitroot.VarianceRatio.vr - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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diff --git a/devel/unitroot/generated/generated/arch.unitroot.VarianceRatio.y.html b/devel/unitroot/generated/generated/arch.unitroot.VarianceRatio.y.html
index 57b055648e..3723f8bc84 100644
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+++ b/devel/unitroot/generated/generated/arch.unitroot.VarianceRatio.y.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.VarianceRatio.y - arch v6.2.1 (+21)</title>
+        <title>arch.unitroot.VarianceRatio.y - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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@@ -916,7 +916,7 @@
 <h1 id="arch-unitroot-varianceratio-y">arch.unitroot.VarianceRatio.y<a class="headerlink" href="#arch-unitroot-varianceratio-y" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.unitroot.VarianceRatio.y">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">VarianceRatio.</span></span><span class="sig-name descname"><span class="pre">y</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span><a class="headerlink" href="#arch.unitroot.VarianceRatio.y" title="Link to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">VarianceRatio.</span></span><span class="sig-name descname"><span class="pre">y</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a></span><a class="headerlink" href="#arch.unitroot.VarianceRatio.y" title="Link to this definition">¶</a></dt>
 <dd><p>Returns the data used in the test statistic</p>
 </dd></dl>
 
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diff --git a/devel/unitroot/generated/generated/arch.unitroot.ZivotAndrews.alternative_hypothesis.html b/devel/unitroot/generated/generated/arch.unitroot.ZivotAndrews.alternative_hypothesis.html
index e60a12931d..7d4f9133c3 100644
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@@ -15,7 +15,7 @@
     
   
       
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+            arch v6.2.1 (+23)
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index 8e803a02f9..1862f2fca2 100644
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@@ -15,7 +15,7 @@
     
   
       
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-    arch v6.2.1 (+21)
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diff --git a/devel/unitroot/generated/generated/arch.unitroot.ZivotAndrews.lags.html b/devel/unitroot/generated/generated/arch.unitroot.ZivotAndrews.lags.html
index 0e6cd6c5fb..d476694ba0 100644
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+++ b/devel/unitroot/generated/generated/arch.unitroot.ZivotAndrews.lags.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.ZivotAndrews.lags - arch v6.2.1 (+21)</title>
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@@ -189,10 +189,10 @@
 
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+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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index dea851402d..4167276452 100644
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+++ b/devel/unitroot/generated/generated/arch.unitroot.ZivotAndrews.nobs.html
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+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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index 2f0c27935a..c9fe5f8638 100644
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+++ b/devel/unitroot/generated/generated/arch.unitroot.ZivotAndrews.null_hypothesis.html
@@ -15,7 +15,7 @@
     
   
       
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+++ b/devel/unitroot/generated/generated/arch.unitroot.ZivotAndrews.pvalue.html
@@ -15,7 +15,7 @@
     
   
       
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diff --git a/devel/unitroot/generated/generated/arch.unitroot.ZivotAndrews.stat.html b/devel/unitroot/generated/generated/arch.unitroot.ZivotAndrews.stat.html
index 50beb52fd3..84f8521d72 100644
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+++ b/devel/unitroot/generated/generated/arch.unitroot.ZivotAndrews.stat.html
@@ -15,7 +15,7 @@
     
   
       
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+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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diff --git a/devel/unitroot/generated/generated/arch.unitroot.ZivotAndrews.summary.html b/devel/unitroot/generated/generated/arch.unitroot.ZivotAndrews.summary.html
index 6c50a22204..d4c113d288 100644
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@@ -15,7 +15,7 @@
     
   
       
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@@ -856,7 +856,7 @@
 <h1 id="arch-unitroot-zivotandrews-summary">arch.unitroot.ZivotAndrews.summary<a class="headerlink" href="#arch-unitroot-zivotandrews-summary" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight py" id="arch.unitroot.ZivotAndrews.summary">
-<span class="sig-prename descclassname"><span class="pre">ZivotAndrews.</span></span><span class="sig-name descname"><span class="pre">summary</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://www.statsmodels.org/dev/generated/statsmodels.iolib.summary.Summary.html#statsmodels.iolib.summary.Summary" title="(in statsmodels 0.15.0 (+59) v0.15.0 (+59))"><span class="pre">Summary</span></a></span></span></span><a class="headerlink" href="#arch.unitroot.ZivotAndrews.summary" title="Link to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">ZivotAndrews.</span></span><span class="sig-name descname"><span class="pre">summary</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://www.statsmodels.org/dev/generated/statsmodels.iolib.summary.Summary.html#statsmodels.iolib.summary.Summary" title="(in statsmodels 0.15.0 (+132) v0.15.0 (+132))"><span class="pre">Summary</span></a></span></span></span><a class="headerlink" href="#arch.unitroot.ZivotAndrews.summary" title="Link to this definition">¶</a></dt>
 <dd><p>Summary of test, containing statistic, p-value and critical values</p>
 </dd></dl>
 
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index 49550c94a5..a044db29d4 100644
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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     <div class="md-nav__source">
@@ -975,7 +975,7 @@ <h1 id="arch-unitroot-zivotandrews-trend">arch.unitroot.ZivotAndrews.trend<a cla
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-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
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   </body>
diff --git a/devel/unitroot/generated/generated/arch.unitroot.ZivotAndrews.valid_trends.html b/devel/unitroot/generated/generated/arch.unitroot.ZivotAndrews.valid_trends.html
index c53ee44996..d0cc49fec7 100644
--- a/devel/unitroot/generated/generated/arch.unitroot.ZivotAndrews.valid_trends.html
+++ b/devel/unitroot/generated/generated/arch.unitroot.ZivotAndrews.valid_trends.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.ZivotAndrews.valid_trends - arch v6.2.1 (+21)</title>
+        <title>arch.unitroot.ZivotAndrews.valid_trends - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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     <div class="md-nav__source">
@@ -974,7 +974,7 @@ <h1 id="arch-unitroot-zivotandrews-valid-trends">arch.unitroot.ZivotAndrews.vali
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-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
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   </body>
diff --git a/devel/unitroot/generated/generated/arch.unitroot.ZivotAndrews.y.html b/devel/unitroot/generated/generated/arch.unitroot.ZivotAndrews.y.html
index a0f68f075a..6bd555ccd7 100644
--- a/devel/unitroot/generated/generated/arch.unitroot.ZivotAndrews.y.html
+++ b/devel/unitroot/generated/generated/arch.unitroot.ZivotAndrews.y.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.ZivotAndrews.y - arch v6.2.1 (+21)</title>
+        <title>arch.unitroot.ZivotAndrews.y - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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@@ -189,10 +189,10 @@
 
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   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -856,7 +856,7 @@
 <h1 id="arch-unitroot-zivotandrews-y">arch.unitroot.ZivotAndrews.y<a class="headerlink" href="#arch-unitroot-zivotandrews-y" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.unitroot.ZivotAndrews.y">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ZivotAndrews.</span></span><span class="sig-name descname"><span class="pre">y</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span><a class="headerlink" href="#arch.unitroot.ZivotAndrews.y" title="Link to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ZivotAndrews.</span></span><span class="sig-name descname"><span class="pre">y</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a></span><a class="headerlink" href="#arch.unitroot.ZivotAndrews.y" title="Link to this definition">¶</a></dt>
 <dd><p>Returns the data used in the test statistic</p>
 </dd></dl>
 
@@ -974,7 +974,7 @@ <h1 id="arch-unitroot-zivotandrews-y">arch.unitroot.ZivotAndrews.y<a class="head
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
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   </body>
diff --git a/devel/unitroot/generated/generated/arch.unitroot.cointegration.CanonicalCointegratingReg.fit.html b/devel/unitroot/generated/generated/arch.unitroot.cointegration.CanonicalCointegratingReg.fit.html
index c3c2ccafbc..9026cd1c0a 100644
--- a/devel/unitroot/generated/generated/arch.unitroot.cointegration.CanonicalCointegratingReg.fit.html
+++ b/devel/unitroot/generated/generated/arch.unitroot.cointegration.CanonicalCointegratingReg.fit.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration.CanonicalCointegratingReg.fit - arch v6.2.1 (+21)</title>
+        <title>arch.unitroot.cointegration.CanonicalCointegratingReg.fit - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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     <div class="md-nav__source">
@@ -947,7 +947,7 @@ <h1 id="arch-unitroot-cointegration-canonicalcointegratingreg-fit">arch.unitroot
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
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   </body>
diff --git a/devel/unitroot/generated/generated/arch.unitroot.cointegration.CointegrationAnalysisResults.bandwidth.html b/devel/unitroot/generated/generated/arch.unitroot.cointegration.CointegrationAnalysisResults.bandwidth.html
index b91b7f4b37..d133a7fb53 100644
--- a/devel/unitroot/generated/generated/arch.unitroot.cointegration.CointegrationAnalysisResults.bandwidth.html
+++ b/devel/unitroot/generated/generated/arch.unitroot.cointegration.CointegrationAnalysisResults.bandwidth.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration.CointegrationAnalysisResults.bandwidth - arch v6.2.1 (+21)</title>
+        <title>arch.unitroot.cointegration.CointegrationAnalysisResults.bandwidth - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -1044,7 +1044,7 @@ <h1 id="arch-unitroot-cointegration-cointegrationanalysisresults-bandwidth">arch
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/devel/unitroot/generated/generated/arch.unitroot.cointegration.CointegrationAnalysisResults.cov.html b/devel/unitroot/generated/generated/arch.unitroot.cointegration.CointegrationAnalysisResults.cov.html
index cd6f715488..df109108dc 100644
--- a/devel/unitroot/generated/generated/arch.unitroot.cointegration.CointegrationAnalysisResults.cov.html
+++ b/devel/unitroot/generated/generated/arch.unitroot.cointegration.CointegrationAnalysisResults.cov.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration.CointegrationAnalysisResults.cov - arch v6.2.1 (+21)</title>
+        <title>arch.unitroot.cointegration.CointegrationAnalysisResults.cov - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -926,7 +926,7 @@
 <h1 id="arch-unitroot-cointegration-cointegrationanalysisresults-cov">arch.unitroot.cointegration.CointegrationAnalysisResults.cov<a class="headerlink" href="#arch-unitroot-cointegration-cointegrationanalysisresults-cov" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.unitroot.cointegration.CointegrationAnalysisResults.cov">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">CointegrationAnalysisResults.</span></span><span class="sig-name descname"><span class="pre">cov</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a></span><a class="headerlink" href="#arch.unitroot.cointegration.CointegrationAnalysisResults.cov" title="Link to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">CointegrationAnalysisResults.</span></span><span class="sig-name descname"><span class="pre">cov</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><span class="pre">DataFrame</span></a></span><a class="headerlink" href="#arch.unitroot.cointegration.CointegrationAnalysisResults.cov" title="Link to this definition">¶</a></dt>
 <dd><p>The estimated parameter covariance of the cointegrating vector</p>
 </dd></dl>
 
@@ -1044,7 +1044,7 @@ <h1 id="arch-unitroot-cointegration-cointegrationanalysisresults-cov">arch.unitr
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/devel/unitroot/generated/generated/arch.unitroot.cointegration.CointegrationAnalysisResults.kernel.html b/devel/unitroot/generated/generated/arch.unitroot.cointegration.CointegrationAnalysisResults.kernel.html
index 0106d9b32a..e8ffabeabf 100644
--- a/devel/unitroot/generated/generated/arch.unitroot.cointegration.CointegrationAnalysisResults.kernel.html
+++ b/devel/unitroot/generated/generated/arch.unitroot.cointegration.CointegrationAnalysisResults.kernel.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration.CointegrationAnalysisResults.kernel - arch v6.2.1 (+21)</title>
+        <title>arch.unitroot.cointegration.CointegrationAnalysisResults.kernel - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -1044,7 +1044,7 @@ <h1 id="arch-unitroot-cointegration-cointegrationanalysisresults-kernel">arch.un
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/devel/unitroot/generated/generated/arch.unitroot.cointegration.CointegrationAnalysisResults.long_run_variance.html b/devel/unitroot/generated/generated/arch.unitroot.cointegration.CointegrationAnalysisResults.long_run_variance.html
index 312a680503..e59d6a362b 100644
--- a/devel/unitroot/generated/generated/arch.unitroot.cointegration.CointegrationAnalysisResults.long_run_variance.html
+++ b/devel/unitroot/generated/generated/arch.unitroot.cointegration.CointegrationAnalysisResults.long_run_variance.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration.CointegrationAnalysisResults.long_run_variance - arch v6.2.1 (+21)</title>
+        <title>arch.unitroot.cointegration.CointegrationAnalysisResults.long_run_variance - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -1044,7 +1044,7 @@ <h1 id="arch-unitroot-cointegration-cointegrationanalysisresults-long-run-varian
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/devel/unitroot/generated/generated/arch.unitroot.cointegration.CointegrationAnalysisResults.params.html b/devel/unitroot/generated/generated/arch.unitroot.cointegration.CointegrationAnalysisResults.params.html
index 98136be29c..6777943f2d 100644
--- a/devel/unitroot/generated/generated/arch.unitroot.cointegration.CointegrationAnalysisResults.params.html
+++ b/devel/unitroot/generated/generated/arch.unitroot.cointegration.CointegrationAnalysisResults.params.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration.CointegrationAnalysisResults.params - arch v6.2.1 (+21)</title>
+        <title>arch.unitroot.cointegration.CointegrationAnalysisResults.params - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -926,7 +926,7 @@
 <h1 id="arch-unitroot-cointegration-cointegrationanalysisresults-params">arch.unitroot.cointegration.CointegrationAnalysisResults.params<a class="headerlink" href="#arch-unitroot-cointegration-cointegrationanalysisresults-params" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.unitroot.cointegration.CointegrationAnalysisResults.params">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">CointegrationAnalysisResults.</span></span><span class="sig-name descname"><span class="pre">params</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span><a class="headerlink" href="#arch.unitroot.cointegration.CointegrationAnalysisResults.params" title="Link to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">CointegrationAnalysisResults.</span></span><span class="sig-name descname"><span class="pre">params</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a></span><a class="headerlink" href="#arch.unitroot.cointegration.CointegrationAnalysisResults.params" title="Link to this definition">¶</a></dt>
 <dd><p>The estimated parameters of the cointegrating vector</p>
 </dd></dl>
 
@@ -1044,7 +1044,7 @@ <h1 id="arch-unitroot-cointegration-cointegrationanalysisresults-params">arch.un
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/devel/unitroot/generated/generated/arch.unitroot.cointegration.CointegrationAnalysisResults.pvalues.html b/devel/unitroot/generated/generated/arch.unitroot.cointegration.CointegrationAnalysisResults.pvalues.html
index ec44831ba9..db41dd50ca 100644
--- a/devel/unitroot/generated/generated/arch.unitroot.cointegration.CointegrationAnalysisResults.pvalues.html
+++ b/devel/unitroot/generated/generated/arch.unitroot.cointegration.CointegrationAnalysisResults.pvalues.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration.CointegrationAnalysisResults.pvalues - arch v6.2.1 (+21)</title>
+        <title>arch.unitroot.cointegration.CointegrationAnalysisResults.pvalues - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -926,7 +926,7 @@
 <h1 id="arch-unitroot-cointegration-cointegrationanalysisresults-pvalues">arch.unitroot.cointegration.CointegrationAnalysisResults.pvalues<a class="headerlink" href="#arch-unitroot-cointegration-cointegrationanalysisresults-pvalues" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.unitroot.cointegration.CointegrationAnalysisResults.pvalues">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">CointegrationAnalysisResults.</span></span><span class="sig-name descname"><span class="pre">pvalues</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span><a class="headerlink" href="#arch.unitroot.cointegration.CointegrationAnalysisResults.pvalues" title="Link to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">CointegrationAnalysisResults.</span></span><span class="sig-name descname"><span class="pre">pvalues</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a></span><a class="headerlink" href="#arch.unitroot.cointegration.CointegrationAnalysisResults.pvalues" title="Link to this definition">¶</a></dt>
 <dd><p>P-value of the parameters in the cointegrating vector</p>
 </dd></dl>
 
@@ -1044,7 +1044,7 @@ <h1 id="arch-unitroot-cointegration-cointegrationanalysisresults-pvalues">arch.u
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/devel/unitroot/generated/generated/arch.unitroot.cointegration.CointegrationAnalysisResults.resid.html b/devel/unitroot/generated/generated/arch.unitroot.cointegration.CointegrationAnalysisResults.resid.html
index 980167d8b0..c67c6e233b 100644
--- a/devel/unitroot/generated/generated/arch.unitroot.cointegration.CointegrationAnalysisResults.resid.html
+++ b/devel/unitroot/generated/generated/arch.unitroot.cointegration.CointegrationAnalysisResults.resid.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration.CointegrationAnalysisResults.resid - arch v6.2.1 (+21)</title>
+        <title>arch.unitroot.cointegration.CointegrationAnalysisResults.resid - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -926,7 +926,7 @@
 <h1 id="arch-unitroot-cointegration-cointegrationanalysisresults-resid">arch.unitroot.cointegration.CointegrationAnalysisResults.resid<a class="headerlink" href="#arch-unitroot-cointegration-cointegrationanalysisresults-resid" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.unitroot.cointegration.CointegrationAnalysisResults.resid">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">CointegrationAnalysisResults.</span></span><span class="sig-name descname"><span class="pre">resid</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span><a class="headerlink" href="#arch.unitroot.cointegration.CointegrationAnalysisResults.resid" title="Link to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">CointegrationAnalysisResults.</span></span><span class="sig-name descname"><span class="pre">resid</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a></span><a class="headerlink" href="#arch.unitroot.cointegration.CointegrationAnalysisResults.resid" title="Link to this definition">¶</a></dt>
 <dd><p>The model residuals</p>
 </dd></dl>
 
@@ -1044,7 +1044,7 @@ <h1 id="arch-unitroot-cointegration-cointegrationanalysisresults-resid">arch.uni
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/devel/unitroot/generated/generated/arch.unitroot.cointegration.CointegrationAnalysisResults.residual_variance.html b/devel/unitroot/generated/generated/arch.unitroot.cointegration.CointegrationAnalysisResults.residual_variance.html
index f7f7551e41..976e8f9b80 100644
--- a/devel/unitroot/generated/generated/arch.unitroot.cointegration.CointegrationAnalysisResults.residual_variance.html
+++ b/devel/unitroot/generated/generated/arch.unitroot.cointegration.CointegrationAnalysisResults.residual_variance.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration.CointegrationAnalysisResults.residual_variance - arch v6.2.1 (+21)</title>
+        <title>arch.unitroot.cointegration.CointegrationAnalysisResults.residual_variance - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -1100,7 +1100,7 @@ <h1 id="arch-unitroot-cointegration-cointegrationanalysisresults-residual-varian
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
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         <script id="MathJax-script" src="../../../_static/mathjax/tex-mml-chtml.js?v=cadf963e"></script>
diff --git a/devel/unitroot/generated/generated/arch.unitroot.cointegration.CointegrationAnalysisResults.rsquared.html b/devel/unitroot/generated/generated/arch.unitroot.cointegration.CointegrationAnalysisResults.rsquared.html
index f049733369..9590ea66f1 100644
--- a/devel/unitroot/generated/generated/arch.unitroot.cointegration.CointegrationAnalysisResults.rsquared.html
+++ b/devel/unitroot/generated/generated/arch.unitroot.cointegration.CointegrationAnalysisResults.rsquared.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration.CointegrationAnalysisResults.rsquared - arch v6.2.1 (+21)</title>
+        <title>arch.unitroot.cointegration.CointegrationAnalysisResults.rsquared - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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@@ -1044,7 +1044,7 @@ <h1 id="arch-unitroot-cointegration-cointegrationanalysisresults-rsquared">arch.
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diff --git a/devel/unitroot/generated/generated/arch.unitroot.cointegration.CointegrationAnalysisResults.rsquared_adj.html b/devel/unitroot/generated/generated/arch.unitroot.cointegration.CointegrationAnalysisResults.rsquared_adj.html
index cdfcf06e11..c60c9ca9d5 100644
--- a/devel/unitroot/generated/generated/arch.unitroot.cointegration.CointegrationAnalysisResults.rsquared_adj.html
+++ b/devel/unitroot/generated/generated/arch.unitroot.cointegration.CointegrationAnalysisResults.rsquared_adj.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration.CointegrationAnalysisResults.rsquared_adj - arch v6.2.1 (+21)</title>
+        <title>arch.unitroot.cointegration.CointegrationAnalysisResults.rsquared_adj - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
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@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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@@ -1044,7 +1044,7 @@ <h1 id="arch-unitroot-cointegration-cointegrationanalysisresults-rsquared-adj">a
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-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
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diff --git a/devel/unitroot/generated/generated/arch.unitroot.cointegration.CointegrationAnalysisResults.std_errors.html b/devel/unitroot/generated/generated/arch.unitroot.cointegration.CointegrationAnalysisResults.std_errors.html
index 501d19cff4..69a50c5ae7 100644
--- a/devel/unitroot/generated/generated/arch.unitroot.cointegration.CointegrationAnalysisResults.std_errors.html
+++ b/devel/unitroot/generated/generated/arch.unitroot.cointegration.CointegrationAnalysisResults.std_errors.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration.CointegrationAnalysisResults.std_errors - arch v6.2.1 (+21)</title>
+        <title>arch.unitroot.cointegration.CointegrationAnalysisResults.std_errors - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
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@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.2.1 (+21)
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-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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@@ -926,7 +926,7 @@
 <h1 id="arch-unitroot-cointegration-cointegrationanalysisresults-std-errors">arch.unitroot.cointegration.CointegrationAnalysisResults.std_errors<a class="headerlink" href="#arch-unitroot-cointegration-cointegrationanalysisresults-std-errors" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.unitroot.cointegration.CointegrationAnalysisResults.std_errors">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">CointegrationAnalysisResults.</span></span><span class="sig-name descname"><span class="pre">std_errors</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span><a class="headerlink" href="#arch.unitroot.cointegration.CointegrationAnalysisResults.std_errors" title="Link to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">CointegrationAnalysisResults.</span></span><span class="sig-name descname"><span class="pre">std_errors</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a></span><a class="headerlink" href="#arch.unitroot.cointegration.CointegrationAnalysisResults.std_errors" title="Link to this definition">¶</a></dt>
 <dd><p>Standard errors  of the parameters in the cointegrating vector</p>
 </dd></dl>
 
@@ -1044,7 +1044,7 @@ <h1 id="arch-unitroot-cointegration-cointegrationanalysisresults-std-errors">arc
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diff --git a/devel/unitroot/generated/generated/arch.unitroot.cointegration.CointegrationAnalysisResults.summary.html b/devel/unitroot/generated/generated/arch.unitroot.cointegration.CointegrationAnalysisResults.summary.html
index 3cc433ead5..2f49d0e79b 100644
--- a/devel/unitroot/generated/generated/arch.unitroot.cointegration.CointegrationAnalysisResults.summary.html
+++ b/devel/unitroot/generated/generated/arch.unitroot.cointegration.CointegrationAnalysisResults.summary.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration.CointegrationAnalysisResults.summary - arch v6.2.1 (+21)</title>
+        <title>arch.unitroot.cointegration.CointegrationAnalysisResults.summary - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
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   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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@@ -966,7 +966,7 @@
 <h1 id="arch-unitroot-cointegration-cointegrationanalysisresults-summary">arch.unitroot.cointegration.CointegrationAnalysisResults.summary<a class="headerlink" href="#arch-unitroot-cointegration-cointegrationanalysisresults-summary" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight py" id="arch.unitroot.cointegration.CointegrationAnalysisResults.summary">
-<span class="sig-prename descclassname"><span class="pre">CointegrationAnalysisResults.</span></span><span class="sig-name descname"><span class="pre">summary</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://www.statsmodels.org/dev/generated/statsmodels.iolib.summary.Summary.html#statsmodels.iolib.summary.Summary" title="(in statsmodels 0.15.0 (+59) v0.15.0 (+59))"><span class="pre">Summary</span></a></span></span></span><a class="headerlink" href="#arch.unitroot.cointegration.CointegrationAnalysisResults.summary" title="Link to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">CointegrationAnalysisResults.</span></span><span class="sig-name descname"><span class="pre">summary</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://www.statsmodels.org/dev/generated/statsmodels.iolib.summary.Summary.html#statsmodels.iolib.summary.Summary" title="(in statsmodels 0.15.0 (+132) v0.15.0 (+132))"><span class="pre">Summary</span></a></span></span></span><a class="headerlink" href="#arch.unitroot.cointegration.CointegrationAnalysisResults.summary" title="Link to this definition">¶</a></dt>
 <dd><p>Summary of the model, containing estimated parameters and std. errors</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.unitroot.cointegration.CointegrationAnalysisResults.summary-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.unitroot.cointegration.CointegrationAnalysisResults.summary-returns" title="Permalink to this headline">¶</a></dt>
@@ -1093,7 +1093,7 @@ <h1 id="arch-unitroot-cointegration-cointegrationanalysisresults-summary">arch.u
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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   </body>
diff --git a/devel/unitroot/generated/generated/arch.unitroot.cointegration.CointegrationAnalysisResults.tvalues.html b/devel/unitroot/generated/generated/arch.unitroot.cointegration.CointegrationAnalysisResults.tvalues.html
index 1c5b20d961..65a04996d1 100644
--- a/devel/unitroot/generated/generated/arch.unitroot.cointegration.CointegrationAnalysisResults.tvalues.html
+++ b/devel/unitroot/generated/generated/arch.unitroot.cointegration.CointegrationAnalysisResults.tvalues.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration.CointegrationAnalysisResults.tvalues - arch v6.2.1 (+21)</title>
+        <title>arch.unitroot.cointegration.CointegrationAnalysisResults.tvalues - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
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@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -926,7 +926,7 @@
 <h1 id="arch-unitroot-cointegration-cointegrationanalysisresults-tvalues">arch.unitroot.cointegration.CointegrationAnalysisResults.tvalues<a class="headerlink" href="#arch-unitroot-cointegration-cointegrationanalysisresults-tvalues" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.unitroot.cointegration.CointegrationAnalysisResults.tvalues">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">CointegrationAnalysisResults.</span></span><span class="sig-name descname"><span class="pre">tvalues</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span><a class="headerlink" href="#arch.unitroot.cointegration.CointegrationAnalysisResults.tvalues" title="Link to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">CointegrationAnalysisResults.</span></span><span class="sig-name descname"><span class="pre">tvalues</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a></span><a class="headerlink" href="#arch.unitroot.cointegration.CointegrationAnalysisResults.tvalues" title="Link to this definition">¶</a></dt>
 <dd><p>T-statistics of the parameters in the cointegrating vector</p>
 </dd></dl>
 
@@ -1044,7 +1044,7 @@ <h1 id="arch-unitroot-cointegration-cointegrationanalysisresults-tvalues">arch.u
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
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+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
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   </body>
diff --git a/devel/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLS.fit.html b/devel/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLS.fit.html
index a51b094f68..325c37990b 100644
--- a/devel/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLS.fit.html
+++ b/devel/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLS.fit.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration.DynamicOLS.fit - arch v6.2.1 (+21)</title>
+        <title>arch.unitroot.cointegration.DynamicOLS.fit - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -837,7 +837,7 @@ <h1 id="arch-unitroot-cointegration-dynamicols-fit">arch.unitroot.cointegration.
 <dl class="simple">
 <dt><a class="reference internal" href="../arch.unitroot.cointegration.engle_granger.html#arch.unitroot.cointegration.engle_granger" title="arch.unitroot.cointegration.engle_granger (Python function) — Test for cointegration within a set of time series."><code class="xref py py-obj docutils literal notranslate"><span class="pre">arch.unitroot.cointegration.engle_granger</span></code></a></dt><dd><p>Cointegration testing using the Engle-Granger methodology</p>
 </dd>
-<dt><a class="reference external" href="https://www.statsmodels.org/dev/generated/statsmodels.regression.linear_model.OLS.html#statsmodels.regression.linear_model.OLS" title="(in statsmodels 0.15.0 (+59) v0.15.0 (+59))"><code class="xref py py-obj docutils literal notranslate"><span class="pre">statsmodels.regression.linear_model.OLS</span></code></a></dt><dd><p>Ordinal Least Squares regression.</p>
+<dt><a class="reference external" href="https://www.statsmodels.org/dev/generated/statsmodels.regression.linear_model.OLS.html#statsmodels.regression.linear_model.OLS" title="(in statsmodels 0.15.0 (+132) v0.15.0 (+132))"><code class="xref py py-obj docutils literal notranslate"><span class="pre">statsmodels.regression.linear_model.OLS</span></code></a></dt><dd><p>Ordinal Least Squares regression.</p>
 </dd>
 </dl>
 </div>
@@ -973,7 +973,7 @@ <h1 id="arch-unitroot-cointegration-dynamicols-fit">arch.unitroot.cointegration.
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
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         <script id="MathJax-script" src="../../../_static/mathjax/tex-mml-chtml.js?v=cadf963e"></script>
diff --git a/devel/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.bandwidth.html b/devel/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.bandwidth.html
index 960b53b8e0..7481f31fbf 100644
--- a/devel/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.bandwidth.html
+++ b/devel/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.bandwidth.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration.DynamicOLSResults.bandwidth - arch v6.2.1 (+21)</title>
+        <title>arch.unitroot.cointegration.DynamicOLSResults.bandwidth - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -1119,7 +1119,7 @@ <h1 id="arch-unitroot-cointegration-dynamicolsresults-bandwidth">arch.unitroot.c
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
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   </body>
diff --git a/devel/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.cov.html b/devel/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.cov.html
index 4a3a423a3b..fd43fc61c6 100644
--- a/devel/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.cov.html
+++ b/devel/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.cov.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration.DynamicOLSResults.cov - arch v6.2.1 (+21)</title>
+        <title>arch.unitroot.cointegration.DynamicOLSResults.cov - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -1001,7 +1001,7 @@
 <h1 id="arch-unitroot-cointegration-dynamicolsresults-cov">arch.unitroot.cointegration.DynamicOLSResults.cov<a class="headerlink" href="#arch-unitroot-cointegration-dynamicolsresults-cov" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.unitroot.cointegration.DynamicOLSResults.cov">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">DynamicOLSResults.</span></span><span class="sig-name descname"><span class="pre">cov</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a></span><a class="headerlink" href="#arch.unitroot.cointegration.DynamicOLSResults.cov" title="Link to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">DynamicOLSResults.</span></span><span class="sig-name descname"><span class="pre">cov</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><span class="pre">DataFrame</span></a></span><a class="headerlink" href="#arch.unitroot.cointegration.DynamicOLSResults.cov" title="Link to this definition">¶</a></dt>
 <dd><p>The estimated parameter covariance of the cointegrating vector</p>
 </dd></dl>
 
@@ -1119,7 +1119,7 @@ <h1 id="arch-unitroot-cointegration-dynamicolsresults-cov">arch.unitroot.cointeg
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
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   </body>
diff --git a/devel/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.cov_type.html b/devel/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.cov_type.html
index 53ec39f841..94de050c43 100644
--- a/devel/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.cov_type.html
+++ b/devel/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.cov_type.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration.DynamicOLSResults.cov_type - arch v6.2.1 (+21)</title>
+        <title>arch.unitroot.cointegration.DynamicOLSResults.cov_type - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -1119,7 +1119,7 @@ <h1 id="arch-unitroot-cointegration-dynamicolsresults-cov-type">arch.unitroot.co
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/devel/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.full_cov.html b/devel/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.full_cov.html
index ca6420bb44..ab960f3fc9 100644
--- a/devel/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.full_cov.html
+++ b/devel/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.full_cov.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration.DynamicOLSResults.full_cov - arch v6.2.1 (+21)</title>
+        <title>arch.unitroot.cointegration.DynamicOLSResults.full_cov - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -1001,7 +1001,7 @@
 <h1 id="arch-unitroot-cointegration-dynamicolsresults-full-cov">arch.unitroot.cointegration.DynamicOLSResults.full_cov<a class="headerlink" href="#arch-unitroot-cointegration-dynamicolsresults-full-cov" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.unitroot.cointegration.DynamicOLSResults.full_cov">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">DynamicOLSResults.</span></span><span class="sig-name descname"><span class="pre">full_cov</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a></span><a class="headerlink" href="#arch.unitroot.cointegration.DynamicOLSResults.full_cov" title="Link to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">DynamicOLSResults.</span></span><span class="sig-name descname"><span class="pre">full_cov</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><span class="pre">DataFrame</span></a></span><a class="headerlink" href="#arch.unitroot.cointegration.DynamicOLSResults.full_cov" title="Link to this definition">¶</a></dt>
 <dd><p>Parameter covariance of the all model parameters, incl. leads and lags</p>
 </dd></dl>
 
@@ -1119,7 +1119,7 @@ <h1 id="arch-unitroot-cointegration-dynamicolsresults-full-cov">arch.unitroot.co
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/devel/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.full_params.html b/devel/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.full_params.html
index dad91e33c6..cab7d02d1e 100644
--- a/devel/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.full_params.html
+++ b/devel/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.full_params.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration.DynamicOLSResults.full_params - arch v6.2.1 (+21)</title>
+        <title>arch.unitroot.cointegration.DynamicOLSResults.full_params - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -1001,7 +1001,7 @@
 <h1 id="arch-unitroot-cointegration-dynamicolsresults-full-params">arch.unitroot.cointegration.DynamicOLSResults.full_params<a class="headerlink" href="#arch-unitroot-cointegration-dynamicolsresults-full-params" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.unitroot.cointegration.DynamicOLSResults.full_params">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">DynamicOLSResults.</span></span><span class="sig-name descname"><span class="pre">full_params</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span><a class="headerlink" href="#arch.unitroot.cointegration.DynamicOLSResults.full_params" title="Link to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">DynamicOLSResults.</span></span><span class="sig-name descname"><span class="pre">full_params</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a></span><a class="headerlink" href="#arch.unitroot.cointegration.DynamicOLSResults.full_params" title="Link to this definition">¶</a></dt>
 <dd><p>The complete set of parameters, including leads and lags</p>
 </dd></dl>
 
@@ -1119,7 +1119,7 @@ <h1 id="arch-unitroot-cointegration-dynamicolsresults-full-params">arch.unitroot
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/devel/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.kernel.html b/devel/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.kernel.html
index 0f40275c06..4ba7485d2f 100644
--- a/devel/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.kernel.html
+++ b/devel/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.kernel.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration.DynamicOLSResults.kernel - arch v6.2.1 (+21)</title>
+        <title>arch.unitroot.cointegration.DynamicOLSResults.kernel - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -1119,7 +1119,7 @@ <h1 id="arch-unitroot-cointegration-dynamicolsresults-kernel">arch.unitroot.coin
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/devel/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.lags.html b/devel/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.lags.html
index 81c4c41e9b..38ddace088 100644
--- a/devel/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.lags.html
+++ b/devel/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.lags.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration.DynamicOLSResults.lags - arch v6.2.1 (+21)</title>
+        <title>arch.unitroot.cointegration.DynamicOLSResults.lags - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -1119,7 +1119,7 @@ <h1 id="arch-unitroot-cointegration-dynamicolsresults-lags">arch.unitroot.cointe
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/devel/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.leads.html b/devel/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.leads.html
index 0c32d6a28f..efd25937ec 100644
--- a/devel/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.leads.html
+++ b/devel/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.leads.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration.DynamicOLSResults.leads - arch v6.2.1 (+21)</title>
+        <title>arch.unitroot.cointegration.DynamicOLSResults.leads - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -1119,7 +1119,7 @@ <h1 id="arch-unitroot-cointegration-dynamicolsresults-leads">arch.unitroot.coint
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/devel/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.long_run_variance.html b/devel/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.long_run_variance.html
index f3d548eae6..711725f89a 100644
--- a/devel/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.long_run_variance.html
+++ b/devel/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.long_run_variance.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration.DynamicOLSResults.long_run_variance - arch v6.2.1 (+21)</title>
+        <title>arch.unitroot.cointegration.DynamicOLSResults.long_run_variance - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -1173,7 +1173,7 @@ <h1 id="arch-unitroot-cointegration-dynamicolsresults-long-run-variance">arch.un
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/devel/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.params.html b/devel/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.params.html
index a375bdce4b..885a34611b 100644
--- a/devel/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.params.html
+++ b/devel/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.params.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration.DynamicOLSResults.params - arch v6.2.1 (+21)</title>
+        <title>arch.unitroot.cointegration.DynamicOLSResults.params - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -1001,7 +1001,7 @@
 <h1 id="arch-unitroot-cointegration-dynamicolsresults-params">arch.unitroot.cointegration.DynamicOLSResults.params<a class="headerlink" href="#arch-unitroot-cointegration-dynamicolsresults-params" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.unitroot.cointegration.DynamicOLSResults.params">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">DynamicOLSResults.</span></span><span class="sig-name descname"><span class="pre">params</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span><a class="headerlink" href="#arch.unitroot.cointegration.DynamicOLSResults.params" title="Link to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">DynamicOLSResults.</span></span><span class="sig-name descname"><span class="pre">params</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a></span><a class="headerlink" href="#arch.unitroot.cointegration.DynamicOLSResults.params" title="Link to this definition">¶</a></dt>
 <dd><p>The estimated parameters of the cointegrating vector</p>
 </dd></dl>
 
@@ -1119,7 +1119,7 @@ <h1 id="arch-unitroot-cointegration-dynamicolsresults-params">arch.unitroot.coin
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/devel/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.pvalues.html b/devel/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.pvalues.html
index 001eb8f86b..f9cdeedd91 100644
--- a/devel/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.pvalues.html
+++ b/devel/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.pvalues.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration.DynamicOLSResults.pvalues - arch v6.2.1 (+21)</title>
+        <title>arch.unitroot.cointegration.DynamicOLSResults.pvalues - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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     <div class="md-nav__source">
@@ -1001,7 +1001,7 @@
 <h1 id="arch-unitroot-cointegration-dynamicolsresults-pvalues">arch.unitroot.cointegration.DynamicOLSResults.pvalues<a class="headerlink" href="#arch-unitroot-cointegration-dynamicolsresults-pvalues" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.unitroot.cointegration.DynamicOLSResults.pvalues">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">DynamicOLSResults.</span></span><span class="sig-name descname"><span class="pre">pvalues</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span><a class="headerlink" href="#arch.unitroot.cointegration.DynamicOLSResults.pvalues" title="Link to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">DynamicOLSResults.</span></span><span class="sig-name descname"><span class="pre">pvalues</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a></span><a class="headerlink" href="#arch.unitroot.cointegration.DynamicOLSResults.pvalues" title="Link to this definition">¶</a></dt>
 <dd><p>P-value of the parameters in the cointegrating vector</p>
 </dd></dl>
 
@@ -1119,7 +1119,7 @@ <h1 id="arch-unitroot-cointegration-dynamicolsresults-pvalues">arch.unitroot.coi
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
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   </body>
diff --git a/devel/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.resid.html b/devel/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.resid.html
index 693cc9b086..93646cf8fe 100644
--- a/devel/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.resid.html
+++ b/devel/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.resid.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration.DynamicOLSResults.resid - arch v6.2.1 (+21)</title>
+        <title>arch.unitroot.cointegration.DynamicOLSResults.resid - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -1001,7 +1001,7 @@
 <h1 id="arch-unitroot-cointegration-dynamicolsresults-resid">arch.unitroot.cointegration.DynamicOLSResults.resid<a class="headerlink" href="#arch-unitroot-cointegration-dynamicolsresults-resid" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.unitroot.cointegration.DynamicOLSResults.resid">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">DynamicOLSResults.</span></span><span class="sig-name descname"><span class="pre">resid</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span><a class="headerlink" href="#arch.unitroot.cointegration.DynamicOLSResults.resid" title="Link to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">DynamicOLSResults.</span></span><span class="sig-name descname"><span class="pre">resid</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a></span><a class="headerlink" href="#arch.unitroot.cointegration.DynamicOLSResults.resid" title="Link to this definition">¶</a></dt>
 <dd><p>The model residuals</p>
 </dd></dl>
 
@@ -1119,7 +1119,7 @@ <h1 id="arch-unitroot-cointegration-dynamicolsresults-resid">arch.unitroot.coint
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/devel/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.residual_variance.html b/devel/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.residual_variance.html
index 392e22e4c6..4284ef9c33 100644
--- a/devel/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.residual_variance.html
+++ b/devel/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.residual_variance.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration.DynamicOLSResults.residual_variance - arch v6.2.1 (+21)</title>
+        <title>arch.unitroot.cointegration.DynamicOLSResults.residual_variance - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -1175,7 +1175,7 @@ <h1 id="arch-unitroot-cointegration-dynamicolsresults-residual-variance">arch.un
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
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         <script id="MathJax-script" src="../../../_static/mathjax/tex-mml-chtml.js?v=cadf963e"></script>
diff --git a/devel/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.rsquared.html b/devel/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.rsquared.html
index 8e780920ba..7fb7995c78 100644
--- a/devel/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.rsquared.html
+++ b/devel/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.rsquared.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration.DynamicOLSResults.rsquared - arch v6.2.1 (+21)</title>
+        <title>arch.unitroot.cointegration.DynamicOLSResults.rsquared - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -1119,7 +1119,7 @@ <h1 id="arch-unitroot-cointegration-dynamicolsresults-rsquared">arch.unitroot.co
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/devel/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.rsquared_adj.html b/devel/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.rsquared_adj.html
index 5c07227cf0..7a75b34d22 100644
--- a/devel/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.rsquared_adj.html
+++ b/devel/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.rsquared_adj.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration.DynamicOLSResults.rsquared_adj - arch v6.2.1 (+21)</title>
+        <title>arch.unitroot.cointegration.DynamicOLSResults.rsquared_adj - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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@@ -1119,7 +1119,7 @@ <h1 id="arch-unitroot-cointegration-dynamicolsresults-rsquared-adj">arch.unitroo
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
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diff --git a/devel/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.std_errors.html b/devel/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.std_errors.html
index 20da34b02e..b1b1bafe2f 100644
--- a/devel/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.std_errors.html
+++ b/devel/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.std_errors.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration.DynamicOLSResults.std_errors - arch v6.2.1 (+21)</title>
+        <title>arch.unitroot.cointegration.DynamicOLSResults.std_errors - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
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+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
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@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
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@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
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-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
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-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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     <div class="md-nav__source">
@@ -1001,7 +1001,7 @@
 <h1 id="arch-unitroot-cointegration-dynamicolsresults-std-errors">arch.unitroot.cointegration.DynamicOLSResults.std_errors<a class="headerlink" href="#arch-unitroot-cointegration-dynamicolsresults-std-errors" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.unitroot.cointegration.DynamicOLSResults.std_errors">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">DynamicOLSResults.</span></span><span class="sig-name descname"><span class="pre">std_errors</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span><a class="headerlink" href="#arch.unitroot.cointegration.DynamicOLSResults.std_errors" title="Link to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">DynamicOLSResults.</span></span><span class="sig-name descname"><span class="pre">std_errors</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a></span><a class="headerlink" href="#arch.unitroot.cointegration.DynamicOLSResults.std_errors" title="Link to this definition">¶</a></dt>
 <dd><p>Standard errors  of the parameters in the cointegrating vector</p>
 </dd></dl>
 
@@ -1119,7 +1119,7 @@ <h1 id="arch-unitroot-cointegration-dynamicolsresults-std-errors">arch.unitroot.
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
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diff --git a/devel/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.summary.html b/devel/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.summary.html
index 5d3945d3b1..c3aae02572 100644
--- a/devel/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.summary.html
+++ b/devel/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.summary.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration.DynamicOLSResults.summary - arch v6.2.1 (+21)</title>
+        <title>arch.unitroot.cointegration.DynamicOLSResults.summary - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
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+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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     <div class="md-nav__source">
@@ -1081,7 +1081,7 @@
 <h1 id="arch-unitroot-cointegration-dynamicolsresults-summary">arch.unitroot.cointegration.DynamicOLSResults.summary<a class="headerlink" href="#arch-unitroot-cointegration-dynamicolsresults-summary" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight py" id="arch.unitroot.cointegration.DynamicOLSResults.summary">
-<span class="sig-prename descclassname"><span class="pre">DynamicOLSResults.</span></span><span class="sig-name descname"><span class="pre">summary</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.DynamicOLSResults.summary.full" title="arch.unitroot.cointegration.DynamicOLSResults.summary.full (Python parameter) — Flag indicating whether to include all estimated parameters (True) or only the parameters of the cointegrating vector"><span class="n"><span class="pre">full</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://www.statsmodels.org/dev/generated/statsmodels.iolib.summary.Summary.html#statsmodels.iolib.summary.Summary" title="(in statsmodels 0.15.0 (+59) v0.15.0 (+59))"><span class="pre">Summary</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/unitroot/cointegration.html#DynamicOLSResults.summary"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.unitroot.cointegration.DynamicOLSResults.summary" title="Link to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">DynamicOLSResults.</span></span><span class="sig-name descname"><span class="pre">summary</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.DynamicOLSResults.summary.full" title="arch.unitroot.cointegration.DynamicOLSResults.summary.full (Python parameter) — Flag indicating whether to include all estimated parameters (True) or only the parameters of the cointegrating vector"><span class="n"><span class="pre">full</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://www.statsmodels.org/dev/generated/statsmodels.iolib.summary.Summary.html#statsmodels.iolib.summary.Summary" title="(in statsmodels 0.15.0 (+132) v0.15.0 (+132))"><span class="pre">Summary</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/unitroot/cointegration.html#DynamicOLSResults.summary"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.unitroot.cointegration.DynamicOLSResults.summary" title="Link to this definition">¶</a></dt>
 <dd><p>Summary of the model, containing estimated parameters and std. errors</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.unitroot.cointegration.DynamicOLSResults.summary-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.unitroot.cointegration.DynamicOLSResults.summary-parameters" title="Permalink to this headline">¶</a></dt>
@@ -1215,7 +1215,7 @@ <h1 id="arch-unitroot-cointegration-dynamicolsresults-summary">arch.unitroot.coi
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
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   </body>
diff --git a/devel/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.tvalues.html b/devel/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.tvalues.html
index d2b120fd14..95527f2a3e 100644
--- a/devel/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.tvalues.html
+++ b/devel/unitroot/generated/generated/arch.unitroot.cointegration.DynamicOLSResults.tvalues.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration.DynamicOLSResults.tvalues - arch v6.2.1 (+21)</title>
+        <title>arch.unitroot.cointegration.DynamicOLSResults.tvalues - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -1001,7 +1001,7 @@
 <h1 id="arch-unitroot-cointegration-dynamicolsresults-tvalues">arch.unitroot.cointegration.DynamicOLSResults.tvalues<a class="headerlink" href="#arch-unitroot-cointegration-dynamicolsresults-tvalues" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.unitroot.cointegration.DynamicOLSResults.tvalues">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">DynamicOLSResults.</span></span><span class="sig-name descname"><span class="pre">tvalues</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span><a class="headerlink" href="#arch.unitroot.cointegration.DynamicOLSResults.tvalues" title="Link to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">DynamicOLSResults.</span></span><span class="sig-name descname"><span class="pre">tvalues</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a></span><a class="headerlink" href="#arch.unitroot.cointegration.DynamicOLSResults.tvalues" title="Link to this definition">¶</a></dt>
 <dd><p>T-statistics of the parameters in the cointegrating vector</p>
 </dd></dl>
 
@@ -1119,7 +1119,7 @@ <h1 id="arch-unitroot-cointegration-dynamicolsresults-tvalues">arch.unitroot.coi
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/devel/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.alternative_hypothesis.html b/devel/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.alternative_hypothesis.html
index 4e707a4f71..8602f3ee22 100644
--- a/devel/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.alternative_hypothesis.html
+++ b/devel/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.alternative_hypothesis.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration.EngleGrangerTestResults.alternative_hypothesis - arch v6.2.1 (+21)</title>
+        <title>arch.unitroot.cointegration.EngleGrangerTestResults.alternative_hypothesis - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -1074,7 +1074,7 @@ <h1 id="arch-unitroot-cointegration-englegrangertestresults-alternative-hypothes
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/devel/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.cointegrating_vector.html b/devel/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.cointegrating_vector.html
index 4a9c81b4f9..a79cd72648 100644
--- a/devel/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.cointegrating_vector.html
+++ b/devel/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.cointegrating_vector.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration.EngleGrangerTestResults.cointegrating_vector - arch v6.2.1 (+21)</title>
+        <title>arch.unitroot.cointegration.EngleGrangerTestResults.cointegrating_vector - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -956,7 +956,7 @@
 <h1 id="arch-unitroot-cointegration-englegrangertestresults-cointegrating-vector">arch.unitroot.cointegration.EngleGrangerTestResults.cointegrating_vector<a class="headerlink" href="#arch-unitroot-cointegration-englegrangertestresults-cointegrating-vector" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.unitroot.cointegration.EngleGrangerTestResults.cointegrating_vector">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">EngleGrangerTestResults.</span></span><span class="sig-name descname"><span class="pre">cointegrating_vector</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span><a class="headerlink" href="#arch.unitroot.cointegration.EngleGrangerTestResults.cointegrating_vector" title="Link to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">EngleGrangerTestResults.</span></span><span class="sig-name descname"><span class="pre">cointegrating_vector</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a></span><a class="headerlink" href="#arch.unitroot.cointegration.EngleGrangerTestResults.cointegrating_vector" title="Link to this definition">¶</a></dt>
 <dd><p>The estimated cointegrating vector.</p>
 </dd></dl>
 
@@ -1074,7 +1074,7 @@ <h1 id="arch-unitroot-cointegration-englegrangertestresults-cointegrating-vector
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
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   </body>
diff --git a/devel/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.critical_values.html b/devel/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.critical_values.html
index 09b77e7b34..1287f49540 100644
--- a/devel/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.critical_values.html
+++ b/devel/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.critical_values.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration.EngleGrangerTestResults.critical_values - arch v6.2.1 (+21)</title>
+        <title>arch.unitroot.cointegration.EngleGrangerTestResults.critical_values - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -996,7 +996,7 @@
 <h1 id="arch-unitroot-cointegration-englegrangertestresults-critical-values">arch.unitroot.cointegration.EngleGrangerTestResults.critical_values<a class="headerlink" href="#arch-unitroot-cointegration-englegrangertestresults-critical-values" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.unitroot.cointegration.EngleGrangerTestResults.critical_values">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">EngleGrangerTestResults.</span></span><span class="sig-name descname"><span class="pre">critical_values</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span><a class="headerlink" href="#arch.unitroot.cointegration.EngleGrangerTestResults.critical_values" title="Link to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">EngleGrangerTestResults.</span></span><span class="sig-name descname"><span class="pre">critical_values</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a></span><a class="headerlink" href="#arch.unitroot.cointegration.EngleGrangerTestResults.critical_values" title="Link to this definition">¶</a></dt>
 <dd><p>Critical Values</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.unitroot.cointegration.EngleGrangerTestResults.critical_values-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.unitroot.cointegration.EngleGrangerTestResults.critical_values-returns" title="Permalink to this headline">¶</a></dt>
@@ -1004,7 +1004,7 @@ <h1 id="arch-unitroot-cointegration-englegrangertestresults-critical-values">arc
 of the test statistic.</p>
 </dd>
 <dt class="field-even" id="arch.unitroot.cointegration.EngleGrangerTestResults.critical_values-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.unitroot.cointegration.EngleGrangerTestResults.critical_values-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><code class="xref py py-class docutils literal notranslate"><span class="pre">pandas.Series</span></code></a></p>
+<dd class="field-even"><p><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><code class="xref py py-class docutils literal notranslate"><span class="pre">pandas.Series</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1123,7 +1123,7 @@ <h1 id="arch-unitroot-cointegration-englegrangertestresults-critical-values">arc
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/devel/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.distribution_order.html b/devel/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.distribution_order.html
index 4bc9641ed4..f918538e2f 100644
--- a/devel/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.distribution_order.html
+++ b/devel/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.distribution_order.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration.EngleGrangerTestResults.distribution_order - arch v6.2.1 (+21)</title>
+        <title>arch.unitroot.cointegration.EngleGrangerTestResults.distribution_order - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
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-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
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diff --git a/devel/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.lags.html b/devel/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.lags.html
index a8bb0225be..257ceb745e 100644
--- a/devel/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.lags.html
+++ b/devel/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.lags.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration.EngleGrangerTestResults.lags - arch v6.2.1 (+21)</title>
+        <title>arch.unitroot.cointegration.EngleGrangerTestResults.lags - arch v6.2.1 (+23)</title>
       
     
 
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
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@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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+            arch v6.2.1 (+23)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
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diff --git a/devel/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.max_lags.html b/devel/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.max_lags.html
index 4f83b3d10e..0672f46822 100644
--- a/devel/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.max_lags.html
+++ b/devel/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.max_lags.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration.EngleGrangerTestResults.max_lags - arch v6.2.1 (+21)</title>
+        <title>arch.unitroot.cointegration.EngleGrangerTestResults.max_lags - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
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+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
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diff --git a/devel/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.name.html b/devel/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.name.html
index 69ebf941ab..fd21928a7f 100644
--- a/devel/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.name.html
+++ b/devel/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.name.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration.EngleGrangerTestResults.name - arch v6.2.1 (+21)</title>
+        <title>arch.unitroot.cointegration.EngleGrangerTestResults.name - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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         <div class="md-header__topic">
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+            arch v6.2.1 (+23)
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@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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+    arch v6.2.1 (+23)
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-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
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diff --git a/devel/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.null_hypothesis.html b/devel/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.null_hypothesis.html
index 851d650b92..7e31a9921f 100644
--- a/devel/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.null_hypothesis.html
+++ b/devel/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.null_hypothesis.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration.EngleGrangerTestResults.null_hypothesis - arch v6.2.1 (+21)</title>
+        <title>arch.unitroot.cointegration.EngleGrangerTestResults.null_hypothesis - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
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@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
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+            arch v6.2.1 (+23)
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@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
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-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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+    arch v6.2.1 (+23)
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-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
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diff --git a/devel/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.plot.html b/devel/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.plot.html
index 49022691a8..f68316ffa5 100644
--- a/devel/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.plot.html
+++ b/devel/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.plot.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration.EngleGrangerTestResults.plot - arch v6.2.1 (+21)</title>
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@@ -47,7 +47,7 @@
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@@ -102,7 +102,7 @@
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+    arch v6.2.1 (+23)
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@@ -1050,12 +1050,12 @@
 <h1 id="arch-unitroot-cointegration-englegrangertestresults-plot">arch.unitroot.cointegration.EngleGrangerTestResults.plot<a class="headerlink" href="#arch-unitroot-cointegration-englegrangertestresults-plot" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.unitroot.cointegration.EngleGrangerTestResults.plot">
-<span class="sig-prename descclassname"><span class="pre">EngleGrangerTestResults.</span></span><span class="sig-name descname"><span class="pre">plot</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.EngleGrangerTestResults.plot.axes" title="arch.unitroot.cointegration.EngleGrangerTestResults.plot.axes (Python parameter) — matplotlib axes instance to hold the figure."><span class="n"><span class="pre">axes</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="(in Matplotlib v3.8.0)"><span class="pre">Axes</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.EngleGrangerTestResults.plot.title" title="arch.unitroot.cointegration.EngleGrangerTestResults.plot.title (Python parameter) — Title for the figure."><span class="n"><span class="pre">title</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://matplotlib.org/stable/api/figure_api.html#matplotlib.figure.Figure" title="(in Matplotlib v3.8.0)"><span class="pre">Figure</span></a></span></span></span><a class="headerlink" href="#arch.unitroot.cointegration.EngleGrangerTestResults.plot" title="Link to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">EngleGrangerTestResults.</span></span><span class="sig-name descname"><span class="pre">plot</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.EngleGrangerTestResults.plot.axes" title="arch.unitroot.cointegration.EngleGrangerTestResults.plot.axes (Python parameter) — matplotlib axes instance to hold the figure."><span class="n"><span class="pre">axes</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="(in Matplotlib v3.8.2)"><span class="pre">Axes</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.EngleGrangerTestResults.plot.title" title="arch.unitroot.cointegration.EngleGrangerTestResults.plot.title (Python parameter) — Title for the figure."><span class="n"><span class="pre">title</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://matplotlib.org/stable/api/figure_api.html#matplotlib.figure.Figure" title="(in Matplotlib v3.8.2)"><span class="pre">Figure</span></a></span></span></span><a class="headerlink" href="#arch.unitroot.cointegration.EngleGrangerTestResults.plot" title="Link to this definition">¶</a></dt>
 <dd><p>Plot the cointegration residuals.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.unitroot.cointegration.EngleGrangerTestResults.plot-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.unitroot.cointegration.EngleGrangerTestResults.plot-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.unitroot.cointegration.EngleGrangerTestResults.plot.axes"><span class="n sig-name">axes</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="(in Matplotlib v3.8.0)">Axes</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.unitroot.cointegration.EngleGrangerTestResults.plot.axes" title="Permalink to this definition">¶</a></dt><dd><p>matplotlib axes instance to hold the figure.</p>
+<dt id="arch.unitroot.cointegration.EngleGrangerTestResults.plot.axes"><span class="n sig-name">axes</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="(in Matplotlib v3.8.2)">Axes</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.unitroot.cointegration.EngleGrangerTestResults.plot.axes" title="Permalink to this definition">¶</a></dt><dd><p>matplotlib axes instance to hold the figure.</p>
 </dd>
 <dt id="arch.unitroot.cointegration.EngleGrangerTestResults.plot.title"><span class="n sig-name">title</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)">str</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.unitroot.cointegration.EngleGrangerTestResults.plot.title" title="Permalink to this definition">¶</a></dt><dd><p>Title for the figure.</p>
 </dd>
@@ -1065,7 +1065,7 @@ <h1 id="arch-unitroot-cointegration-englegrangertestresults-plot">arch.unitroot.
 <dd class="field-even"><p>The matplotlib Figure instance.</p>
 </dd>
 <dt class="field-odd" id="arch.unitroot.cointegration.EngleGrangerTestResults.plot-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.unitroot.cointegration.EngleGrangerTestResults.plot-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p><a class="reference external" href="https://matplotlib.org/stable/api/figure_api.html#matplotlib.figure.Figure" title="(in Matplotlib v3.8.0)"><code class="xref py py-class docutils literal notranslate"><span class="pre">matplotlib.figure.Figure</span></code></a></p>
+<dd class="field-odd"><p><a class="reference external" href="https://matplotlib.org/stable/api/figure_api.html#matplotlib.figure.Figure" title="(in Matplotlib v3.8.2)"><code class="xref py py-class docutils literal notranslate"><span class="pre">matplotlib.figure.Figure</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1184,7 +1184,7 @@ <h1 id="arch-unitroot-cointegration-englegrangertestresults-plot">arch.unitroot.
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diff --git a/devel/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.pvalue.html b/devel/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.pvalue.html
index b662a2a969..7f946e8f90 100644
--- a/devel/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.pvalue.html
+++ b/devel/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.pvalue.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration.EngleGrangerTestResults.pvalue - arch v6.2.1 (+21)</title>
+        <title>arch.unitroot.cointegration.EngleGrangerTestResults.pvalue - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
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@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
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-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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     <div class="md-nav__source">
@@ -1074,7 +1074,7 @@ <h1 id="arch-unitroot-cointegration-englegrangertestresults-pvalue">arch.unitroo
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-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
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diff --git a/devel/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.resid.html b/devel/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.resid.html
index 5b2e7b1603..71adb02777 100644
--- a/devel/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.resid.html
+++ b/devel/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.resid.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration.EngleGrangerTestResults.resid - arch v6.2.1 (+21)</title>
+        <title>arch.unitroot.cointegration.EngleGrangerTestResults.resid - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -956,7 +956,7 @@
 <h1 id="arch-unitroot-cointegration-englegrangertestresults-resid">arch.unitroot.cointegration.EngleGrangerTestResults.resid<a class="headerlink" href="#arch-unitroot-cointegration-englegrangertestresults-resid" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.unitroot.cointegration.EngleGrangerTestResults.resid">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">EngleGrangerTestResults.</span></span><span class="sig-name descname"><span class="pre">resid</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span><a class="headerlink" href="#arch.unitroot.cointegration.EngleGrangerTestResults.resid" title="Link to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">EngleGrangerTestResults.</span></span><span class="sig-name descname"><span class="pre">resid</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a></span><a class="headerlink" href="#arch.unitroot.cointegration.EngleGrangerTestResults.resid" title="Link to this definition">¶</a></dt>
 <dd><p>The residual from the cointegrating regression.</p>
 </dd></dl>
 
@@ -1074,7 +1074,7 @@ <h1 id="arch-unitroot-cointegration-englegrangertestresults-resid">arch.unitroot
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/devel/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.rho.html b/devel/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.rho.html
index 695706b48a..330a39c1cd 100644
--- a/devel/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.rho.html
+++ b/devel/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.rho.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration.EngleGrangerTestResults.rho - arch v6.2.1 (+21)</title>
+        <title>arch.unitroot.cointegration.EngleGrangerTestResults.rho - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -1128,7 +1128,7 @@ <h1 id="arch-unitroot-cointegration-englegrangertestresults-rho">arch.unitroot.c
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
         <script>window.MathJax = {"tex": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true}, "options": {"ignoreHtmlClass": "tex2jax_ignore|mathjax_ignore|document", "processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
         <script id="MathJax-script" src="../../../_static/mathjax/tex-mml-chtml.js?v=cadf963e"></script>
diff --git a/devel/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.stat.html b/devel/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.stat.html
index c38abf505f..243797fb97 100644
--- a/devel/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.stat.html
+++ b/devel/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.stat.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration.EngleGrangerTestResults.stat - arch v6.2.1 (+21)</title>
+        <title>arch.unitroot.cointegration.EngleGrangerTestResults.stat - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -1074,7 +1074,7 @@ <h1 id="arch-unitroot-cointegration-englegrangertestresults-stat">arch.unitroot.
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-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/devel/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.summary.html b/devel/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.summary.html
index 19b6796600..191cd7fabd 100644
--- a/devel/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.summary.html
+++ b/devel/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.summary.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration.EngleGrangerTestResults.summary - arch v6.2.1 (+21)</title>
+        <title>arch.unitroot.cointegration.EngleGrangerTestResults.summary - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -956,7 +956,7 @@
 <h1 id="arch-unitroot-cointegration-englegrangertestresults-summary">arch.unitroot.cointegration.EngleGrangerTestResults.summary<a class="headerlink" href="#arch-unitroot-cointegration-englegrangertestresults-summary" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight py" id="arch.unitroot.cointegration.EngleGrangerTestResults.summary">
-<span class="sig-prename descclassname"><span class="pre">EngleGrangerTestResults.</span></span><span class="sig-name descname"><span class="pre">summary</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://www.statsmodels.org/dev/generated/statsmodels.iolib.summary.Summary.html#statsmodels.iolib.summary.Summary" title="(in statsmodels 0.15.0 (+59) v0.15.0 (+59))"><span class="pre">Summary</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/unitroot/_engle_granger.html#EngleGrangerTestResults.summary"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.unitroot.cointegration.EngleGrangerTestResults.summary" title="Link to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">EngleGrangerTestResults.</span></span><span class="sig-name descname"><span class="pre">summary</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://www.statsmodels.org/dev/generated/statsmodels.iolib.summary.Summary.html#statsmodels.iolib.summary.Summary" title="(in statsmodels 0.15.0 (+132) v0.15.0 (+132))"><span class="pre">Summary</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/unitroot/_engle_granger.html#EngleGrangerTestResults.summary"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.unitroot.cointegration.EngleGrangerTestResults.summary" title="Link to this definition">¶</a></dt>
 <dd><p>Summary of test, containing statistic, p-value and critical values</p>
 </dd></dl>
 
@@ -1074,7 +1074,7 @@ <h1 id="arch-unitroot-cointegration-englegrangertestresults-summary">arch.unitro
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
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diff --git a/devel/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.trend.html b/devel/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.trend.html
index 3c227ecef1..490f8385a0 100644
--- a/devel/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.trend.html
+++ b/devel/unitroot/generated/generated/arch.unitroot.cointegration.EngleGrangerTestResults.trend.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration.EngleGrangerTestResults.trend - arch v6.2.1 (+21)</title>
+        <title>arch.unitroot.cointegration.EngleGrangerTestResults.trend - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
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@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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@@ -189,10 +189,10 @@
 
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   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
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diff --git a/devel/unitroot/generated/generated/arch.unitroot.cointegration.FullyModifiedOLS.fit.html b/devel/unitroot/generated/generated/arch.unitroot.cointegration.FullyModifiedOLS.fit.html
index 04f7f9ef92..15c2559a15 100644
--- a/devel/unitroot/generated/generated/arch.unitroot.cointegration.FullyModifiedOLS.fit.html
+++ b/devel/unitroot/generated/generated/arch.unitroot.cointegration.FullyModifiedOLS.fit.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration.FullyModifiedOLS.fit - arch v6.2.1 (+21)</title>
+        <title>arch.unitroot.cointegration.FullyModifiedOLS.fit - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
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@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
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-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -947,7 +947,7 @@ <h1 id="arch-unitroot-cointegration-fullymodifiedols-fit">arch.unitroot.cointegr
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
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diff --git a/devel/unitroot/generated/generated/arch.unitroot.cointegration.PhillipsOuliarisTestResults.alternative_hypothesis.html b/devel/unitroot/generated/generated/arch.unitroot.cointegration.PhillipsOuliarisTestResults.alternative_hypothesis.html
index a129ecc6b9..60ef349fb4 100644
--- a/devel/unitroot/generated/generated/arch.unitroot.cointegration.PhillipsOuliarisTestResults.alternative_hypothesis.html
+++ b/devel/unitroot/generated/generated/arch.unitroot.cointegration.PhillipsOuliarisTestResults.alternative_hypothesis.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration.PhillipsOuliarisTestResults.alternative_hypothesis - arch v6.2.1 (+21)</title>
+        <title>arch.unitroot.cointegration.PhillipsOuliarisTestResults.alternative_hypothesis - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
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@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
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@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
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-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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@@ -1059,7 +1059,7 @@ <h1 id="arch-unitroot-cointegration-phillipsouliaristestresults-alternative-hypo
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-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
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   </body>
diff --git a/devel/unitroot/generated/generated/arch.unitroot.cointegration.PhillipsOuliarisTestResults.bandwidth.html b/devel/unitroot/generated/generated/arch.unitroot.cointegration.PhillipsOuliarisTestResults.bandwidth.html
index bf04bd3654..32d66388f6 100644
--- a/devel/unitroot/generated/generated/arch.unitroot.cointegration.PhillipsOuliarisTestResults.bandwidth.html
+++ b/devel/unitroot/generated/generated/arch.unitroot.cointegration.PhillipsOuliarisTestResults.bandwidth.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration.PhillipsOuliarisTestResults.bandwidth - arch v6.2.1 (+21)</title>
+        <title>arch.unitroot.cointegration.PhillipsOuliarisTestResults.bandwidth - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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     <div class="md-nav__source">
@@ -1059,7 +1059,7 @@ <h1 id="arch-unitroot-cointegration-phillipsouliaristestresults-bandwidth">arch.
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
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   </body>
diff --git a/devel/unitroot/generated/generated/arch.unitroot.cointegration.PhillipsOuliarisTestResults.cointegrating_vector.html b/devel/unitroot/generated/generated/arch.unitroot.cointegration.PhillipsOuliarisTestResults.cointegrating_vector.html
index dad734d083..936fcb1c17 100644
--- a/devel/unitroot/generated/generated/arch.unitroot.cointegration.PhillipsOuliarisTestResults.cointegrating_vector.html
+++ b/devel/unitroot/generated/generated/arch.unitroot.cointegration.PhillipsOuliarisTestResults.cointegrating_vector.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration.PhillipsOuliarisTestResults.cointegrating_vector - arch v6.2.1 (+21)</title>
+        <title>arch.unitroot.cointegration.PhillipsOuliarisTestResults.cointegrating_vector - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -941,7 +941,7 @@
 <h1 id="arch-unitroot-cointegration-phillipsouliaristestresults-cointegrating-vector">arch.unitroot.cointegration.PhillipsOuliarisTestResults.cointegrating_vector<a class="headerlink" href="#arch-unitroot-cointegration-phillipsouliaristestresults-cointegrating-vector" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.unitroot.cointegration.PhillipsOuliarisTestResults.cointegrating_vector">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">PhillipsOuliarisTestResults.</span></span><span class="sig-name descname"><span class="pre">cointegrating_vector</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span><a class="headerlink" href="#arch.unitroot.cointegration.PhillipsOuliarisTestResults.cointegrating_vector" title="Link to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">PhillipsOuliarisTestResults.</span></span><span class="sig-name descname"><span class="pre">cointegrating_vector</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a></span><a class="headerlink" href="#arch.unitroot.cointegration.PhillipsOuliarisTestResults.cointegrating_vector" title="Link to this definition">¶</a></dt>
 <dd><p>The estimated cointegrating vector.</p>
 </dd></dl>
 
@@ -1059,7 +1059,7 @@ <h1 id="arch-unitroot-cointegration-phillipsouliaristestresults-cointegrating-ve
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
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diff --git a/devel/unitroot/generated/generated/arch.unitroot.cointegration.PhillipsOuliarisTestResults.critical_values.html b/devel/unitroot/generated/generated/arch.unitroot.cointegration.PhillipsOuliarisTestResults.critical_values.html
index 6b86dd1fe5..2f102086d3 100644
--- a/devel/unitroot/generated/generated/arch.unitroot.cointegration.PhillipsOuliarisTestResults.critical_values.html
+++ b/devel/unitroot/generated/generated/arch.unitroot.cointegration.PhillipsOuliarisTestResults.critical_values.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration.PhillipsOuliarisTestResults.critical_values - arch v6.2.1 (+21)</title>
+        <title>arch.unitroot.cointegration.PhillipsOuliarisTestResults.critical_values - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
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@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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     <div class="md-nav__source">
@@ -981,7 +981,7 @@
 <h1 id="arch-unitroot-cointegration-phillipsouliaristestresults-critical-values">arch.unitroot.cointegration.PhillipsOuliarisTestResults.critical_values<a class="headerlink" href="#arch-unitroot-cointegration-phillipsouliaristestresults-critical-values" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.unitroot.cointegration.PhillipsOuliarisTestResults.critical_values">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">PhillipsOuliarisTestResults.</span></span><span class="sig-name descname"><span class="pre">critical_values</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span><a class="headerlink" href="#arch.unitroot.cointegration.PhillipsOuliarisTestResults.critical_values" title="Link to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">PhillipsOuliarisTestResults.</span></span><span class="sig-name descname"><span class="pre">critical_values</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a></span><a class="headerlink" href="#arch.unitroot.cointegration.PhillipsOuliarisTestResults.critical_values" title="Link to this definition">¶</a></dt>
 <dd><p>Critical Values</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.unitroot.cointegration.PhillipsOuliarisTestResults.critical_values-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.unitroot.cointegration.PhillipsOuliarisTestResults.critical_values-returns" title="Permalink to this headline">¶</a></dt>
@@ -989,7 +989,7 @@ <h1 id="arch-unitroot-cointegration-phillipsouliaristestresults-critical-values"
 of the test statistic.</p>
 </dd>
 <dt class="field-even" id="arch.unitroot.cointegration.PhillipsOuliarisTestResults.critical_values-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.unitroot.cointegration.PhillipsOuliarisTestResults.critical_values-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-even"><p><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><code class="xref py py-class docutils literal notranslate"><span class="pre">pandas.Series</span></code></a></p>
+<dd class="field-even"><p><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><code class="xref py py-class docutils literal notranslate"><span class="pre">pandas.Series</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1108,7 +1108,7 @@ <h1 id="arch-unitroot-cointegration-phillipsouliaristestresults-critical-values"
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
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   </body>
diff --git a/devel/unitroot/generated/generated/arch.unitroot.cointegration.PhillipsOuliarisTestResults.distribution_order.html b/devel/unitroot/generated/generated/arch.unitroot.cointegration.PhillipsOuliarisTestResults.distribution_order.html
index 4c721c3ea0..e079b6b427 100644
--- a/devel/unitroot/generated/generated/arch.unitroot.cointegration.PhillipsOuliarisTestResults.distribution_order.html
+++ b/devel/unitroot/generated/generated/arch.unitroot.cointegration.PhillipsOuliarisTestResults.distribution_order.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration.PhillipsOuliarisTestResults.distribution_order - arch v6.2.1 (+21)</title>
+        <title>arch.unitroot.cointegration.PhillipsOuliarisTestResults.distribution_order - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -1059,7 +1059,7 @@ <h1 id="arch-unitroot-cointegration-phillipsouliaristestresults-distribution-ord
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/devel/unitroot/generated/generated/arch.unitroot.cointegration.PhillipsOuliarisTestResults.kernel.html b/devel/unitroot/generated/generated/arch.unitroot.cointegration.PhillipsOuliarisTestResults.kernel.html
index 31f6571939..35a2b093e4 100644
--- a/devel/unitroot/generated/generated/arch.unitroot.cointegration.PhillipsOuliarisTestResults.kernel.html
+++ b/devel/unitroot/generated/generated/arch.unitroot.cointegration.PhillipsOuliarisTestResults.kernel.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration.PhillipsOuliarisTestResults.kernel - arch v6.2.1 (+21)</title>
+        <title>arch.unitroot.cointegration.PhillipsOuliarisTestResults.kernel - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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     <div class="md-nav__source">
@@ -1059,7 +1059,7 @@ <h1 id="arch-unitroot-cointegration-phillipsouliaristestresults-kernel">arch.uni
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
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   </body>
diff --git a/devel/unitroot/generated/generated/arch.unitroot.cointegration.PhillipsOuliarisTestResults.name.html b/devel/unitroot/generated/generated/arch.unitroot.cointegration.PhillipsOuliarisTestResults.name.html
index 423adddcce..040f674268 100644
--- a/devel/unitroot/generated/generated/arch.unitroot.cointegration.PhillipsOuliarisTestResults.name.html
+++ b/devel/unitroot/generated/generated/arch.unitroot.cointegration.PhillipsOuliarisTestResults.name.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration.PhillipsOuliarisTestResults.name - arch v6.2.1 (+21)</title>
+        <title>arch.unitroot.cointegration.PhillipsOuliarisTestResults.name - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
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@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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@@ -1059,7 +1059,7 @@ <h1 id="arch-unitroot-cointegration-phillipsouliaristestresults-name">arch.unitr
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diff --git a/devel/unitroot/generated/generated/arch.unitroot.cointegration.PhillipsOuliarisTestResults.null_hypothesis.html b/devel/unitroot/generated/generated/arch.unitroot.cointegration.PhillipsOuliarisTestResults.null_hypothesis.html
index e8ff8b9e97..953f26e779 100644
--- a/devel/unitroot/generated/generated/arch.unitroot.cointegration.PhillipsOuliarisTestResults.null_hypothesis.html
+++ b/devel/unitroot/generated/generated/arch.unitroot.cointegration.PhillipsOuliarisTestResults.null_hypothesis.html
@@ -15,7 +15,7 @@
     
   
       
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diff --git a/devel/unitroot/generated/generated/arch.unitroot.cointegration.PhillipsOuliarisTestResults.plot.html b/devel/unitroot/generated/generated/arch.unitroot.cointegration.PhillipsOuliarisTestResults.plot.html
index 4232c447f8..29215330a3 100644
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+++ b/devel/unitroot/generated/generated/arch.unitroot.cointegration.PhillipsOuliarisTestResults.plot.html
@@ -15,7 +15,7 @@
     
   
       
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+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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@@ -1035,12 +1035,12 @@
 <h1 id="arch-unitroot-cointegration-phillipsouliaristestresults-plot">arch.unitroot.cointegration.PhillipsOuliarisTestResults.plot<a class="headerlink" href="#arch-unitroot-cointegration-phillipsouliaristestresults-plot" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.unitroot.cointegration.PhillipsOuliarisTestResults.plot">
-<span class="sig-prename descclassname"><span class="pre">PhillipsOuliarisTestResults.</span></span><span class="sig-name descname"><span class="pre">plot</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.PhillipsOuliarisTestResults.plot.axes" title="arch.unitroot.cointegration.PhillipsOuliarisTestResults.plot.axes (Python parameter) — matplotlib axes instance to hold the figure."><span class="n"><span class="pre">axes</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="(in Matplotlib v3.8.0)"><span class="pre">Axes</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.PhillipsOuliarisTestResults.plot.title" title="arch.unitroot.cointegration.PhillipsOuliarisTestResults.plot.title (Python parameter) — Title for the figure."><span class="n"><span class="pre">title</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://matplotlib.org/stable/api/figure_api.html#matplotlib.figure.Figure" title="(in Matplotlib v3.8.0)"><span class="pre">Figure</span></a></span></span></span><a class="headerlink" href="#arch.unitroot.cointegration.PhillipsOuliarisTestResults.plot" title="Link to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">PhillipsOuliarisTestResults.</span></span><span class="sig-name descname"><span class="pre">plot</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.PhillipsOuliarisTestResults.plot.axes" title="arch.unitroot.cointegration.PhillipsOuliarisTestResults.plot.axes (Python parameter) — matplotlib axes instance to hold the figure."><span class="n"><span class="pre">axes</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="(in Matplotlib v3.8.2)"><span class="pre">Axes</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.unitroot.cointegration.PhillipsOuliarisTestResults.plot.title" title="arch.unitroot.cointegration.PhillipsOuliarisTestResults.plot.title (Python parameter) — Title for the figure."><span class="n"><span class="pre">title</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://matplotlib.org/stable/api/figure_api.html#matplotlib.figure.Figure" title="(in Matplotlib v3.8.2)"><span class="pre">Figure</span></a></span></span></span><a class="headerlink" href="#arch.unitroot.cointegration.PhillipsOuliarisTestResults.plot" title="Link to this definition">¶</a></dt>
 <dd><p>Plot the cointegration residuals.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.unitroot.cointegration.PhillipsOuliarisTestResults.plot-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.unitroot.cointegration.PhillipsOuliarisTestResults.plot-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.unitroot.cointegration.PhillipsOuliarisTestResults.plot.axes"><span class="n sig-name">axes</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="(in Matplotlib v3.8.0)">Axes</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.unitroot.cointegration.PhillipsOuliarisTestResults.plot.axes" title="Permalink to this definition">¶</a></dt><dd><p>matplotlib axes instance to hold the figure.</p>
+<dt id="arch.unitroot.cointegration.PhillipsOuliarisTestResults.plot.axes"><span class="n sig-name">axes</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes" title="(in Matplotlib v3.8.2)">Axes</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.unitroot.cointegration.PhillipsOuliarisTestResults.plot.axes" title="Permalink to this definition">¶</a></dt><dd><p>matplotlib axes instance to hold the figure.</p>
 </dd>
 <dt id="arch.unitroot.cointegration.PhillipsOuliarisTestResults.plot.title"><span class="n sig-name">title</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)">str</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.unitroot.cointegration.PhillipsOuliarisTestResults.plot.title" title="Permalink to this definition">¶</a></dt><dd><p>Title for the figure.</p>
 </dd>
@@ -1050,7 +1050,7 @@ <h1 id="arch-unitroot-cointegration-phillipsouliaristestresults-plot">arch.unitr
 <dd class="field-even"><p>The matplotlib Figure instance.</p>
 </dd>
 <dt class="field-odd" id="arch.unitroot.cointegration.PhillipsOuliarisTestResults.plot-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.unitroot.cointegration.PhillipsOuliarisTestResults.plot-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p><a class="reference external" href="https://matplotlib.org/stable/api/figure_api.html#matplotlib.figure.Figure" title="(in Matplotlib v3.8.0)"><code class="xref py py-class docutils literal notranslate"><span class="pre">matplotlib.figure.Figure</span></code></a></p>
+<dd class="field-odd"><p><a class="reference external" href="https://matplotlib.org/stable/api/figure_api.html#matplotlib.figure.Figure" title="(in Matplotlib v3.8.2)"><code class="xref py py-class docutils literal notranslate"><span class="pre">matplotlib.figure.Figure</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
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diff --git a/devel/unitroot/generated/generated/arch.unitroot.cointegration.PhillipsOuliarisTestResults.pvalue.html b/devel/unitroot/generated/generated/arch.unitroot.cointegration.PhillipsOuliarisTestResults.pvalue.html
index ec1223b197..c79e21efab 100644
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+++ b/devel/unitroot/generated/generated/arch.unitroot.cointegration.PhillipsOuliarisTestResults.pvalue.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration.PhillipsOuliarisTestResults.pvalue - arch v6.2.1 (+21)</title>
+        <title>arch.unitroot.cointegration.PhillipsOuliarisTestResults.pvalue - arch v6.2.1 (+23)</title>
       
     
 
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+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
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@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -1059,7 +1059,7 @@ <h1 id="arch-unitroot-cointegration-phillipsouliaristestresults-pvalue">arch.uni
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/devel/unitroot/generated/generated/arch.unitroot.cointegration.PhillipsOuliarisTestResults.resid.html b/devel/unitroot/generated/generated/arch.unitroot.cointegration.PhillipsOuliarisTestResults.resid.html
index 98acf6c9a5..bdc8d2c8c0 100644
--- a/devel/unitroot/generated/generated/arch.unitroot.cointegration.PhillipsOuliarisTestResults.resid.html
+++ b/devel/unitroot/generated/generated/arch.unitroot.cointegration.PhillipsOuliarisTestResults.resid.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration.PhillipsOuliarisTestResults.resid - arch v6.2.1 (+21)</title>
+        <title>arch.unitroot.cointegration.PhillipsOuliarisTestResults.resid - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -941,7 +941,7 @@
 <h1 id="arch-unitroot-cointegration-phillipsouliaristestresults-resid">arch.unitroot.cointegration.PhillipsOuliarisTestResults.resid<a class="headerlink" href="#arch-unitroot-cointegration-phillipsouliaristestresults-resid" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.unitroot.cointegration.PhillipsOuliarisTestResults.resid">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">PhillipsOuliarisTestResults.</span></span><span class="sig-name descname"><span class="pre">resid</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span><a class="headerlink" href="#arch.unitroot.cointegration.PhillipsOuliarisTestResults.resid" title="Link to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">PhillipsOuliarisTestResults.</span></span><span class="sig-name descname"><span class="pre">resid</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a></span><a class="headerlink" href="#arch.unitroot.cointegration.PhillipsOuliarisTestResults.resid" title="Link to this definition">¶</a></dt>
 <dd><p>The residual from the cointegrating regression.</p>
 </dd></dl>
 
@@ -1059,7 +1059,7 @@ <h1 id="arch-unitroot-cointegration-phillipsouliaristestresults-resid">arch.unit
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/devel/unitroot/generated/generated/arch.unitroot.cointegration.PhillipsOuliarisTestResults.stat.html b/devel/unitroot/generated/generated/arch.unitroot.cointegration.PhillipsOuliarisTestResults.stat.html
index 12d24e35eb..344186d972 100644
--- a/devel/unitroot/generated/generated/arch.unitroot.cointegration.PhillipsOuliarisTestResults.stat.html
+++ b/devel/unitroot/generated/generated/arch.unitroot.cointegration.PhillipsOuliarisTestResults.stat.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration.PhillipsOuliarisTestResults.stat - arch v6.2.1 (+21)</title>
+        <title>arch.unitroot.cointegration.PhillipsOuliarisTestResults.stat - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -1059,7 +1059,7 @@ <h1 id="arch-unitroot-cointegration-phillipsouliaristestresults-stat">arch.unitr
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/devel/unitroot/generated/generated/arch.unitroot.cointegration.PhillipsOuliarisTestResults.summary.html b/devel/unitroot/generated/generated/arch.unitroot.cointegration.PhillipsOuliarisTestResults.summary.html
index 5fcc466647..fc66276407 100644
--- a/devel/unitroot/generated/generated/arch.unitroot.cointegration.PhillipsOuliarisTestResults.summary.html
+++ b/devel/unitroot/generated/generated/arch.unitroot.cointegration.PhillipsOuliarisTestResults.summary.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration.PhillipsOuliarisTestResults.summary - arch v6.2.1 (+21)</title>
+        <title>arch.unitroot.cointegration.PhillipsOuliarisTestResults.summary - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -941,7 +941,7 @@
 <h1 id="arch-unitroot-cointegration-phillipsouliaristestresults-summary">arch.unitroot.cointegration.PhillipsOuliarisTestResults.summary<a class="headerlink" href="#arch-unitroot-cointegration-phillipsouliaristestresults-summary" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight py" id="arch.unitroot.cointegration.PhillipsOuliarisTestResults.summary">
-<span class="sig-prename descclassname"><span class="pre">PhillipsOuliarisTestResults.</span></span><span class="sig-name descname"><span class="pre">summary</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://www.statsmodels.org/dev/generated/statsmodels.iolib.summary.Summary.html#statsmodels.iolib.summary.Summary" title="(in statsmodels 0.15.0 (+59) v0.15.0 (+59))"><span class="pre">Summary</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/unitroot/_phillips_ouliaris.html#PhillipsOuliarisTestResults.summary"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.unitroot.cointegration.PhillipsOuliarisTestResults.summary" title="Link to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">PhillipsOuliarisTestResults.</span></span><span class="sig-name descname"><span class="pre">summary</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://www.statsmodels.org/dev/generated/statsmodels.iolib.summary.Summary.html#statsmodels.iolib.summary.Summary" title="(in statsmodels 0.15.0 (+132) v0.15.0 (+132))"><span class="pre">Summary</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/unitroot/_phillips_ouliaris.html#PhillipsOuliarisTestResults.summary"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.unitroot.cointegration.PhillipsOuliarisTestResults.summary" title="Link to this definition">¶</a></dt>
 <dd><p>Summary of test, containing statistic, p-value and critical values</p>
 </dd></dl>
 
@@ -1059,7 +1059,7 @@ <h1 id="arch-unitroot-cointegration-phillipsouliaristestresults-summary">arch.un
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/devel/unitroot/generated/generated/arch.unitroot.cointegration.PhillipsOuliarisTestResults.trend.html b/devel/unitroot/generated/generated/arch.unitroot.cointegration.PhillipsOuliarisTestResults.trend.html
index 30d435e49a..f85f382d7e 100644
--- a/devel/unitroot/generated/generated/arch.unitroot.cointegration.PhillipsOuliarisTestResults.trend.html
+++ b/devel/unitroot/generated/generated/arch.unitroot.cointegration.PhillipsOuliarisTestResults.trend.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.unitroot.cointegration.PhillipsOuliarisTestResults.trend - arch v6.2.1 (+21)</title>
+        <title>arch.unitroot.cointegration.PhillipsOuliarisTestResults.trend - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
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diff --git a/devel/unitroot/tests.html b/devel/unitroot/tests.html
index 2453069a13..d40bc0c154 100644
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diff --git a/devel/unitroot/unitroot.html b/devel/unitroot/unitroot.html
index 021cf8e7fa..099d6a3cbf 100644
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@@ -15,7 +15,7 @@
     
   
       
-        <title>Unit Root Testing - arch v6.2.1 (+21)</title>
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@@ -47,7 +47,7 @@
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@@ -87,7 +87,7 @@
 
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@@ -97,7 +97,7 @@
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-    <a href="../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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index 68b5ac2607..2a72971abd 100644
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+++ b/devel/unitroot/unitroot_cointegration_examples.html
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-        <script src="../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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diff --git a/devel/unitroot/unitroot_examples.html b/devel/unitroot/unitroot_examples.html
index 7c6a27b26b..ebd0ca56fe 100644
--- a/devel/unitroot/unitroot_examples.html
+++ b/devel/unitroot/unitroot_examples.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>Unit Root Testing - arch v6.2.1 (+21)</title>
+        <title>Unit Root Testing - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
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-        <link rel="stylesheet" type="text/css" href="../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
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@@ -93,7 +93,7 @@
 
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-    <a href="../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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@@ -103,7 +103,7 @@
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-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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@@ -190,10 +190,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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     <div class="md-nav__source">
@@ -1365,7 +1365,7 @@ <h2 id="Variance-Ratio-Testing">Variance Ratio Testing<a class="headerlink" href
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-        <script src="../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
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diff --git a/devel/univariate/background.html b/devel/univariate/background.html
index 9965cf4031..c31dcce0de 100644
--- a/devel/univariate/background.html
+++ b/devel/univariate/background.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>Theoretical Background - arch v6.2.1 (+21)</title>
+        <title>Theoretical Background - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
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-        <link rel="stylesheet" type="text/css" href="../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
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@@ -87,7 +87,7 @@
 
 <header class="md-header" data-md-component="header">
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-    <a href="../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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     </a>
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@@ -97,7 +97,7 @@
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         <div class="md-header__topic">
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-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
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@@ -184,10 +184,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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     <div class="md-nav__source">
@@ -722,7 +722,7 @@ <h1 id="theoretical-background">Theoretical Background<a class="headerlink" href
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-        <script src="../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
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   </body>
diff --git a/devel/univariate/distribution.html b/devel/univariate/distribution.html
index 96b3ce6a43..3998c7a210 100644
--- a/devel/univariate/distribution.html
+++ b/devel/univariate/distribution.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>Distributions - arch v6.2.1 (+21)</title>
+        <title>Distributions - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
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-        <link rel="stylesheet" type="text/css" href="../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -984,7 +984,7 @@ <h2 id="writing-new-distributions">Writing New Distributions<a class="headerlink
     <script id="__config" type="application/json">{"base": "..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/devel/univariate/forecasting.html b/devel/univariate/forecasting.html
index 45da13ab45..e0606f0f92 100644
--- a/devel/univariate/forecasting.html
+++ b/devel/univariate/forecasting.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>Forecasting - arch v6.2.1 (+21)</title>
+        <title>Forecasting - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
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-        <link rel="stylesheet" type="text/css" href="../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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     <div class="md-nav__source">
@@ -1150,7 +1150,7 @@ <h2 id="output-classes">Output Classes<a class="headerlink" href="#output-classe
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-        <script src="../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
         <script>window.MathJax = {"tex": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true}, "options": {"ignoreHtmlClass": "tex2jax_ignore|mathjax_ignore|document", "processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
         <script id="MathJax-script" src="../_static/mathjax/tex-mml-chtml.js?v=cadf963e"></script>
diff --git a/devel/univariate/generated/arch.univariate.APARCH.html b/devel/univariate/generated/arch.univariate.APARCH.html
index bea1a16695..e3947b7419 100644
--- a/devel/univariate/generated/arch.univariate.APARCH.html
+++ b/devel/univariate/generated/arch.univariate.APARCH.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.APARCH - arch v6.2.1 (+21)</title>
+        <title>arch.univariate.APARCH - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
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-        <link rel="stylesheet" type="text/css" href="../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../_static/bw-logo.svg" alt="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -1677,7 +1677,7 @@ <h1 id="arch-univariate-aparch">arch.univariate.APARCH<a class="headerlink" href
     <script id="__config" type="application/json">{"base": "../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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diff --git a/devel/univariate/generated/arch.univariate.ARCH.html b/devel/univariate/generated/arch.univariate.ARCH.html
index 37e0433ef2..18c40fcdaf 100644
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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@@ -1545,7 +1545,7 @@ <h1 id="arch-univariate-arch">arch.univariate.ARCH<a class="headerlink" href="#a
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diff --git a/devel/univariate/generated/arch.univariate.ARCHInMean.html b/devel/univariate/generated/arch.univariate.ARCHInMean.html
index 119bbd43e9..b75528f0c2 100644
--- a/devel/univariate/generated/arch.univariate.ARCHInMean.html
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+        <title>arch.univariate.ARCHInMean - arch v6.2.1 (+23)</title>
       
     
 
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+    <a href="../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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 <h1 id="arch-univariate-archinmean">arch.univariate.ARCHInMean<a class="headerlink" href="#arch-univariate-archinmean" title="Link to this heading">¶</a></h1>
 <dl class="py class objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.ARCHInMean">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.univariate.</span></span><span class="sig-name descname"><span class="pre">ARCHInMean</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCHInMean.__init__.y" title="arch.univariate.ARCHInMean.__init__.y (Python parameter) — nobs element vector containing the dependent variable"><span class="n"><span class="pre">y</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCHInMean.__init__.x" title="arch.univariate.ARCHInMean.__init__.x (Python parameter) — nobs by k element array containing exogenous regressors"><span class="n"><span class="pre">x</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCHInMean.__init__.lags" title="arch.univariate.ARCHInMean.__init__.lags (Python parameter) — Description of lag structure of the HAR.  Scalar included all lags between 1 and the value.  A 1-d array includes the AR lags lags[0], lags[1], ..."><span class="n"><span class="pre">lags</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCHInMean.__init__.constant" title="arch.univariate.ARCHInMean.__init__.constant (Python parameter) — Flag whether the model should include a constant"><span class="n"><span class="pre">constant</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCHInMean.__init__.hold_back" title="arch.univariate.ARCHInMean.__init__.hold_back (Python parameter) — Number of observations at the start of the sample to exclude when estimating model parameters.  Used when comparing models with different lag lengths to estimate on the common sample."><span class="n"><span class="pre">hold_back</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCHInMean.__init__.volatility" title="arch.univariate.ARCHInMean.__init__.volatility (Python parameter) — Volatility process to use in the model. volatility.updateable must return True."><span class="n"><span class="pre">volatility</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference internal" href="arch.univariate.volatility.VolatilityProcess.html#arch.univariate.volatility.VolatilityProcess" title="arch.univariate.volatility.VolatilityProcess (Python class) — Abstract base class for ARCH models.  Allows the conditional mean model to be specified separately from the conditional variance, even though parameters are estimated jointly."><span class="pre">VolatilityProcess</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCHInMean.__init__.distribution" title="arch.univariate.ARCHInMean.__init__.distribution (Python parameter) — Error distribution to use in the model"><span class="n"><span class="pre">distribution</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference internal" href="arch.univariate.distribution.Distribution.html#arch.univariate.distribution.Distribution" title="arch.univariate.distribution.Distribution (Python class) — Template for subclassing only"><span class="pre">Distribution</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCHInMean.__init__.rescale" title="arch.univariate.ARCHInMean.__init__.rescale (Python parameter) — Flag indicating whether to automatically rescale data if the scale of the data is likely to produce convergence issues when estimating model parameters. If False, the model is estimated on the data without transformation.  If True, than y is rescaled and the new scale is reported in the estimation results."><span class="n"><span class="pre">rescale</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCHInMean.__init__.form" title="arch.univariate.ARCHInMean.__init__.form (Python parameter) — The form of the conditional variance that appears in the mean equation. The string names use the log of the conditional variance (&quot;log&quot;), the square-root of the conditional variance (&quot;vol&quot;) or the conditional variance. When specified using a float, interpreted as \sigma_t^{form} so that 1 is equivalent to &quot;vol&quot; and 2 is equivalent to &quot;var&quot;. When using a number, must be different from 0."><span class="n"><span class="pre">form</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'log'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'vol'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'var'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;vol&#39;</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/univariate/mean.html#ARCHInMean"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.ARCHInMean" title="Link to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.univariate.</span></span><span class="sig-name descname"><span class="pre">ARCHInMean</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCHInMean.__init__.y" title="arch.univariate.ARCHInMean.__init__.y (Python parameter) — nobs element vector containing the dependent variable"><span class="n"><span class="pre">y</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCHInMean.__init__.x" title="arch.univariate.ARCHInMean.__init__.x (Python parameter) — nobs by k element array containing exogenous regressors"><span class="n"><span class="pre">x</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCHInMean.__init__.lags" title="arch.univariate.ARCHInMean.__init__.lags (Python parameter) — Description of lag structure of the HAR.  Scalar included all lags between 1 and the value.  A 1-d array includes the AR lags lags[0], lags[1], ..."><span class="n"><span class="pre">lags</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCHInMean.__init__.constant" title="arch.univariate.ARCHInMean.__init__.constant (Python parameter) — Flag whether the model should include a constant"><span class="n"><span class="pre">constant</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCHInMean.__init__.hold_back" title="arch.univariate.ARCHInMean.__init__.hold_back (Python parameter) — Number of observations at the start of the sample to exclude when estimating model parameters.  Used when comparing models with different lag lengths to estimate on the common sample."><span class="n"><span class="pre">hold_back</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCHInMean.__init__.volatility" title="arch.univariate.ARCHInMean.__init__.volatility (Python parameter) — Volatility process to use in the model. volatility.updateable must return True."><span class="n"><span class="pre">volatility</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference internal" href="arch.univariate.volatility.VolatilityProcess.html#arch.univariate.volatility.VolatilityProcess" title="arch.univariate.volatility.VolatilityProcess (Python class) — Abstract base class for ARCH models.  Allows the conditional mean model to be specified separately from the conditional variance, even though parameters are estimated jointly."><span class="pre">VolatilityProcess</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCHInMean.__init__.distribution" title="arch.univariate.ARCHInMean.__init__.distribution (Python parameter) — Error distribution to use in the model"><span class="n"><span class="pre">distribution</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference internal" href="arch.univariate.distribution.Distribution.html#arch.univariate.distribution.Distribution" title="arch.univariate.distribution.Distribution (Python class) — Template for subclassing only"><span class="pre">Distribution</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCHInMean.__init__.rescale" title="arch.univariate.ARCHInMean.__init__.rescale (Python parameter) — Flag indicating whether to automatically rescale data if the scale of the data is likely to produce convergence issues when estimating model parameters. If False, the model is estimated on the data without transformation.  If True, than y is rescaled and the new scale is reported in the estimation results."><span class="n"><span class="pre">rescale</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCHInMean.__init__.form" title="arch.univariate.ARCHInMean.__init__.form (Python parameter) — The form of the conditional variance that appears in the mean equation. The string names use the log of the conditional variance (&quot;log&quot;), the square-root of the conditional variance (&quot;vol&quot;) or the conditional variance. When specified using a float, interpreted as \sigma_t^{form} so that 1 is equivalent to &quot;vol&quot; and 2 is equivalent to &quot;var&quot;. When using a number, must be different from 0."><span class="n"><span class="pre">form</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'log'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'vol'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'var'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;vol&#39;</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/univariate/mean.html#ARCHInMean"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.ARCHInMean" title="Link to this definition">¶</a></dt>
 <dd><p>(G)ARCH-in-mean model and simulation</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.ARCHInMean-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.ARCHInMean-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.ARCHInMean.__init__.y"><span class="n sig-name">y</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ARCHInMean.__init__.y" title="Permalink to this definition">¶</a></dt><dd><p>nobs element vector containing the dependent variable</p>
+<dt id="arch.univariate.ARCHInMean.__init__.y"><span class="n sig-name">y</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ARCHInMean.__init__.y" title="Permalink to this definition">¶</a></dt><dd><p>nobs element vector containing the dependent variable</p>
 </dd>
-<dt id="arch.univariate.ARCHInMean.__init__.x"><span class="n sig-name">x</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ARCHInMean.__init__.x" title="Permalink to this definition">¶</a></dt><dd><p>nobs by k element array containing exogenous regressors</p>
+<dt id="arch.univariate.ARCHInMean.__init__.x"><span class="n sig-name">x</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ARCHInMean.__init__.x" title="Permalink to this definition">¶</a></dt><dd><p>nobs by k element array containing exogenous regressors</p>
 </dd>
 <dt id="arch.univariate.ARCHInMean.__init__.lags"><span class="n sig-name">lags</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)">list</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ARCHInMean.__init__.lags" title="Permalink to this definition">¶</a></dt><dd><p>Description of lag structure of the HAR.  Scalar included all lags
 between 1 and the value.  A 1-d array includes the AR lags lags[0],
@@ -1669,7 +1669,7 @@ <h1 id="arch-univariate-archinmean">arch.univariate.ARCHInMean<a class="headerli
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@@ -1361,15 +1361,15 @@
 <h1 id="arch-univariate-arx">arch.univariate.ARX<a class="headerlink" href="#arch-univariate-arx" title="Link to this heading">¶</a></h1>
 <dl class="py class objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.ARX">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.univariate.</span></span><span class="sig-name descname"><span class="pre">ARX</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARX.__init__.y" title="arch.univariate.ARX.__init__.y (Python parameter) — nobs element vector containing the dependent variable"><span class="n"><span class="pre">y</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARX.__init__.x" title="arch.univariate.ARX.__init__.x (Python parameter) — nobs by k element array containing exogenous regressors"><span class="n"><span class="pre">x</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARX.__init__.lags" title="arch.univariate.ARX.__init__.lags (Python parameter) — Description of lag structure of the HAR.  Scalar included all lags between 1 and the value.  A 1-d array includes the AR lags lags[0], lags[1], ..."><span class="n"><span class="pre">lags</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARX.__init__.constant" title="arch.univariate.ARX.__init__.constant (Python parameter) — Flag whether the model should include a constant"><span class="n"><span class="pre">constant</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARX.__init__.hold_back" title="arch.univariate.ARX.__init__.hold_back (Python parameter) — Number of observations at the start of the sample to exclude when estimating model parameters.  Used when comparing models with different lag lengths to estimate on the common sample."><span class="n"><span class="pre">hold_back</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARX.__init__.volatility" title="arch.univariate.ARX.__init__.volatility (Python parameter) — Volatility process to use in the model"><span class="n"><span class="pre">volatility</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference internal" href="arch.univariate.volatility.VolatilityProcess.html#arch.univariate.volatility.VolatilityProcess" title="arch.univariate.volatility.VolatilityProcess (Python class) — Abstract base class for ARCH models.  Allows the conditional mean model to be specified separately from the conditional variance, even though parameters are estimated jointly."><span class="pre">VolatilityProcess</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARX.__init__.distribution" title="arch.univariate.ARX.__init__.distribution (Python parameter) — Error distribution to use in the model"><span class="n"><span class="pre">distribution</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference internal" href="arch.univariate.distribution.Distribution.html#arch.univariate.distribution.Distribution" title="arch.univariate.distribution.Distribution (Python class) — Template for subclassing only"><span class="pre">Distribution</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARX.__init__.rescale" title="arch.univariate.ARX.__init__.rescale (Python parameter) — Flag indicating whether to automatically rescale data if the scale of the data is likely to produce convergence issues when estimating model parameters. If False, the model is estimated on the data without transformation.  If True, than y is rescaled and the new scale is reported in the estimation results."><span class="n"><span class="pre">rescale</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/univariate/mean.html#ARX"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.ARX" title="Link to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.univariate.</span></span><span class="sig-name descname"><span class="pre">ARX</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARX.__init__.y" title="arch.univariate.ARX.__init__.y (Python parameter) — nobs element vector containing the dependent variable"><span class="n"><span class="pre">y</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARX.__init__.x" title="arch.univariate.ARX.__init__.x (Python parameter) — nobs by k element array containing exogenous regressors"><span class="n"><span class="pre">x</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARX.__init__.lags" title="arch.univariate.ARX.__init__.lags (Python parameter) — Description of lag structure of the HAR.  Scalar included all lags between 1 and the value.  A 1-d array includes the AR lags lags[0], lags[1], ..."><span class="n"><span class="pre">lags</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARX.__init__.constant" title="arch.univariate.ARX.__init__.constant (Python parameter) — Flag whether the model should include a constant"><span class="n"><span class="pre">constant</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARX.__init__.hold_back" title="arch.univariate.ARX.__init__.hold_back (Python parameter) — Number of observations at the start of the sample to exclude when estimating model parameters.  Used when comparing models with different lag lengths to estimate on the common sample."><span class="n"><span class="pre">hold_back</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARX.__init__.volatility" title="arch.univariate.ARX.__init__.volatility (Python parameter) — Volatility process to use in the model"><span class="n"><span class="pre">volatility</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference internal" href="arch.univariate.volatility.VolatilityProcess.html#arch.univariate.volatility.VolatilityProcess" title="arch.univariate.volatility.VolatilityProcess (Python class) — Abstract base class for ARCH models.  Allows the conditional mean model to be specified separately from the conditional variance, even though parameters are estimated jointly."><span class="pre">VolatilityProcess</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARX.__init__.distribution" title="arch.univariate.ARX.__init__.distribution (Python parameter) — Error distribution to use in the model"><span class="n"><span class="pre">distribution</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference internal" href="arch.univariate.distribution.Distribution.html#arch.univariate.distribution.Distribution" title="arch.univariate.distribution.Distribution (Python class) — Template for subclassing only"><span class="pre">Distribution</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARX.__init__.rescale" title="arch.univariate.ARX.__init__.rescale (Python parameter) — Flag indicating whether to automatically rescale data if the scale of the data is likely to produce convergence issues when estimating model parameters. If False, the model is estimated on the data without transformation.  If True, than y is rescaled and the new scale is reported in the estimation results."><span class="n"><span class="pre">rescale</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/univariate/mean.html#ARX"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.ARX" title="Link to this definition">¶</a></dt>
 <dd><p>Autoregressive model with optional exogenous regressors estimation and
 simulation</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.ARX-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.ARX-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.ARX.__init__.y"><span class="n sig-name">y</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ARX.__init__.y" title="Permalink to this definition">¶</a></dt><dd><p>nobs element vector containing the dependent variable</p>
+<dt id="arch.univariate.ARX.__init__.y"><span class="n sig-name">y</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ARX.__init__.y" title="Permalink to this definition">¶</a></dt><dd><p>nobs element vector containing the dependent variable</p>
 </dd>
-<dt id="arch.univariate.ARX.__init__.x"><span class="n sig-name">x</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ARX.__init__.x" title="Permalink to this definition">¶</a></dt><dd><p>nobs by k element array containing exogenous regressors</p>
+<dt id="arch.univariate.ARX.__init__.x"><span class="n sig-name">x</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ARX.__init__.x" title="Permalink to this definition">¶</a></dt><dd><p>nobs by k element array containing exogenous regressors</p>
 </dd>
 <dt id="arch.univariate.ARX.__init__.lags"><span class="n sig-name">lags</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)">list</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ARX.__init__.lags" title="Permalink to this definition">¶</a></dt><dd><p>Description of lag structure of the HAR.  Scalar included all lags
 between 1 and the value.  A 1-d array includes the AR lags lags[0],
@@ -1586,7 +1586,7 @@ <h1 id="arch-univariate-arx">arch.univariate.ARX<a class="headerlink" href="#arc
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diff --git a/devel/univariate/generated/arch.univariate.ConstantMean.html b/devel/univariate/generated/arch.univariate.ConstantMean.html
index 7413d99ba8..96a8d485a2 100644
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@@ -15,7 +15,7 @@
     
   
       
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@@ -1319,12 +1319,12 @@
 <h1 id="arch-univariate-constantmean">arch.univariate.ConstantMean<a class="headerlink" href="#arch-univariate-constantmean" title="Link to this heading">¶</a></h1>
 <dl class="py class objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.ConstantMean">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.univariate.</span></span><span class="sig-name descname"><span class="pre">ConstantMean</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantMean.__init__.y" title="arch.univariate.ConstantMean.__init__.y (Python parameter) — nobs element vector containing the dependent variable"><span class="n"><span class="pre">y</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantMean.__init__.hold_back" title="arch.univariate.ConstantMean.__init__.hold_back (Python parameter) — Number of observations at the start of the sample to exclude when estimating model parameters.  Used when comparing models with different lag lengths to estimate on the common sample."><span class="n"><span class="pre">hold_back</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantMean.__init__.volatility" title="arch.univariate.ConstantMean.__init__.volatility (Python parameter) — Volatility process to use in the model"><span class="n"><span class="pre">volatility</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference internal" href="arch.univariate.volatility.VolatilityProcess.html#arch.univariate.volatility.VolatilityProcess" title="arch.univariate.volatility.VolatilityProcess (Python class) — Abstract base class for ARCH models.  Allows the conditional mean model to be specified separately from the conditional variance, even though parameters are estimated jointly."><span class="pre">VolatilityProcess</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantMean.__init__.distribution" title="arch.univariate.ConstantMean.__init__.distribution (Python parameter) — Error distribution to use in the model"><span class="n"><span class="pre">distribution</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference internal" href="arch.univariate.distribution.Distribution.html#arch.univariate.distribution.Distribution" title="arch.univariate.distribution.Distribution (Python class) — Template for subclassing only"><span class="pre">Distribution</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantMean.__init__.rescale" title="arch.univariate.ConstantMean.__init__.rescale (Python parameter) — Flag indicating whether to automatically rescale data if the scale of the data is likely to produce convergence issues when estimating model parameters. If False, the model is estimated on the data without transformation.  If True, than y is rescaled and the new scale is reported in the estimation results."><span class="n"><span class="pre">rescale</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/univariate/mean.html#ConstantMean"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.ConstantMean" title="Link to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.univariate.</span></span><span class="sig-name descname"><span class="pre">ConstantMean</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantMean.__init__.y" title="arch.univariate.ConstantMean.__init__.y (Python parameter) — nobs element vector containing the dependent variable"><span class="n"><span class="pre">y</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantMean.__init__.hold_back" title="arch.univariate.ConstantMean.__init__.hold_back (Python parameter) — Number of observations at the start of the sample to exclude when estimating model parameters.  Used when comparing models with different lag lengths to estimate on the common sample."><span class="n"><span class="pre">hold_back</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantMean.__init__.volatility" title="arch.univariate.ConstantMean.__init__.volatility (Python parameter) — Volatility process to use in the model"><span class="n"><span class="pre">volatility</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference internal" href="arch.univariate.volatility.VolatilityProcess.html#arch.univariate.volatility.VolatilityProcess" title="arch.univariate.volatility.VolatilityProcess (Python class) — Abstract base class for ARCH models.  Allows the conditional mean model to be specified separately from the conditional variance, even though parameters are estimated jointly."><span class="pre">VolatilityProcess</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantMean.__init__.distribution" title="arch.univariate.ConstantMean.__init__.distribution (Python parameter) — Error distribution to use in the model"><span class="n"><span class="pre">distribution</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference internal" href="arch.univariate.distribution.Distribution.html#arch.univariate.distribution.Distribution" title="arch.univariate.distribution.Distribution (Python class) — Template for subclassing only"><span class="pre">Distribution</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantMean.__init__.rescale" title="arch.univariate.ConstantMean.__init__.rescale (Python parameter) — Flag indicating whether to automatically rescale data if the scale of the data is likely to produce convergence issues when estimating model parameters. If False, the model is estimated on the data without transformation.  If True, than y is rescaled and the new scale is reported in the estimation results."><span class="n"><span class="pre">rescale</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/univariate/mean.html#ConstantMean"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.ConstantMean" title="Link to this definition">¶</a></dt>
 <dd><p>Constant mean model estimation and simulation.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.ConstantMean-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.ConstantMean-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.ConstantMean.__init__.y"><span class="n sig-name">y</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ConstantMean.__init__.y" title="Permalink to this definition">¶</a></dt><dd><p>nobs element vector containing the dependent variable</p>
+<dt id="arch.univariate.ConstantMean.__init__.y"><span class="n sig-name">y</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ConstantMean.__init__.y" title="Permalink to this definition">¶</a></dt><dd><p>nobs element vector containing the dependent variable</p>
 </dd>
 <dt id="arch.univariate.ConstantMean.__init__.hold_back"><span class="n sig-name">hold_back</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ConstantMean.__init__.hold_back" title="Permalink to this definition">¶</a></dt><dd><p>Number of observations at the start of the sample to exclude when
 estimating model parameters.  Used when comparing models with different
@@ -1528,7 +1528,7 @@ <h1 id="arch-univariate-constantmean">arch.univariate.ConstantMean<a class="head
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-            arch v6.2.1 (+21)
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diff --git a/devel/univariate/generated/arch.univariate.EGARCH.html b/devel/univariate/generated/arch.univariate.EGARCH.html
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@@ -15,7 +15,7 @@
     
   
       
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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 <h1 id="arch-univariate-harx">arch.univariate.HARX<a class="headerlink" href="#arch-univariate-harx" title="Link to this heading">¶</a></h1>
 <dl class="py class objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.HARX">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.univariate.</span></span><span class="sig-name descname"><span class="pre">HARX</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARX.__init__.y" title="arch.univariate.HARX.__init__.y (Python parameter) — nobs element vector containing the dependent variable"><span class="n"><span class="pre">y</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARX.__init__.x" title="arch.univariate.HARX.__init__.x (Python parameter) — nobs by k element array containing exogenous regressors"><span class="n"><span class="pre">x</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARX.__init__.lags" title="arch.univariate.HARX.__init__.lags (Python parameter) — Description of lag structure of the HAR."><span class="n"><span class="pre">lags</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARX.__init__.constant" title="arch.univariate.HARX.__init__.constant (Python parameter) — Flag whether the model should include a constant"><span class="n"><span class="pre">constant</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARX.__init__.use_rotated" title="arch.univariate.HARX.__init__.use_rotated (Python parameter) — Flag indicating to use the alternative rotated form of the HAR where HAR lags do not overlap"><span class="n"><span class="pre">use_rotated</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARX.__init__.hold_back" title="arch.univariate.HARX.__init__.hold_back (Python parameter) — Number of observations at the start of the sample to exclude when estimating model parameters.  Used when comparing models with different lag lengths to estimate on the common sample."><span class="n"><span class="pre">hold_back</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARX.__init__.volatility" title="arch.univariate.HARX.__init__.volatility (Python parameter) — Volatility process to use in the model"><span class="n"><span class="pre">volatility</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference internal" href="arch.univariate.volatility.VolatilityProcess.html#arch.univariate.volatility.VolatilityProcess" title="arch.univariate.volatility.VolatilityProcess (Python class) — Abstract base class for ARCH models.  Allows the conditional mean model to be specified separately from the conditional variance, even though parameters are estimated jointly."><span class="pre">VolatilityProcess</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARX.__init__.distribution" title="arch.univariate.HARX.__init__.distribution (Python parameter) — Error distribution to use in the model"><span class="n"><span class="pre">distribution</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference internal" href="arch.univariate.distribution.Distribution.html#arch.univariate.distribution.Distribution" title="arch.univariate.distribution.Distribution (Python class) — Template for subclassing only"><span class="pre">Distribution</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARX.__init__.rescale" title="arch.univariate.HARX.__init__.rescale (Python parameter) — Flag indicating whether to automatically rescale data if the scale of the data is likely to produce convergence issues when estimating model parameters. If False, the model is estimated on the data without transformation.  If True, than y is rescaled and the new scale is reported in the estimation results."><span class="n"><span class="pre">rescale</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/univariate/mean.html#HARX"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.HARX" title="Link to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.univariate.</span></span><span class="sig-name descname"><span class="pre">HARX</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARX.__init__.y" title="arch.univariate.HARX.__init__.y (Python parameter) — nobs element vector containing the dependent variable"><span class="n"><span class="pre">y</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARX.__init__.x" title="arch.univariate.HARX.__init__.x (Python parameter) — nobs by k element array containing exogenous regressors"><span class="n"><span class="pre">x</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARX.__init__.lags" title="arch.univariate.HARX.__init__.lags (Python parameter) — Description of lag structure of the HAR."><span class="n"><span class="pre">lags</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARX.__init__.constant" title="arch.univariate.HARX.__init__.constant (Python parameter) — Flag whether the model should include a constant"><span class="n"><span class="pre">constant</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARX.__init__.use_rotated" title="arch.univariate.HARX.__init__.use_rotated (Python parameter) — Flag indicating to use the alternative rotated form of the HAR where HAR lags do not overlap"><span class="n"><span class="pre">use_rotated</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARX.__init__.hold_back" title="arch.univariate.HARX.__init__.hold_back (Python parameter) — Number of observations at the start of the sample to exclude when estimating model parameters.  Used when comparing models with different lag lengths to estimate on the common sample."><span class="n"><span class="pre">hold_back</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARX.__init__.volatility" title="arch.univariate.HARX.__init__.volatility (Python parameter) — Volatility process to use in the model"><span class="n"><span class="pre">volatility</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference internal" href="arch.univariate.volatility.VolatilityProcess.html#arch.univariate.volatility.VolatilityProcess" title="arch.univariate.volatility.VolatilityProcess (Python class) — Abstract base class for ARCH models.  Allows the conditional mean model to be specified separately from the conditional variance, even though parameters are estimated jointly."><span class="pre">VolatilityProcess</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARX.__init__.distribution" title="arch.univariate.HARX.__init__.distribution (Python parameter) — Error distribution to use in the model"><span class="n"><span class="pre">distribution</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference internal" href="arch.univariate.distribution.Distribution.html#arch.univariate.distribution.Distribution" title="arch.univariate.distribution.Distribution (Python class) — Template for subclassing only"><span class="pre">Distribution</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARX.__init__.rescale" title="arch.univariate.HARX.__init__.rescale (Python parameter) — Flag indicating whether to automatically rescale data if the scale of the data is likely to produce convergence issues when estimating model parameters. If False, the model is estimated on the data without transformation.  If True, than y is rescaled and the new scale is reported in the estimation results."><span class="n"><span class="pre">rescale</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/univariate/mean.html#HARX"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.HARX" title="Link to this definition">¶</a></dt>
 <dd><p>Heterogeneous Autoregression (HAR), with optional exogenous regressors,
 model estimation and simulation</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.HARX-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.HARX-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.HARX.__init__.y"><span class="n sig-name">y</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.HARX.__init__.y" title="Permalink to this definition">¶</a></dt><dd><p>nobs element vector containing the dependent variable</p>
+<dt id="arch.univariate.HARX.__init__.y"><span class="n sig-name">y</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.HARX.__init__.y" title="Permalink to this definition">¶</a></dt><dd><p>nobs element vector containing the dependent variable</p>
 </dd>
-<dt id="arch.univariate.HARX.__init__.x"><span class="n sig-name">x</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.HARX.__init__.x" title="Permalink to this definition">¶</a></dt><dd><p>nobs by k element array containing exogenous regressors</p>
+<dt id="arch.univariate.HARX.__init__.x"><span class="n sig-name">x</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.HARX.__init__.x" title="Permalink to this definition">¶</a></dt><dd><p>nobs by k element array containing exogenous regressors</p>
 </dd>
 <dt id="arch.univariate.HARX.__init__.lags"><span class="n sig-name">lags</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a><span class="p">]</span><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.HARX.__init__.lags" title="Permalink to this definition">¶</a></dt><dd><p><p>Description of lag structure of the HAR.</p>
 <ul class="simple">
@@ -1636,7 +1636,7 @@ <h1 id="arch-univariate-harx">arch.univariate.HARX<a class="headerlink" href="#a
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diff --git a/devel/univariate/generated/arch.univariate.LS.html b/devel/univariate/generated/arch.univariate.LS.html
index 5e21fe3596..ada9f0cc06 100644
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 <h1 id="arch-univariate-ls">arch.univariate.LS<a class="headerlink" href="#arch-univariate-ls" title="Link to this heading">¶</a></h1>
 <dl class="py class objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.LS">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.univariate.</span></span><span class="sig-name descname"><span class="pre">LS</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.LS.__init__.y" title="arch.univariate.LS.__init__.y (Python parameter) — nobs by k element array containing exogenous regressors"><span class="n"><span class="pre">y</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">x</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.LS.__init__.constant" title="arch.univariate.LS.__init__.constant (Python parameter) — Flag whether the model should include a constant"><span class="n"><span class="pre">constant</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.LS.__init__.hold_back" title="arch.univariate.LS.__init__.hold_back (Python parameter) — Number of observations at the start of the sample to exclude when estimating model parameters.  Used when comparing models with different lag lengths to estimate on the common sample."><span class="n"><span class="pre">hold_back</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.LS.__init__.volatility" title="arch.univariate.LS.__init__.volatility (Python parameter) — Volatility process to use in the model"><span class="n"><span class="pre">volatility</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference internal" href="arch.univariate.volatility.VolatilityProcess.html#arch.univariate.volatility.VolatilityProcess" title="arch.univariate.volatility.VolatilityProcess (Python class) — Abstract base class for ARCH models.  Allows the conditional mean model to be specified separately from the conditional variance, even though parameters are estimated jointly."><span class="pre">VolatilityProcess</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.LS.__init__.distribution" title="arch.univariate.LS.__init__.distribution (Python parameter) — Error distribution to use in the model"><span class="n"><span class="pre">distribution</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference internal" href="arch.univariate.distribution.Distribution.html#arch.univariate.distribution.Distribution" title="arch.univariate.distribution.Distribution (Python class) — Template for subclassing only"><span class="pre">Distribution</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.LS.__init__.rescale" title="arch.univariate.LS.__init__.rescale (Python parameter) — Flag indicating whether to automatically rescale data if the scale of the data is likely to produce convergence issues when estimating model parameters. If False, the model is estimated on the data without transformation.  If True, than y is rescaled and the new scale is reported in the estimation results."><span class="n"><span class="pre">rescale</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/univariate/mean.html#LS"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.LS" title="Link to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.univariate.</span></span><span class="sig-name descname"><span class="pre">LS</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.LS.__init__.y" title="arch.univariate.LS.__init__.y (Python parameter) — nobs by k element array containing exogenous regressors"><span class="n"><span class="pre">y</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">x</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.LS.__init__.constant" title="arch.univariate.LS.__init__.constant (Python parameter) — Flag whether the model should include a constant"><span class="n"><span class="pre">constant</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.LS.__init__.hold_back" title="arch.univariate.LS.__init__.hold_back (Python parameter) — Number of observations at the start of the sample to exclude when estimating model parameters.  Used when comparing models with different lag lengths to estimate on the common sample."><span class="n"><span class="pre">hold_back</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.LS.__init__.volatility" title="arch.univariate.LS.__init__.volatility (Python parameter) — Volatility process to use in the model"><span class="n"><span class="pre">volatility</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference internal" href="arch.univariate.volatility.VolatilityProcess.html#arch.univariate.volatility.VolatilityProcess" title="arch.univariate.volatility.VolatilityProcess (Python class) — Abstract base class for ARCH models.  Allows the conditional mean model to be specified separately from the conditional variance, even though parameters are estimated jointly."><span class="pre">VolatilityProcess</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.LS.__init__.distribution" title="arch.univariate.LS.__init__.distribution (Python parameter) — Error distribution to use in the model"><span class="n"><span class="pre">distribution</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference internal" href="arch.univariate.distribution.Distribution.html#arch.univariate.distribution.Distribution" title="arch.univariate.distribution.Distribution (Python class) — Template for subclassing only"><span class="pre">Distribution</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.LS.__init__.rescale" title="arch.univariate.LS.__init__.rescale (Python parameter) — Flag indicating whether to automatically rescale data if the scale of the data is likely to produce convergence issues when estimating model parameters. If False, the model is estimated on the data without transformation.  If True, than y is rescaled and the new scale is reported in the estimation results."><span class="n"><span class="pre">rescale</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/univariate/mean.html#LS"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.LS" title="Link to this definition">¶</a></dt>
 <dd><p>Least squares model estimation and simulation</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.LS-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.LS-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.LS.__init__.y"><span class="n sig-name">y</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.LS.__init__.y" title="Permalink to this definition">¶</a></dt><dd><p>nobs element vector containing the dependent variable</p>
+<dt id="arch.univariate.LS.__init__.y"><span class="n sig-name">y</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.LS.__init__.y" title="Permalink to this definition">¶</a></dt><dd><p>nobs element vector containing the dependent variable</p>
 </dd>
-<dt id="arch.univariate.LS.__init__.y"><span class="n sig-name">y</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.LS.__init__.y" title="Permalink to this definition">¶</a></dt><dd><p>nobs by k element array containing exogenous regressors</p>
+<dt id="arch.univariate.LS.__init__.y"><span class="n sig-name">y</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.LS.__init__.y" title="Permalink to this definition">¶</a></dt><dd><p>nobs by k element array containing exogenous regressors</p>
 </dd>
 <dt id="arch.univariate.LS.__init__.constant"><span class="n sig-name">constant</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)">bool</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code><a class="headerlink" href="#arch.univariate.LS.__init__.constant" title="Permalink to this definition">¶</a></dt><dd><p>Flag whether the model should include a constant</p>
 </dd>
@@ -1561,7 +1561,7 @@ <h1 id="arch-univariate-ls">arch.univariate.LS<a class="headerlink" href="#arch-
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-        <script src="../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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+    arch v6.2.1 (+23)
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diff --git a/devel/univariate/generated/arch.univariate.ZeroMean.html b/devel/univariate/generated/arch.univariate.ZeroMean.html
index df1ed26aed..17c2b9826b 100644
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@@ -1319,12 +1319,12 @@
 <h1 id="arch-univariate-zeromean">arch.univariate.ZeroMean<a class="headerlink" href="#arch-univariate-zeromean" title="Link to this heading">¶</a></h1>
 <dl class="py class objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.ZeroMean">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.univariate.</span></span><span class="sig-name descname"><span class="pre">ZeroMean</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ZeroMean.__init__.y" title="arch.univariate.ZeroMean.__init__.y (Python parameter) — nobs element vector containing the dependent variable"><span class="n"><span class="pre">y</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ZeroMean.__init__.hold_back" title="arch.univariate.ZeroMean.__init__.hold_back (Python parameter) — Number of observations at the start of the sample to exclude when estimating model parameters.  Used when comparing models with different lag lengths to estimate on the common sample."><span class="n"><span class="pre">hold_back</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ZeroMean.__init__.volatility" title="arch.univariate.ZeroMean.__init__.volatility (Python parameter) — Volatility process to use in the model"><span class="n"><span class="pre">volatility</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference internal" href="arch.univariate.volatility.VolatilityProcess.html#arch.univariate.volatility.VolatilityProcess" title="arch.univariate.volatility.VolatilityProcess (Python class) — Abstract base class for ARCH models.  Allows the conditional mean model to be specified separately from the conditional variance, even though parameters are estimated jointly."><span class="pre">VolatilityProcess</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ZeroMean.__init__.distribution" title="arch.univariate.ZeroMean.__init__.distribution (Python parameter) — Error distribution to use in the model"><span class="n"><span class="pre">distribution</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference internal" href="arch.univariate.distribution.Distribution.html#arch.univariate.distribution.Distribution" title="arch.univariate.distribution.Distribution (Python class) — Template for subclassing only"><span class="pre">Distribution</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ZeroMean.__init__.rescale" title="arch.univariate.ZeroMean.__init__.rescale (Python parameter) — Flag indicating whether to automatically rescale data if the scale of the data is likely to produce convergence issues when estimating model parameters. If False, the model is estimated on the data without transformation.  If True, than y is rescaled and the new scale is reported in the estimation results."><span class="n"><span class="pre">rescale</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/univariate/mean.html#ZeroMean"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.ZeroMean" title="Link to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.univariate.</span></span><span class="sig-name descname"><span class="pre">ZeroMean</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ZeroMean.__init__.y" title="arch.univariate.ZeroMean.__init__.y (Python parameter) — nobs element vector containing the dependent variable"><span class="n"><span class="pre">y</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ZeroMean.__init__.hold_back" title="arch.univariate.ZeroMean.__init__.hold_back (Python parameter) — Number of observations at the start of the sample to exclude when estimating model parameters.  Used when comparing models with different lag lengths to estimate on the common sample."><span class="n"><span class="pre">hold_back</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ZeroMean.__init__.volatility" title="arch.univariate.ZeroMean.__init__.volatility (Python parameter) — Volatility process to use in the model"><span class="n"><span class="pre">volatility</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference internal" href="arch.univariate.volatility.VolatilityProcess.html#arch.univariate.volatility.VolatilityProcess" title="arch.univariate.volatility.VolatilityProcess (Python class) — Abstract base class for ARCH models.  Allows the conditional mean model to be specified separately from the conditional variance, even though parameters are estimated jointly."><span class="pre">VolatilityProcess</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ZeroMean.__init__.distribution" title="arch.univariate.ZeroMean.__init__.distribution (Python parameter) — Error distribution to use in the model"><span class="n"><span class="pre">distribution</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference internal" href="arch.univariate.distribution.Distribution.html#arch.univariate.distribution.Distribution" title="arch.univariate.distribution.Distribution (Python class) — Template for subclassing only"><span class="pre">Distribution</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ZeroMean.__init__.rescale" title="arch.univariate.ZeroMean.__init__.rescale (Python parameter) — Flag indicating whether to automatically rescale data if the scale of the data is likely to produce convergence issues when estimating model parameters. If False, the model is estimated on the data without transformation.  If True, than y is rescaled and the new scale is reported in the estimation results."><span class="n"><span class="pre">rescale</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/univariate/mean.html#ZeroMean"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.ZeroMean" title="Link to this definition">¶</a></dt>
 <dd><p>Model with zero conditional mean estimation and simulation</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.ZeroMean-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.ZeroMean-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.ZeroMean.__init__.y"><span class="n sig-name">y</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ZeroMean.__init__.y" title="Permalink to this definition">¶</a></dt><dd><p>nobs element vector containing the dependent variable</p>
+<dt id="arch.univariate.ZeroMean.__init__.y"><span class="n sig-name">y</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ZeroMean.__init__.y" title="Permalink to this definition">¶</a></dt><dd><p>nobs element vector containing the dependent variable</p>
 </dd>
 <dt id="arch.univariate.ZeroMean.__init__.hold_back"><span class="n sig-name">hold_back</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ZeroMean.__init__.hold_back" title="Permalink to this definition">¶</a></dt><dd><p>Number of observations at the start of the sample to exclude when
 estimating model parameters.  Used when comparing models with different
@@ -1528,7 +1528,7 @@ <h1 id="arch-univariate-zeromean">arch.univariate.ZeroMean<a class="headerlink"
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diff --git a/devel/univariate/generated/arch.univariate.base.ARCHModel.html b/devel/univariate/generated/arch.univariate.base.ARCHModel.html
index 1ee390b78a..87307e7a62 100644
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+++ b/devel/univariate/generated/arch.univariate.base.ARCHModel.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.base.ARCHModel - arch v6.2.1 (+21)</title>
+        <title>arch.univariate.base.ARCHModel - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
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@@ -1235,7 +1235,7 @@
 <h1 id="arch-univariate-base-archmodel">arch.univariate.base.ARCHModel<a class="headerlink" href="#arch-univariate-base-archmodel" title="Link to this heading">¶</a></h1>
 <dl class="py class objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.base.ARCHModel">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.univariate.base.</span></span><span class="sig-name descname"><span class="pre">ARCHModel</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">volatility</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference internal" href="arch.univariate.volatility.VolatilityProcess.html#arch.univariate.volatility.VolatilityProcess" title="arch.univariate.volatility.VolatilityProcess (Python class) — Abstract base class for ARCH models.  Allows the conditional mean model to be specified separately from the conditional variance, even though parameters are estimated jointly."><span class="pre">VolatilityProcess</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">distribution</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference internal" href="arch.univariate.distribution.Distribution.html#arch.univariate.distribution.Distribution" title="arch.univariate.distribution.Distribution (Python class) — Template for subclassing only"><span class="pre">Distribution</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">hold_back</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">rescale</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/univariate/base.html#ARCHModel"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.base.ARCHModel" title="Link to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.univariate.base.</span></span><span class="sig-name descname"><span class="pre">ARCHModel</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">y</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">volatility</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference internal" href="arch.univariate.volatility.VolatilityProcess.html#arch.univariate.volatility.VolatilityProcess" title="arch.univariate.volatility.VolatilityProcess (Python class) — Abstract base class for ARCH models.  Allows the conditional mean model to be specified separately from the conditional variance, even though parameters are estimated jointly."><span class="pre">VolatilityProcess</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">distribution</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference internal" href="arch.univariate.distribution.Distribution.html#arch.univariate.distribution.Distribution" title="arch.univariate.distribution.Distribution (Python class) — Template for subclassing only"><span class="pre">Distribution</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">hold_back</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">rescale</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/univariate/base.html#ARCHModel"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.base.ARCHModel" title="Link to this definition">¶</a></dt>
 <dd><p>Abstract base class for mean models in ARCH processes.  Specifies the
 conditional mean process.</p>
 <p>All public methods that raise NotImplementedError should be overridden by
@@ -1412,7 +1412,7 @@ <h1 id="arch-univariate-base-archmodel">arch.univariate.base.ARCHModel<a class="
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diff --git a/devel/univariate/generated/arch.univariate.base.ARCHModelFixedResult.html b/devel/univariate/generated/arch.univariate.base.ARCHModelFixedResult.html
index 9cf72db0b2..5fcd20482b 100644
--- a/devel/univariate/generated/arch.univariate.base.ARCHModelFixedResult.html
+++ b/devel/univariate/generated/arch.univariate.base.ARCHModelFixedResult.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.base.ARCHModelFixedResult - arch v6.2.1 (+21)</title>
+        <title>arch.univariate.base.ARCHModelFixedResult - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
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-    <a href="../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../_static/bw-logo.svg" alt="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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     <div class="md-nav__source">
@@ -1264,7 +1264,7 @@
 <h1 id="arch-univariate-base-archmodelfixedresult">arch.univariate.base.ARCHModelFixedResult<a class="headerlink" href="#arch-univariate-base-archmodelfixedresult" title="Link to this heading">¶</a></h1>
 <dl class="py class objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.base.ARCHModelFixedResult">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.univariate.base.</span></span><span class="sig-name descname"><span class="pre">ARCHModelFixedResult</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelFixedResult.__init__.params" title="arch.univariate.base.ARCHModelFixedResult.__init__.params (Python parameter) — Estimated parameters"><span class="n"><span class="pre">params</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelFixedResult.__init__.resid" title="arch.univariate.base.ARCHModelFixedResult.__init__.resid (Python parameter) — Residuals from model.  Residuals have same shape as original data and contain nan-values in locations not used in estimation"><span class="n"><span class="pre">resid</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelFixedResult.__init__.volatility" title="arch.univariate.base.ARCHModelFixedResult.__init__.volatility (Python parameter) — Conditional volatility from model"><span class="n"><span class="pre">volatility</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelFixedResult.__init__.dep_var" title="arch.univariate.base.ARCHModelFixedResult.__init__.dep_var (Python parameter) — Dependent variable"><span class="n"><span class="pre">dep_var</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">pandas.Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelFixedResult.__init__.names" title="arch.univariate.base.ARCHModelFixedResult.__init__.names (Python parameter) — Model parameter names"><span class="n"><span class="pre">names</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="p"><span class="pre">]</span></span></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelFixedResult.__init__.loglikelihood" title="arch.univariate.base.ARCHModelFixedResult.__init__.loglikelihood (Python parameter) — Loglikelihood at specified parameters"><span class="n"><span class="pre">loglikelihood</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelFixedResult.__init__.is_pandas" title="arch.univariate.base.ARCHModelFixedResult.__init__.is_pandas (Python parameter) — Whether the original input was pandas"><span class="n"><span class="pre">is_pandas</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelFixedResult.__init__.model" title="arch.univariate.base.ARCHModelFixedResult.__init__.model (Python parameter) — The model object used to estimate the parameters"><span class="n"><span class="pre">model</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference internal" href="arch.univariate.base.ARCHModel.html#arch.univariate.base.ARCHModel" title="arch.univariate.base.ARCHModel (Python class) — Abstract base class for mean models in ARCH processes.  Specifies the conditional mean process."><span class="pre">ARCHModel</span></a></span></span></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/univariate/base.html#ARCHModelFixedResult"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.base.ARCHModelFixedResult" title="Link to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.univariate.base.</span></span><span class="sig-name descname"><span class="pre">ARCHModelFixedResult</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelFixedResult.__init__.params" title="arch.univariate.base.ARCHModelFixedResult.__init__.params (Python parameter) — Estimated parameters"><span class="n"><span class="pre">params</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelFixedResult.__init__.resid" title="arch.univariate.base.ARCHModelFixedResult.__init__.resid (Python parameter) — Residuals from model.  Residuals have same shape as original data and contain nan-values in locations not used in estimation"><span class="n"><span class="pre">resid</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelFixedResult.__init__.volatility" title="arch.univariate.base.ARCHModelFixedResult.__init__.volatility (Python parameter) — Conditional volatility from model"><span class="n"><span class="pre">volatility</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelFixedResult.__init__.dep_var" title="arch.univariate.base.ARCHModelFixedResult.__init__.dep_var (Python parameter) — Dependent variable"><span class="n"><span class="pre">dep_var</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">pandas.Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelFixedResult.__init__.names" title="arch.univariate.base.ARCHModelFixedResult.__init__.names (Python parameter) — Model parameter names"><span class="n"><span class="pre">names</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="p"><span class="pre">]</span></span></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelFixedResult.__init__.loglikelihood" title="arch.univariate.base.ARCHModelFixedResult.__init__.loglikelihood (Python parameter) — Loglikelihood at specified parameters"><span class="n"><span class="pre">loglikelihood</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelFixedResult.__init__.is_pandas" title="arch.univariate.base.ARCHModelFixedResult.__init__.is_pandas (Python parameter) — Whether the original input was pandas"><span class="n"><span class="pre">is_pandas</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelFixedResult.__init__.model" title="arch.univariate.base.ARCHModelFixedResult.__init__.model (Python parameter) — The model object used to estimate the parameters"><span class="n"><span class="pre">model</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference internal" href="arch.univariate.base.ARCHModel.html#arch.univariate.base.ARCHModel" title="arch.univariate.base.ARCHModel (Python class) — Abstract base class for mean models in ARCH processes.  Specifies the conditional mean process."><span class="pre">ARCHModel</span></a></span></span></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/univariate/base.html#ARCHModelFixedResult"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.base.ARCHModelFixedResult" title="Link to this definition">¶</a></dt>
 <dd><p>Results for fixed parameters for an ARCHModel model</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.base.ARCHModelFixedResult-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.base.ARCHModelFixedResult-parameters" title="Permalink to this headline">¶</a></dt>
@@ -1276,7 +1276,7 @@ <h1 id="arch-univariate-base-archmodelfixedresult">arch.univariate.base.ARCHMode
 </dd>
 <dt id="arch.univariate.base.ARCHModelFixedResult.__init__.volatility"><span class="n sig-name">volatility</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.base.ARCHModelFixedResult.__init__.volatility" title="Permalink to this definition">¶</a></dt><dd><p>Conditional volatility from model</p>
 </dd>
-<dt id="arch.univariate.base.ARCHModelFixedResult.__init__.dep_var"><span class="n sig-name">dep_var</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">pandas.Series</a></span></span><a class="headerlink" href="#arch.univariate.base.ARCHModelFixedResult.__init__.dep_var" title="Permalink to this definition">¶</a></dt><dd><p>Dependent variable</p>
+<dt id="arch.univariate.base.ARCHModelFixedResult.__init__.dep_var"><span class="n sig-name">dep_var</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">pandas.Series</a></span></span><a class="headerlink" href="#arch.univariate.base.ARCHModelFixedResult.__init__.dep_var" title="Permalink to this definition">¶</a></dt><dd><p>Dependent variable</p>
 </dd>
 <dt id="arch.univariate.base.ARCHModelFixedResult.__init__.names"><span class="n sig-name">names</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)">list</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)">str</a><span class="p">]</span></span></span><a class="headerlink" href="#arch.univariate.base.ARCHModelFixedResult.__init__.names" title="Permalink to this definition">¶</a></dt><dd><p>Model parameter names</p>
 </dd>
@@ -1460,7 +1460,7 @@ <h1 id="arch-univariate-base-archmodelfixedresult">arch.univariate.base.ARCHMode
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diff --git a/devel/univariate/generated/arch.univariate.base.ARCHModelForecast.html b/devel/univariate/generated/arch.univariate.base.ARCHModelForecast.html
index 75db21abfb..f717f786f0 100644
--- a/devel/univariate/generated/arch.univariate.base.ARCHModelForecast.html
+++ b/devel/univariate/generated/arch.univariate.base.ARCHModelForecast.html
@@ -15,7 +15,7 @@
     
   
       
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+        <title>arch.univariate.base.ARCHModelForecast - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
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-        <link rel="stylesheet" type="text/css" href="../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
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@@ -92,7 +92,7 @@
 
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-    <a href="../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
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-    <a href="../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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     <div class="md-nav__source">
@@ -1240,7 +1240,7 @@ <h1 id="arch-univariate-base-archmodelforecast">arch.univariate.base.ARCHModelFo
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diff --git a/devel/univariate/generated/arch.univariate.base.ARCHModelForecastSimulation.html b/devel/univariate/generated/arch.univariate.base.ARCHModelForecastSimulation.html
index c58fc23283..5efe5e5f42 100644
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+++ b/devel/univariate/generated/arch.univariate.base.ARCHModelForecastSimulation.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.base.ARCHModelForecastSimulation - arch v6.2.1 (+21)</title>
+        <title>arch.univariate.base.ARCHModelForecastSimulation - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
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-        <link rel="stylesheet" type="text/css" href="../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
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@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
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   <label class="md-nav__title" for="__drawer">
-    <a href="../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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@@ -1201,7 +1201,7 @@ <h1 id="arch-univariate-base-archmodelforecastsimulation">arch.univariate.base.A
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-        <title>arch.univariate.base.ARCHModelResult - arch v6.2.1 (+21)</title>
+        <title>arch.univariate.base.ARCHModelResult - arch v6.2.1 (+23)</title>
       
     
 
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 <h1 id="arch-univariate-base-archmodelresult">arch.univariate.base.ARCHModelResult<a class="headerlink" href="#arch-univariate-base-archmodelresult" title="Link to this heading">¶</a></h1>
 <dl class="py class objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.base.ARCHModelResult">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.univariate.base.</span></span><span class="sig-name descname"><span class="pre">ARCHModelResult</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelResult.__init__.params" title="arch.univariate.base.ARCHModelResult.__init__.params (Python parameter) — Estimated parameters"><span class="n"><span class="pre">params</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelResult.__init__.param_cov" title="arch.univariate.base.ARCHModelResult.__init__.param_cov (Python parameter) — Estimated variance-covariance matrix of params.  If none, calls method to compute variance from model when parameter covariance is first used from result"><span class="n"><span class="pre">param_cov</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelResult.__init__.r2" title="arch.univariate.base.ARCHModelResult.__init__.r2 (Python parameter) — Model R-squared"><span class="n"><span class="pre">r2</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelResult.__init__.resid" title="arch.univariate.base.ARCHModelResult.__init__.resid (Python parameter) — Residuals from model.  Residuals have same shape as original data and contain nan-values in locations not used in estimation"><span class="n"><span class="pre">resid</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelResult.__init__.volatility" title="arch.univariate.base.ARCHModelResult.__init__.volatility (Python parameter) — Conditional volatility from model"><span class="n"><span class="pre">volatility</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelResult.__init__.cov_type" title="arch.univariate.base.ARCHModelResult.__init__.cov_type (Python parameter) — String describing the covariance estimator used"><span class="n"><span class="pre">cov_type</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelResult.__init__.dep_var" title="arch.univariate.base.ARCHModelResult.__init__.dep_var (Python parameter) — Dependent variable"><span class="n"><span class="pre">dep_var</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">pandas.Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelResult.__init__.names" title="arch.univariate.base.ARCHModelResult.__init__.names (Python parameter) — Model parameter names"><span class="n"><span class="pre">names</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="p"><span class="pre">]</span></span></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelResult.__init__.loglikelihood" title="arch.univariate.base.ARCHModelResult.__init__.loglikelihood (Python parameter) — Loglikelihood at estimated parameters"><span class="n"><span class="pre">loglikelihood</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelResult.__init__.is_pandas" title="arch.univariate.base.ARCHModelResult.__init__.is_pandas (Python parameter) — Whether the original input was pandas"><span class="n"><span class="pre">is_pandas</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelResult.__init__.optim_output" title="arch.univariate.base.ARCHModelResult.__init__.optim_output (Python parameter) — Result of log-likelihood optimization"><span class="n"><span class="pre">optim_output</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.OptimizeResult.html#scipy.optimize.OptimizeResult" title="(in SciPy v1.11.3)"><span class="pre">OptimizeResult</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelResult.__init__.fit_start" title="arch.univariate.base.ARCHModelResult.__init__.fit_start (Python parameter) — Integer index of the first observation used to fit the model"><span class="n"><span class="pre">fit_start</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelResult.__init__.fit_stop" title="arch.univariate.base.ARCHModelResult.__init__.fit_stop (Python parameter) — Integer index of the last observation used to fit the model using slice notation fit_start:fit_stop"><span class="n"><span class="pre">fit_stop</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelResult.__init__.model" title="arch.univariate.base.ARCHModelResult.__init__.model (Python parameter) — The model object used to estimate the parameters"><span class="n"><span class="pre">model</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference internal" href="arch.univariate.base.ARCHModel.html#arch.univariate.base.ARCHModel" title="arch.univariate.base.ARCHModel (Python class) — Abstract base class for mean models in ARCH processes.  Specifies the conditional mean process."><span class="pre">ARCHModel</span></a></span></span></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/univariate/base.html#ARCHModelResult"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.base.ARCHModelResult" title="Link to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">arch.univariate.base.</span></span><span class="sig-name descname"><span class="pre">ARCHModelResult</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelResult.__init__.params" title="arch.univariate.base.ARCHModelResult.__init__.params (Python parameter) — Estimated parameters"><span class="n"><span class="pre">params</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelResult.__init__.param_cov" title="arch.univariate.base.ARCHModelResult.__init__.param_cov (Python parameter) — Estimated variance-covariance matrix of params.  If none, calls method to compute variance from model when parameter covariance is first used from result"><span class="n"><span class="pre">param_cov</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelResult.__init__.r2" title="arch.univariate.base.ARCHModelResult.__init__.r2 (Python parameter) — Model R-squared"><span class="n"><span class="pre">r2</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelResult.__init__.resid" title="arch.univariate.base.ARCHModelResult.__init__.resid (Python parameter) — Residuals from model.  Residuals have same shape as original data and contain nan-values in locations not used in estimation"><span class="n"><span class="pre">resid</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelResult.__init__.volatility" title="arch.univariate.base.ARCHModelResult.__init__.volatility (Python parameter) — Conditional volatility from model"><span class="n"><span class="pre">volatility</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelResult.__init__.cov_type" title="arch.univariate.base.ARCHModelResult.__init__.cov_type (Python parameter) — String describing the covariance estimator used"><span class="n"><span class="pre">cov_type</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelResult.__init__.dep_var" title="arch.univariate.base.ARCHModelResult.__init__.dep_var (Python parameter) — Dependent variable"><span class="n"><span class="pre">dep_var</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">pandas.Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelResult.__init__.names" title="arch.univariate.base.ARCHModelResult.__init__.names (Python parameter) — Model parameter names"><span class="n"><span class="pre">names</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="p"><span class="pre">]</span></span></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelResult.__init__.loglikelihood" title="arch.univariate.base.ARCHModelResult.__init__.loglikelihood (Python parameter) — Loglikelihood at estimated parameters"><span class="n"><span class="pre">loglikelihood</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelResult.__init__.is_pandas" title="arch.univariate.base.ARCHModelResult.__init__.is_pandas (Python parameter) — Whether the original input was pandas"><span class="n"><span class="pre">is_pandas</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelResult.__init__.optim_output" title="arch.univariate.base.ARCHModelResult.__init__.optim_output (Python parameter) — Result of log-likelihood optimization"><span class="n"><span class="pre">optim_output</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.OptimizeResult.html#scipy.optimize.OptimizeResult" title="(in SciPy v1.11.4)"><span class="pre">OptimizeResult</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelResult.__init__.fit_start" title="arch.univariate.base.ARCHModelResult.__init__.fit_start (Python parameter) — Integer index of the first observation used to fit the model"><span class="n"><span class="pre">fit_start</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelResult.__init__.fit_stop" title="arch.univariate.base.ARCHModelResult.__init__.fit_stop (Python parameter) — Integer index of the last observation used to fit the model using slice notation fit_start:fit_stop"><span class="n"><span class="pre">fit_stop</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelResult.__init__.model" title="arch.univariate.base.ARCHModelResult.__init__.model (Python parameter) — The model object used to estimate the parameters"><span class="n"><span class="pre">model</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference internal" href="arch.univariate.base.ARCHModel.html#arch.univariate.base.ARCHModel" title="arch.univariate.base.ARCHModel (Python class) — Abstract base class for mean models in ARCH processes.  Specifies the conditional mean process."><span class="pre">ARCHModel</span></a></span></span></em></span><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/arch/univariate/base.html#ARCHModelResult"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.base.ARCHModelResult" title="Link to this definition">¶</a></dt>
 <dd><p>Results from estimation of an ARCHModel model</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.base.ARCHModelResult-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.base.ARCHModelResult-parameters" title="Permalink to this headline">¶</a></dt>
@@ -1632,7 +1632,7 @@ <h1 id="arch-univariate-base-archmodelresult">arch.univariate.base.ARCHModelResu
 </dd>
 <dt id="arch.univariate.base.ARCHModelResult.__init__.cov_type"><span class="n sig-name">cov_type</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)">str</a></span></span><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.__init__.cov_type" title="Permalink to this definition">¶</a></dt><dd><p>String describing the covariance estimator used</p>
 </dd>
-<dt id="arch.univariate.base.ARCHModelResult.__init__.dep_var"><span class="n sig-name">dep_var</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">pandas.Series</a></span></span><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.__init__.dep_var" title="Permalink to this definition">¶</a></dt><dd><p>Dependent variable</p>
+<dt id="arch.univariate.base.ARCHModelResult.__init__.dep_var"><span class="n sig-name">dep_var</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">pandas.Series</a></span></span><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.__init__.dep_var" title="Permalink to this definition">¶</a></dt><dd><p>Dependent variable</p>
 </dd>
 <dt id="arch.univariate.base.ARCHModelResult.__init__.names"><span class="n sig-name">names</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)">list</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)">str</a><span class="p">]</span></span></span><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.__init__.names" title="Permalink to this definition">¶</a></dt><dd><p>Model parameter names</p>
 </dd>
@@ -1640,7 +1640,7 @@ <h1 id="arch-univariate-base-archmodelresult">arch.univariate.base.ARCHModelResu
 </dd>
 <dt id="arch.univariate.base.ARCHModelResult.__init__.is_pandas"><span class="n sig-name">is_pandas</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)">bool</a></span></span><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.__init__.is_pandas" title="Permalink to this definition">¶</a></dt><dd><p>Whether the original input was pandas</p>
 </dd>
-<dt id="arch.univariate.base.ARCHModelResult.__init__.optim_output"><span class="n sig-name">optim_output</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.OptimizeResult.html#scipy.optimize.OptimizeResult" title="(in SciPy v1.11.3)">OptimizeResult</a></span></span><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.__init__.optim_output" title="Permalink to this definition">¶</a></dt><dd><p>Result of log-likelihood optimization</p>
+<dt id="arch.univariate.base.ARCHModelResult.__init__.optim_output"><span class="n sig-name">optim_output</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.OptimizeResult.html#scipy.optimize.OptimizeResult" title="(in SciPy v1.11.4)">OptimizeResult</a></span></span><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.__init__.optim_output" title="Permalink to this definition">¶</a></dt><dd><p>Result of log-likelihood optimization</p>
 </dd>
 <dt id="arch.univariate.base.ARCHModelResult.__init__.fit_start"><span class="n sig-name">fit_start</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a></span></span><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.__init__.fit_start" title="Permalink to this definition">¶</a></dt><dd><p>Integer index of the first observation used to fit the model</p>
 </dd>
@@ -1859,7 +1859,7 @@ <h1 id="arch-univariate-base-archmodelresult">arch.univariate.base.ARCHModelResu
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diff --git a/devel/univariate/generated/arch.univariate.distribution.Distribution.html b/devel/univariate/generated/arch.univariate.distribution.Distribution.html
index a87c14a4c8..41d1b85ded 100644
--- a/devel/univariate/generated/arch.univariate.distribution.Distribution.html
+++ b/devel/univariate/generated/arch.univariate.distribution.Distribution.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.distribution.Distribution - arch v6.2.1 (+21)</title>
+        <title>arch.univariate.distribution.Distribution - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
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+        <link rel="stylesheet" type="text/css" href="../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
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@@ -92,7 +92,7 @@
 
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+    <a href="../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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     </a>
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@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
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-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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@@ -189,10 +189,10 @@
 
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+    <a href="../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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@@ -1328,7 +1328,7 @@ <h1 id="arch-univariate-distribution-distribution">arch.univariate.distribution.
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diff --git a/devel/univariate/generated/arch.univariate.recursions_python.VolatilityUpdater.html b/devel/univariate/generated/arch.univariate.recursions_python.VolatilityUpdater.html
index 6535366536..59a03b133e 100644
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+++ b/devel/univariate/generated/arch.univariate.recursions_python.VolatilityUpdater.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.recursions_python.VolatilityUpdater - arch v6.2.1 (+21)</title>
+        <title>arch.univariate.recursions_python.VolatilityUpdater - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
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@@ -92,7 +92,7 @@
 
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-    <a href="../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
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-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../_static/bw-logo.svg" alt="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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diff --git a/devel/univariate/generated/arch.univariate.volatility.VolatilityProcess.html b/devel/univariate/generated/arch.univariate.volatility.VolatilityProcess.html
index 5699bf98eb..392edacf1c 100644
--- a/devel/univariate/generated/arch.univariate.volatility.VolatilityProcess.html
+++ b/devel/univariate/generated/arch.univariate.volatility.VolatilityProcess.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.volatility.VolatilityProcess - arch v6.2.1 (+21)</title>
+        <title>arch.univariate.volatility.VolatilityProcess - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
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@@ -92,7 +92,7 @@
 
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-    <a href="../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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diff --git a/devel/univariate/generated/generated/arch.univariate.APARCH.backcast.html b/devel/univariate/generated/generated/arch.univariate.APARCH.backcast.html
index 69695b28c9..c674b0d13b 100644
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@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.APARCH.backcast - arch v6.2.1 (+21)</title>
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@@ -92,7 +92,7 @@
 
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index 93c9aea210..801ff78074 100644
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+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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+    arch v6.2.1 (+23)
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-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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index 956847934d..391298c78d 100644
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diff --git a/devel/univariate/generated/generated/arch.univariate.APARCH.simulate.html b/devel/univariate/generated/generated/arch.univariate.APARCH.simulate.html
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 <h1 id="arch-univariate-aparch-simulate">arch.univariate.APARCH.simulate<a class="headerlink" href="#arch-univariate-aparch-simulate" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.APARCH.simulate">
-<span class="sig-prename descclassname"><span class="pre">APARCH.</span></span><span class="sig-name descname"><span class="pre">simulate</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.APARCH.simulate.parameters" title="arch.univariate.APARCH.simulate.parameters (Python parameter) — Parameters required to simulate the volatility model"><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.APARCH.simulate.nobs" title="arch.univariate.APARCH.simulate.nobs (Python parameter) — Number of data points to simulate"><span class="n"><span class="pre">nobs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.APARCH.simulate.rng" title="arch.univariate.APARCH.simulate.rng (Python parameter) — Callable function that takes a single integer input and returns a vector of random numbers"><span class="n"><span class="pre">rng</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.12)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.APARCH.simulate.burn" title="arch.univariate.APARCH.simulate.burn (Python parameter) — Number of additional observations to generate when initializing the simulation"><span class="n"><span class="pre">burn</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">500</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.APARCH.simulate.initial_value" title="arch.univariate.APARCH.simulate.initial_value (Python parameter) — Scalar or array of initial values to use when initializing the simulation"><span class="n"><span class="pre">initial_value</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#APARCH.simulate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.APARCH.simulate" title="Link to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">APARCH.</span></span><span class="sig-name descname"><span class="pre">simulate</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.APARCH.simulate.parameters" title="arch.univariate.APARCH.simulate.parameters (Python parameter) — Parameters required to simulate the volatility model"><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.APARCH.simulate.nobs" title="arch.univariate.APARCH.simulate.nobs (Python parameter) — Number of data points to simulate"><span class="n"><span class="pre">nobs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.APARCH.simulate.rng" title="arch.univariate.APARCH.simulate.rng (Python parameter) — Callable function that takes a single integer input and returns a vector of random numbers"><span class="n"><span class="pre">rng</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.12)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.APARCH.simulate.burn" title="arch.univariate.APARCH.simulate.burn (Python parameter) — Number of additional observations to generate when initializing the simulation"><span class="n"><span class="pre">burn</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">500</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.APARCH.simulate.initial_value" title="arch.univariate.APARCH.simulate.initial_value (Python parameter) — Scalar or array of initial values to use when initializing the simulation"><span class="n"><span class="pre">initial_value</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#APARCH.simulate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.APARCH.simulate" title="Link to this definition">¶</a></dt>
 <dd><p>Simulate data from the model</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.APARCH.simulate-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.APARCH.simulate-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.APARCH.simulate.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.univariate.APARCH.simulate.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Parameters required to simulate the volatility model</p>
+<dt id="arch.univariate.APARCH.simulate.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a></span></span><a class="headerlink" href="#arch.univariate.APARCH.simulate.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Parameters required to simulate the volatility model</p>
 </dd>
 <dt id="arch.univariate.APARCH.simulate.nobs"><span class="n sig-name">nobs</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a></span></span><a class="headerlink" href="#arch.univariate.APARCH.simulate.nobs" title="Permalink to this definition">¶</a></dt><dd><p>Number of data points to simulate</p>
 </dd>
@@ -1467,7 +1467,7 @@ <h1 id="arch-univariate-aparch-simulate">arch.univariate.APARCH.simulate<a class
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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   </body>
diff --git a/devel/univariate/generated/generated/arch.univariate.APARCH.start.html b/devel/univariate/generated/generated/arch.univariate.APARCH.start.html
index f35d29bd51..10e6eae8b6 100644
--- a/devel/univariate/generated/generated/arch.univariate.APARCH.start.html
+++ b/devel/univariate/generated/generated/arch.univariate.APARCH.start.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.APARCH.start - arch v6.2.1 (+21)</title>
+        <title>arch.univariate.APARCH.start - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
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@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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@@ -1319,7 +1319,7 @@ <h1 id="arch-univariate-aparch-start">arch.univariate.APARCH.start<a class="head
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-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
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diff --git a/devel/univariate/generated/generated/arch.univariate.APARCH.starting_values.html b/devel/univariate/generated/generated/arch.univariate.APARCH.starting_values.html
index dc523de070..ca93525e65 100644
--- a/devel/univariate/generated/generated/arch.univariate.APARCH.starting_values.html
+++ b/devel/univariate/generated/generated/arch.univariate.APARCH.starting_values.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.APARCH.starting_values - arch v6.2.1 (+21)</title>
+        <title>arch.univariate.APARCH.starting_values - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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@@ -1414,7 +1414,7 @@ <h1 id="arch-univariate-aparch-starting-values">arch.univariate.APARCH.starting_
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-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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diff --git a/devel/univariate/generated/generated/arch.univariate.APARCH.stop.html b/devel/univariate/generated/generated/arch.univariate.APARCH.stop.html
index fc24ec5c07..0f84affd15 100644
--- a/devel/univariate/generated/generated/arch.univariate.APARCH.stop.html
+++ b/devel/univariate/generated/generated/arch.univariate.APARCH.stop.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.APARCH.stop - arch v6.2.1 (+21)</title>
+        <title>arch.univariate.APARCH.stop - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
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@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
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-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
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-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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@@ -1319,7 +1319,7 @@ <h1 id="arch-univariate-aparch-stop">arch.univariate.APARCH.stop<a class="header
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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diff --git a/devel/univariate/generated/generated/arch.univariate.APARCH.update.html b/devel/univariate/generated/generated/arch.univariate.APARCH.update.html
index c2052d4400..a417473382 100644
--- a/devel/univariate/generated/generated/arch.univariate.APARCH.update.html
+++ b/devel/univariate/generated/generated/arch.univariate.APARCH.update.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.APARCH.update - arch v6.2.1 (+21)</title>
+        <title>arch.univariate.APARCH.update - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
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@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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@@ -1496,7 +1496,7 @@ <h1 id="arch-univariate-aparch-update">arch.univariate.APARCH.update<a class="he
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@@ -47,7 +47,7 @@
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+    arch v6.2.1 (+23)
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-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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index 63c7aded9b..ddabe02c9c 100644
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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+            arch v6.2.1 (+23)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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+    arch v6.2.1 (+23)
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index 17b962fe3e..a0389315f0 100644
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@@ -47,7 +47,7 @@
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@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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@@ -102,7 +102,7 @@
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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+    arch v6.2.1 (+23)
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-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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index 6eb9356c36..2e92831a36 100644
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@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ARCH.backcast_transform - arch v6.2.1 (+21)</title>
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@@ -47,7 +47,7 @@
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@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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diff --git a/devel/univariate/generated/generated/arch.univariate.ARCH.bounds.html b/devel/univariate/generated/generated/arch.univariate.ARCH.bounds.html
index e28b8f7d9b..1c565331ae 100644
--- a/devel/univariate/generated/generated/arch.univariate.ARCH.bounds.html
+++ b/devel/univariate/generated/generated/arch.univariate.ARCH.bounds.html
@@ -15,7 +15,7 @@
     
   
       
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@@ -47,7 +47,7 @@
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+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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@@ -1383,7 +1383,7 @@ <h1 id="arch-univariate-arch-bounds">arch.univariate.ARCH.bounds<a class="header
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diff --git a/devel/univariate/generated/generated/arch.univariate.ARCH.compute_variance.html b/devel/univariate/generated/generated/arch.univariate.ARCH.compute_variance.html
index 109102457e..3756777c99 100644
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@@ -15,7 +15,7 @@
     
   
       
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+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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+            arch v6.2.1 (+23)
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-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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diff --git a/devel/univariate/generated/generated/arch.univariate.ARCH.constraints.html b/devel/univariate/generated/generated/arch.univariate.ARCH.constraints.html
index 363d988ce5..8ebadbec13 100644
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@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ARCH.constraints - arch v6.2.1 (+21)</title>
+        <title>arch.univariate.ARCH.constraints - arch v6.2.1 (+23)</title>
       
     
 
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+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
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-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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diff --git a/devel/univariate/generated/generated/arch.univariate.ARCH.forecast.html b/devel/univariate/generated/generated/arch.univariate.ARCH.forecast.html
index a9f59c67d6..cb9bf6558d 100644
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+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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 <h1 id="arch-univariate-arch-forecast">arch.univariate.ARCH.forecast<a class="headerlink" href="#arch-univariate-arch-forecast" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.ARCH.forecast">
-<span class="sig-prename descclassname"><span class="pre">ARCH.</span></span><span class="sig-name descname"><span class="pre">forecast</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCH.forecast.parameters" title="arch.univariate.ARCH.forecast.parameters (Python parameter) — Parameters required to forecast the volatility model"><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCH.forecast.resids" title="arch.univariate.ARCH.forecast.resids (Python parameter) — Residuals to use in the recursion"><span class="n"><span class="pre">resids</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCH.forecast.backcast" title="arch.univariate.ARCH.forecast.backcast (Python parameter) — Value to use when initializing the recursion"><span class="n"><span class="pre">backcast</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCH.forecast.var_bounds" title="arch.univariate.ARCH.forecast.var_bounds (Python parameter) — Array containing columns of lower and upper bounds"><span class="n"><span class="pre">var_bounds</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCH.forecast.start" title="arch.univariate.ARCH.forecast.start (Python parameter) — Index of the first observation to use as the starting point for the forecast.  Default is len(resids)."><span class="n"><span class="pre">start</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCH.forecast.horizon" title="arch.univariate.ARCH.forecast.horizon (Python parameter) — Forecast horizon.  Must be 1 or larger.  Forecasts are produced for horizons in [1, horizon]."><span class="n"><span class="pre">horizon</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCH.forecast.method" title="arch.univariate.ARCH.forecast.method (Python parameter) — Method to use when producing the forecast. The default is analytic."><span class="n"><span class="pre">method</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'analytic'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'simulation'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'bootstrap'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;analytic&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCH.forecast.simulations" title="arch.univariate.ARCH.forecast.simulations (Python parameter) — Number of simulations to run when computing the forecast using either simulation or bootstrap."><span class="n"><span class="pre">simulations</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCH.forecast.rng" title="arch.univariate.ARCH.forecast.rng (Python parameter) — Callable random number generator required if method is 'simulation'. Must take a single shape input and return random samples numbers with that shape."><span class="n"><span class="pre">rng</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.12)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCH.forecast.random_state" title="arch.univariate.ARCH.forecast.random_state (Python parameter) — NumPy RandomState instance to use when method is 'bootstrap'"><span class="n"><span class="pre">random_state</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.26)"><span class="pre">RandomState</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><span class="pre">VarianceForecast</span></span></span></span><a class="headerlink" href="#arch.univariate.ARCH.forecast" title="Link to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">ARCH.</span></span><span class="sig-name descname"><span class="pre">forecast</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCH.forecast.parameters" title="arch.univariate.ARCH.forecast.parameters (Python parameter) — Parameters required to forecast the volatility model"><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCH.forecast.resids" title="arch.univariate.ARCH.forecast.resids (Python parameter) — Residuals to use in the recursion"><span class="n"><span class="pre">resids</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCH.forecast.backcast" title="arch.univariate.ARCH.forecast.backcast (Python parameter) — Value to use when initializing the recursion"><span class="n"><span class="pre">backcast</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCH.forecast.var_bounds" title="arch.univariate.ARCH.forecast.var_bounds (Python parameter) — Array containing columns of lower and upper bounds"><span class="n"><span class="pre">var_bounds</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCH.forecast.start" title="arch.univariate.ARCH.forecast.start (Python parameter) — Index of the first observation to use as the starting point for the forecast.  Default is len(resids)."><span class="n"><span class="pre">start</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCH.forecast.horizon" title="arch.univariate.ARCH.forecast.horizon (Python parameter) — Forecast horizon.  Must be 1 or larger.  Forecasts are produced for horizons in [1, horizon]."><span class="n"><span class="pre">horizon</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCH.forecast.method" title="arch.univariate.ARCH.forecast.method (Python parameter) — Method to use when producing the forecast. The default is analytic."><span class="n"><span class="pre">method</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'analytic'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'simulation'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'bootstrap'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;analytic&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCH.forecast.simulations" title="arch.univariate.ARCH.forecast.simulations (Python parameter) — Number of simulations to run when computing the forecast using either simulation or bootstrap."><span class="n"><span class="pre">simulations</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCH.forecast.rng" title="arch.univariate.ARCH.forecast.rng (Python parameter) — Callable random number generator required if method is 'simulation'. Must take a single shape input and return random samples numbers with that shape."><span class="n"><span class="pre">rng</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.12)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCH.forecast.random_state" title="arch.univariate.ARCH.forecast.random_state (Python parameter) — NumPy RandomState instance to use when method is 'bootstrap'"><span class="n"><span class="pre">random_state</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.26)"><span class="pre">RandomState</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><span class="pre">VarianceForecast</span></span></span></span><a class="headerlink" href="#arch.univariate.ARCH.forecast" title="Link to this definition">¶</a></dt>
 <dd><p>Forecast volatility from the model</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.ARCH.forecast-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.ARCH.forecast-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.ARCH.forecast.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.univariate.ARCH.forecast.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Parameters required to forecast the volatility model</p>
+<dt id="arch.univariate.ARCH.forecast.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a></span></span><a class="headerlink" href="#arch.univariate.ARCH.forecast.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Parameters required to forecast the volatility model</p>
 </dd>
 <dt id="arch.univariate.ARCH.forecast.resids"><span class="n sig-name">resids</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.ARCH.forecast.resids" title="Permalink to this definition">¶</a></dt><dd><p>Residuals to use in the recursion</p>
 </dd>
@@ -1562,7 +1562,7 @@ <h1 id="arch-univariate-arch-forecast">arch.univariate.ARCH.forecast<a class="he
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diff --git a/devel/univariate/generated/generated/arch.univariate.ARCH.name.html b/devel/univariate/generated/generated/arch.univariate.ARCH.name.html
index b32e65add1..be3d88c110 100644
--- a/devel/univariate/generated/generated/arch.univariate.ARCH.name.html
+++ b/devel/univariate/generated/generated/arch.univariate.ARCH.name.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ARCH.name - arch v6.2.1 (+21)</title>
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@@ -102,7 +102,7 @@
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+            arch v6.2.1 (+23)
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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@@ -1289,7 +1289,7 @@ <h1 id="arch-univariate-arch-name">arch.univariate.ARCH.name<a class="headerlink
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diff --git a/devel/univariate/generated/generated/arch.univariate.ARCH.num_params.html b/devel/univariate/generated/generated/arch.univariate.ARCH.num_params.html
index daaacbd960..8e83a64595 100644
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@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ARCH.num_params - arch v6.2.1 (+21)</title>
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@@ -47,7 +47,7 @@
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+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
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+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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@@ -1289,7 +1289,7 @@ <h1 id="arch-univariate-arch-num-params">arch.univariate.ARCH.num_params<a class
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-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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diff --git a/devel/univariate/generated/generated/arch.univariate.ARCH.parameter_names.html b/devel/univariate/generated/generated/arch.univariate.ARCH.parameter_names.html
index cb504d55c4..18d07f3483 100644
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@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ARCH.parameter_names - arch v6.2.1 (+21)</title>
+        <title>arch.univariate.ARCH.parameter_names - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
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@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
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-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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@@ -1337,7 +1337,7 @@ <h1 id="arch-univariate-arch-parameter-names">arch.univariate.ARCH.parameter_nam
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diff --git a/devel/univariate/generated/generated/arch.univariate.ARCH.simulate.html b/devel/univariate/generated/generated/arch.univariate.ARCH.simulate.html
index 1e9cb59432..8d01ca5f97 100644
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+++ b/devel/univariate/generated/generated/arch.univariate.ARCH.simulate.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ARCH.simulate - arch v6.2.1 (+21)</title>
+        <title>arch.univariate.ARCH.simulate - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
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@@ -92,7 +92,7 @@
 
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+            arch v6.2.1 (+23)
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@@ -189,10 +189,10 @@
 
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+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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@@ -1293,12 +1293,12 @@
 <h1 id="arch-univariate-arch-simulate">arch.univariate.ARCH.simulate<a class="headerlink" href="#arch-univariate-arch-simulate" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.ARCH.simulate">
-<span class="sig-prename descclassname"><span class="pre">ARCH.</span></span><span class="sig-name descname"><span class="pre">simulate</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCH.simulate.parameters" title="arch.univariate.ARCH.simulate.parameters (Python parameter) — Parameters required to simulate the volatility model"><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCH.simulate.nobs" title="arch.univariate.ARCH.simulate.nobs (Python parameter) — Number of data points to simulate"><span class="n"><span class="pre">nobs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCH.simulate.rng" title="arch.univariate.ARCH.simulate.rng (Python parameter) — Callable function that takes a single integer input and returns a vector of random numbers"><span class="n"><span class="pre">rng</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.12)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCH.simulate.burn" title="arch.univariate.ARCH.simulate.burn (Python parameter) — Number of additional observations to generate when initializing the simulation"><span class="n"><span class="pre">burn</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">500</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCH.simulate.initial_value" title="arch.univariate.ARCH.simulate.initial_value (Python parameter) — Scalar or array of initial values to use when initializing the simulation"><span class="n"><span class="pre">initial_value</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span></span></span></span><a class="headerlink" href="#arch.univariate.ARCH.simulate" title="Link to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">ARCH.</span></span><span class="sig-name descname"><span class="pre">simulate</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCH.simulate.parameters" title="arch.univariate.ARCH.simulate.parameters (Python parameter) — Parameters required to simulate the volatility model"><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCH.simulate.nobs" title="arch.univariate.ARCH.simulate.nobs (Python parameter) — Number of data points to simulate"><span class="n"><span class="pre">nobs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCH.simulate.rng" title="arch.univariate.ARCH.simulate.rng (Python parameter) — Callable function that takes a single integer input and returns a vector of random numbers"><span class="n"><span class="pre">rng</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.12)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCH.simulate.burn" title="arch.univariate.ARCH.simulate.burn (Python parameter) — Number of additional observations to generate when initializing the simulation"><span class="n"><span class="pre">burn</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">500</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCH.simulate.initial_value" title="arch.univariate.ARCH.simulate.initial_value (Python parameter) — Scalar or array of initial values to use when initializing the simulation"><span class="n"><span class="pre">initial_value</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span></span></span></span><a class="headerlink" href="#arch.univariate.ARCH.simulate" title="Link to this definition">¶</a></dt>
 <dd><p>Simulate data from the model</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.ARCH.simulate-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.ARCH.simulate-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.ARCH.simulate.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.univariate.ARCH.simulate.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Parameters required to simulate the volatility model</p>
+<dt id="arch.univariate.ARCH.simulate.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a></span></span><a class="headerlink" href="#arch.univariate.ARCH.simulate.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Parameters required to simulate the volatility model</p>
 </dd>
 <dt id="arch.univariate.ARCH.simulate.nobs"><span class="n sig-name">nobs</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a></span></span><a class="headerlink" href="#arch.univariate.ARCH.simulate.nobs" title="Permalink to this definition">¶</a></dt><dd><p>Number of data points to simulate</p>
 </dd>
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+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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-            arch v6.2.1 (+21)
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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index 3f831c5fcc..1be552d9cc 100644
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@@ -15,7 +15,7 @@
     
   
       
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+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
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index a64ed1c363..6bc1b16ed0 100644
--- a/devel/univariate/generated/generated/arch.univariate.ARCH.update.html
+++ b/devel/univariate/generated/generated/arch.univariate.ARCH.update.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ARCH.update - arch v6.2.1 (+21)</title>
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@@ -47,7 +47,7 @@
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@@ -92,7 +92,7 @@
 
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@@ -102,7 +102,7 @@
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@@ -189,10 +189,10 @@
 
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+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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+    arch v6.2.1 (+23)
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-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
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diff --git a/devel/univariate/generated/generated/arch.univariate.ARCH.updateable.html b/devel/univariate/generated/generated/arch.univariate.ARCH.updateable.html
index 547a05fc9b..36720763c3 100644
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+++ b/devel/univariate/generated/generated/arch.univariate.ARCH.updateable.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ARCH.updateable - arch v6.2.1 (+21)</title>
+        <title>arch.univariate.ARCH.updateable - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
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@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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@@ -102,7 +102,7 @@
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+            arch v6.2.1 (+23)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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+    arch v6.2.1 (+23)
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-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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index b0544cac06..01b951824a 100644
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+++ b/devel/univariate/generated/generated/arch.univariate.ARCH.variance_bounds.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ARCH.variance_bounds - arch v6.2.1 (+21)</title>
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@@ -47,7 +47,7 @@
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@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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@@ -102,7 +102,7 @@
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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+    arch v6.2.1 (+23)
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     <div class="md-nav__source">
@@ -1404,7 +1404,7 @@ <h1 id="arch-univariate-arch-variance-bounds">arch.univariate.ARCH.variance_boun
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-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
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diff --git a/devel/univariate/generated/generated/arch.univariate.ARCH.volatility_updater.html b/devel/univariate/generated/generated/arch.univariate.ARCH.volatility_updater.html
index 7d9d452877..36a54dad10 100644
--- a/devel/univariate/generated/generated/arch.univariate.ARCH.volatility_updater.html
+++ b/devel/univariate/generated/generated/arch.univariate.ARCH.volatility_updater.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ARCH.volatility_updater - arch v6.2.1 (+21)</title>
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@@ -47,7 +47,7 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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+            arch v6.2.1 (+23)
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   <nav class="md-header__inner md-grid" aria-label="Header">
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 <h1 id="arch-univariate-archinmean-fit">arch.univariate.ARCHInMean.fit<a class="headerlink" href="#arch-univariate-archinmean-fit" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.ARCHInMean.fit">
-<span class="sig-prename descclassname"><span class="pre">ARCHInMean.</span></span><span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCHInMean.fit.update_freq" title="arch.univariate.ARCHInMean.fit.update_freq (Python parameter) — Frequency of iteration updates.  Output is generated every update_freq iterations. Set to 0 to disable iterative output."><span class="n"><span class="pre">update_freq</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCHInMean.fit.disp" title="arch.univariate.ARCHInMean.fit.disp (Python parameter) — Either 'final' to print optimization result or 'off' to display nothing. If using a boolean, False is &quot;off&quot; and True is &quot;final&quot;"><span class="n"><span class="pre">disp</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'off'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'final'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;final&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCHInMean.fit.starting_values" title="arch.univariate.ARCHInMean.fit.starting_values (Python parameter) — Array of starting values to use.  If not provided, starting values are constructed by the model components."><span class="n"><span class="pre">starting_values</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCHInMean.fit.cov_type" title="arch.univariate.ARCHInMean.fit.cov_type (Python parameter) — Estimation method of parameter covariance.  Supported options are 'robust', which does not assume the Information Matrix Equality holds and 'classic' which does.  In the ARCH literature, 'robust' corresponds to Bollerslev-Wooldridge covariance estimator."><span class="n"><span class="pre">cov_type</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'robust'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'classic'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;robust&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCHInMean.fit.show_warning" title="arch.univariate.ARCHInMean.fit.show_warning (Python parameter) — Flag indicating whether convergence warnings should be shown."><span class="n"><span class="pre">show_warning</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCHInMean.fit.first_obs" title="arch.univariate.ARCHInMean.fit.first_obs (Python parameter) — First observation to use when estimating model"><span class="n"><span class="pre">first_obs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.12)"><span class="pre">datetime</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)"><span class="pre">datetime64</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.1)"><span class="pre">Timestamp</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCHInMean.fit.last_obs" title="arch.univariate.ARCHInMean.fit.last_obs (Python parameter) — Last observation to use when estimating model"><span class="n"><span class="pre">last_obs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.12)"><span class="pre">datetime</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)"><span class="pre">datetime64</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.1)"><span class="pre">Timestamp</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCHInMean.fit.tol" title="arch.univariate.ARCHInMean.fit.tol (Python parameter) — Tolerance for termination."><span class="n"><span class="pre">tol</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCHInMean.fit.options" title="arch.univariate.ARCHInMean.fit.options (Python parameter) — Options to pass to scipy.optimize.minimize.  Valid entries include 'ftol', 'eps', 'disp', and 'maxiter'."><span class="n"><span class="pre">options</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.12)"><span class="pre">Any</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCHInMean.fit.backcast" title="arch.univariate.ARCHInMean.fit.backcast (Python parameter) — Value to use as backcast. Should be measure \sigma^2_0 since model-specific non-linear transformations are applied to value before computing the variance recursions."><span class="n"><span class="pre">backcast</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference internal" href="../arch.univariate.base.ARCHModelResult.html#arch.univariate.base.ARCHModelResult" title="arch.univariate.base.ARCHModelResult (Python class) — Results from estimation of an ARCHModel model"><span class="pre">ARCHModelResult</span></a></span></span></span><a class="headerlink" href="#arch.univariate.ARCHInMean.fit" title="Link to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">ARCHInMean.</span></span><span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCHInMean.fit.update_freq" title="arch.univariate.ARCHInMean.fit.update_freq (Python parameter) — Frequency of iteration updates.  Output is generated every update_freq iterations. Set to 0 to disable iterative output."><span class="n"><span class="pre">update_freq</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCHInMean.fit.disp" title="arch.univariate.ARCHInMean.fit.disp (Python parameter) — Either 'final' to print optimization result or 'off' to display nothing. If using a boolean, False is &quot;off&quot; and True is &quot;final&quot;"><span class="n"><span class="pre">disp</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'off'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'final'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;final&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCHInMean.fit.starting_values" title="arch.univariate.ARCHInMean.fit.starting_values (Python parameter) — Array of starting values to use.  If not provided, starting values are constructed by the model components."><span class="n"><span class="pre">starting_values</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCHInMean.fit.cov_type" title="arch.univariate.ARCHInMean.fit.cov_type (Python parameter) — Estimation method of parameter covariance.  Supported options are 'robust', which does not assume the Information Matrix Equality holds and 'classic' which does.  In the ARCH literature, 'robust' corresponds to Bollerslev-Wooldridge covariance estimator."><span class="n"><span class="pre">cov_type</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'robust'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'classic'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;robust&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCHInMean.fit.show_warning" title="arch.univariate.ARCHInMean.fit.show_warning (Python parameter) — Flag indicating whether convergence warnings should be shown."><span class="n"><span class="pre">show_warning</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCHInMean.fit.first_obs" title="arch.univariate.ARCHInMean.fit.first_obs (Python parameter) — First observation to use when estimating model"><span class="n"><span class="pre">first_obs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.12)"><span class="pre">datetime</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)"><span class="pre">datetime64</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.4)"><span class="pre">Timestamp</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCHInMean.fit.last_obs" title="arch.univariate.ARCHInMean.fit.last_obs (Python parameter) — Last observation to use when estimating model"><span class="n"><span class="pre">last_obs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.12)"><span class="pre">datetime</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)"><span class="pre">datetime64</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.4)"><span class="pre">Timestamp</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCHInMean.fit.tol" title="arch.univariate.ARCHInMean.fit.tol (Python parameter) — Tolerance for termination."><span class="n"><span class="pre">tol</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCHInMean.fit.options" title="arch.univariate.ARCHInMean.fit.options (Python parameter) — Options to pass to scipy.optimize.minimize.  Valid entries include 'ftol', 'eps', 'disp', and 'maxiter'."><span class="n"><span class="pre">options</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.12)"><span class="pre">Any</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCHInMean.fit.backcast" title="arch.univariate.ARCHInMean.fit.backcast (Python parameter) — Value to use as backcast. Should be measure \sigma^2_0 since model-specific non-linear transformations are applied to value before computing the variance recursions."><span class="n"><span class="pre">backcast</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference internal" href="../arch.univariate.base.ARCHModelResult.html#arch.univariate.base.ARCHModelResult" title="arch.univariate.base.ARCHModelResult (Python class) — Results from estimation of an ARCHModel model"><span class="pre">ARCHModelResult</span></a></span></span></span><a class="headerlink" href="#arch.univariate.ARCHInMean.fit" title="Link to this definition">¶</a></dt>
 <dd><p>Estimate model parameters</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.ARCHInMean.fit-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.ARCHInMean.fit-parameters" title="Permalink to this headline">¶</a></dt>
@@ -1368,7 +1368,7 @@ <h1 id="arch-univariate-archinmean-fit">arch.univariate.ARCHInMean.fit<a class="
 <dt id="arch.univariate.ARCHInMean.fit.disp"><span class="n sig-name">disp</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)">bool</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'off'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'final'</span></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;final&#39;</span></code><a class="headerlink" href="#arch.univariate.ARCHInMean.fit.disp" title="Permalink to this definition">¶</a></dt><dd><p>Either ‘final’ to print optimization result or ‘off’ to display
 nothing. If using a boolean, False is “off” and True is “final”</p>
 </dd>
-<dt id="arch.univariate.ARCHInMean.fit.starting_values"><span class="n sig-name">starting_values</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ARCHInMean.fit.starting_values" title="Permalink to this definition">¶</a></dt><dd><p>Array of starting values to use.  If not provided, starting values
+<dt id="arch.univariate.ARCHInMean.fit.starting_values"><span class="n sig-name">starting_values</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ARCHInMean.fit.starting_values" title="Permalink to this definition">¶</a></dt><dd><p>Array of starting values to use.  If not provided, starting values
 are constructed by the model components.</p>
 </dd>
 <dt id="arch.univariate.ARCHInMean.fit.cov_type"><span class="n sig-name">cov_type</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><span class="s">'robust'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'classic'</span></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;robust&#39;</span></code><a class="headerlink" href="#arch.univariate.ARCHInMean.fit.cov_type" title="Permalink to this definition">¶</a></dt><dd><p>Estimation method of parameter covariance.  Supported options are
@@ -1378,9 +1378,9 @@ <h1 id="arch-univariate-archinmean-fit">arch.univariate.ARCHInMean.fit<a class="
 </dd>
 <dt id="arch.univariate.ARCHInMean.fit.show_warning"><span class="n sig-name">show_warning</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)">bool</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code><a class="headerlink" href="#arch.univariate.ARCHInMean.fit.show_warning" title="Permalink to this definition">¶</a></dt><dd><p>Flag indicating whether convergence warnings should be shown.</p>
 </dd>
-<dt id="arch.univariate.ARCHInMean.fit.first_obs"><span class="n sig-name">first_obs</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)">str</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.12)">datetime</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)">datetime64</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.1)">Timestamp</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ARCHInMean.fit.first_obs" title="Permalink to this definition">¶</a></dt><dd><p>First observation to use when estimating model</p>
+<dt id="arch.univariate.ARCHInMean.fit.first_obs"><span class="n sig-name">first_obs</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)">str</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.12)">datetime</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)">datetime64</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.4)">Timestamp</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ARCHInMean.fit.first_obs" title="Permalink to this definition">¶</a></dt><dd><p>First observation to use when estimating model</p>
 </dd>
-<dt id="arch.univariate.ARCHInMean.fit.last_obs"><span class="n sig-name">last_obs</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)">str</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.12)">datetime</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)">datetime64</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.1)">Timestamp</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ARCHInMean.fit.last_obs" title="Permalink to this definition">¶</a></dt><dd><p>Last observation to use when estimating model</p>
+<dt id="arch.univariate.ARCHInMean.fit.last_obs"><span class="n sig-name">last_obs</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)">str</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.12)">datetime</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)">datetime64</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.4)">Timestamp</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ARCHInMean.fit.last_obs" title="Permalink to this definition">¶</a></dt><dd><p>Last observation to use when estimating model</p>
 </dd>
 <dt id="arch.univariate.ARCHInMean.fit.tol"><span class="n sig-name">tol</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ARCHInMean.fit.tol" title="Permalink to this definition">¶</a></dt><dd><p>Tolerance for termination.</p>
 </dd>
@@ -1520,7 +1520,7 @@ <h1 id="arch-univariate-archinmean-fit">arch.univariate.ARCHInMean.fit<a class="
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index 0ebcc7330b..d83faf45fb 100644
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+++ b/devel/univariate/generated/generated/arch.univariate.ARCHInMean.fix.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ARCHInMean.fix - arch v6.2.1 (+21)</title>
+        <title>arch.univariate.ARCHInMean.fix - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
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@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
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@@ -1259,18 +1259,18 @@
 <h1 id="arch-univariate-archinmean-fix">arch.univariate.ARCHInMean.fix<a class="headerlink" href="#arch-univariate-archinmean-fix" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.ARCHInMean.fix">
-<span class="sig-prename descclassname"><span class="pre">ARCHInMean.</span></span><span class="sig-name descname"><span class="pre">fix</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCHInMean.fix.params" title="arch.univariate.ARCHInMean.fix.params (Python parameter) — User specified parameters to use when generating the result. Must have the correct number of parameters for a given choice of mean model, volatility model and distribution."><span class="n"><span class="pre">params</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCHInMean.fix.first_obs" title="arch.univariate.ARCHInMean.fix.first_obs (Python parameter) — First observation to use when fixing model"><span class="n"><span class="pre">first_obs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.12)"><span class="pre">datetime</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)"><span class="pre">datetime64</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.1)"><span class="pre">Timestamp</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCHInMean.fix.last_obs" title="arch.univariate.ARCHInMean.fix.last_obs (Python parameter) — Last observation to use when fixing model"><span class="n"><span class="pre">last_obs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.12)"><span class="pre">datetime</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)"><span class="pre">datetime64</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.1)"><span class="pre">Timestamp</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference internal" href="../arch.univariate.base.ARCHModelFixedResult.html#arch.univariate.base.ARCHModelFixedResult" title="arch.univariate.base.ARCHModelFixedResult (Python class) — Results for fixed parameters for an ARCHModel model"><span class="pre">ARCHModelFixedResult</span></a></span></span></span><a class="headerlink" href="#arch.univariate.ARCHInMean.fix" title="Link to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">ARCHInMean.</span></span><span class="sig-name descname"><span class="pre">fix</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCHInMean.fix.params" title="arch.univariate.ARCHInMean.fix.params (Python parameter) — User specified parameters to use when generating the result. Must have the correct number of parameters for a given choice of mean model, volatility model and distribution."><span class="n"><span class="pre">params</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCHInMean.fix.first_obs" title="arch.univariate.ARCHInMean.fix.first_obs (Python parameter) — First observation to use when fixing model"><span class="n"><span class="pre">first_obs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.12)"><span class="pre">datetime</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)"><span class="pre">datetime64</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.4)"><span class="pre">Timestamp</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCHInMean.fix.last_obs" title="arch.univariate.ARCHInMean.fix.last_obs (Python parameter) — Last observation to use when fixing model"><span class="n"><span class="pre">last_obs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.12)"><span class="pre">datetime</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)"><span class="pre">datetime64</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.4)"><span class="pre">Timestamp</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference internal" href="../arch.univariate.base.ARCHModelFixedResult.html#arch.univariate.base.ARCHModelFixedResult" title="arch.univariate.base.ARCHModelFixedResult (Python class) — Results for fixed parameters for an ARCHModel model"><span class="pre">ARCHModelFixedResult</span></a></span></span></span><a class="headerlink" href="#arch.univariate.ARCHInMean.fix" title="Link to this definition">¶</a></dt>
 <dd><p>Allows an ARCHModelFixedResult to be constructed from fixed parameters.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.ARCHInMean.fix-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.ARCHInMean.fix-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.ARCHInMean.fix.params"><span class="n sig-name">params</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.univariate.ARCHInMean.fix.params" title="Permalink to this definition">¶</a></dt><dd><p>User specified parameters to use when generating the result. Must
+<dt id="arch.univariate.ARCHInMean.fix.params"><span class="n sig-name">params</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a></span></span><a class="headerlink" href="#arch.univariate.ARCHInMean.fix.params" title="Permalink to this definition">¶</a></dt><dd><p>User specified parameters to use when generating the result. Must
 have the correct number of parameters for a given choice of mean
 model, volatility model and distribution.</p>
 </dd>
-<dt id="arch.univariate.ARCHInMean.fix.first_obs"><span class="n sig-name">first_obs</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)">str</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.12)">datetime</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)">datetime64</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.1)">Timestamp</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ARCHInMean.fix.first_obs" title="Permalink to this definition">¶</a></dt><dd><p>First observation to use when fixing model</p>
+<dt id="arch.univariate.ARCHInMean.fix.first_obs"><span class="n sig-name">first_obs</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)">str</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.12)">datetime</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)">datetime64</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.4)">Timestamp</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ARCHInMean.fix.first_obs" title="Permalink to this definition">¶</a></dt><dd><p>First observation to use when fixing model</p>
 </dd>
-<dt id="arch.univariate.ARCHInMean.fix.last_obs"><span class="n sig-name">last_obs</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)">str</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.12)">datetime</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)">datetime64</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.1)">Timestamp</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ARCHInMean.fix.last_obs" title="Permalink to this definition">¶</a></dt><dd><p>Last observation to use when fixing model</p>
+<dt id="arch.univariate.ARCHInMean.fix.last_obs"><span class="n sig-name">last_obs</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)">str</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.12)">datetime</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)">datetime64</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.4)">Timestamp</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ARCHInMean.fix.last_obs" title="Permalink to this definition">¶</a></dt><dd><p>Last observation to use when fixing model</p>
 </dd>
 </dl>
 </dd>
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 <h1 id="arch-univariate-archinmean-forecast">arch.univariate.ARCHInMean.forecast<a class="headerlink" href="#arch-univariate-archinmean-forecast" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.ARCHInMean.forecast">
-<span class="sig-prename descclassname"><span class="pre">ARCHInMean.</span></span><span class="sig-name descname"><span class="pre">forecast</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCHInMean.forecast.params" title="arch.univariate.ARCHInMean.forecast.params (Python parameter) — Parameters required to forecast. Must be identical in shape to the parameters computed by fitting the model."><span class="n"><span class="pre">params</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCHInMean.forecast.horizon" title="arch.univariate.ARCHInMean.forecast.horizon (Python parameter) — Number of steps to forecast"><span class="n"><span class="pre">horizon</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCHInMean.forecast.start" title="arch.univariate.ARCHInMean.forecast.start (Python parameter) — An integer, datetime or str indicating the first observation to produce the forecast for.  Datetimes can only be used with pandas inputs that have a datetime index. Strings must be convertible to a date time, such as in '1945-01-01'."><span class="n"><span class="pre">start</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.12)"><span class="pre">datetime</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)"><span class="pre">datetime64</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.1)"><span class="pre">Timestamp</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCHInMean.forecast.align" title="arch.univariate.ARCHInMean.forecast.align (Python parameter) — Either 'origin' or 'target'.  When set of 'origin', the t-th row of forecasts contains the forecasts for t+1, t+2, ..., t+h. When set to 'target', the t-th row contains the 1-step ahead forecast from time t-1, the 2 step from time t-2, ..., and the h-step from time t-h.  'target' simplified computing forecast errors since the realization and h-step forecast are aligned."><span class="n"><span class="pre">align</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'origin'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'target'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;origin&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCHInMean.forecast.method" title="arch.univariate.ARCHInMean.forecast.method (Python parameter) — Method to use when producing the forecast. The default is analytic. The method only affects the variance forecast generation.  Not all volatility models support all methods. In particular, volatility models that do not evolve in squares such as EGARCH or TARCH do not support the 'analytic' method for horizons &gt; 1."><span class="n"><span class="pre">method</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'analytic'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'simulation'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'bootstrap'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;analytic&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCHInMean.forecast.simulations" title="arch.univariate.ARCHInMean.forecast.simulations (Python parameter) — Number of simulations to run when computing the forecast using either simulation or bootstrap."><span class="n"><span class="pre">simulations</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCHInMean.forecast.rng" title="arch.univariate.ARCHInMean.forecast.rng (Python parameter) — Custom random number generator to use in simulation-based forecasts. Must produce random samples using the syntax rng(size) where size the 2-element tuple (simulations, horizon)."><span class="n"><span class="pre">rng</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.12)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCHInMean.forecast.random_state" title="arch.univariate.ARCHInMean.forecast.random_state (Python parameter) — NumPy RandomState instance to use when method is 'bootstrap'"><span class="n"><span class="pre">random_state</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.26)"><span class="pre">RandomState</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCHInMean.forecast.reindex" title="arch.univariate.ARCHInMean.forecast.reindex (Python parameter) — Whether to reindex the forecasts to have the same dimension as the series being forecast. Prior to 4.18 this was the default. As of 4.19 this is now optional. If not provided, a warning is raised about the future change in the default which will occur after September 2021."><span class="n"><span class="pre">reindex</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCHInMean.forecast.x" title="arch.univariate.ARCHInMean.forecast.x (Python parameter) — Values to use for exogenous regressors if any are included in the model. Three formats are accepted:"><span class="n"><span class="pre">x</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Hashable" title="(in Python v3.12)"><span class="pre">Hashable</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference internal" href="../arch.univariate.base.ARCHModelForecast.html#arch.univariate.base.ARCHModelForecast" title="arch.univariate.base.ARCHModelForecast (Python class) — Container for forecasts from an ARCH Model"><span class="pre">ARCHModelForecast</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/mean.html#ARCHInMean.forecast"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.ARCHInMean.forecast" title="Link to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">ARCHInMean.</span></span><span class="sig-name descname"><span class="pre">forecast</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCHInMean.forecast.params" title="arch.univariate.ARCHInMean.forecast.params (Python parameter) — Parameters required to forecast. Must be identical in shape to the parameters computed by fitting the model."><span class="n"><span class="pre">params</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCHInMean.forecast.horizon" title="arch.univariate.ARCHInMean.forecast.horizon (Python parameter) — Number of steps to forecast"><span class="n"><span class="pre">horizon</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCHInMean.forecast.start" title="arch.univariate.ARCHInMean.forecast.start (Python parameter) — An integer, datetime or str indicating the first observation to produce the forecast for.  Datetimes can only be used with pandas inputs that have a datetime index. Strings must be convertible to a date time, such as in '1945-01-01'."><span class="n"><span class="pre">start</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.12)"><span class="pre">datetime</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)"><span class="pre">datetime64</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.4)"><span class="pre">Timestamp</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCHInMean.forecast.align" title="arch.univariate.ARCHInMean.forecast.align (Python parameter) — Either 'origin' or 'target'.  When set of 'origin', the t-th row of forecasts contains the forecasts for t+1, t+2, ..., t+h. When set to 'target', the t-th row contains the 1-step ahead forecast from time t-1, the 2 step from time t-2, ..., and the h-step from time t-h.  'target' simplified computing forecast errors since the realization and h-step forecast are aligned."><span class="n"><span class="pre">align</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'origin'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'target'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;origin&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCHInMean.forecast.method" title="arch.univariate.ARCHInMean.forecast.method (Python parameter) — Method to use when producing the forecast. The default is analytic. The method only affects the variance forecast generation.  Not all volatility models support all methods. In particular, volatility models that do not evolve in squares such as EGARCH or TARCH do not support the 'analytic' method for horizons &gt; 1."><span class="n"><span class="pre">method</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'analytic'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'simulation'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'bootstrap'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;analytic&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCHInMean.forecast.simulations" title="arch.univariate.ARCHInMean.forecast.simulations (Python parameter) — Number of simulations to run when computing the forecast using either simulation or bootstrap."><span class="n"><span class="pre">simulations</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCHInMean.forecast.rng" title="arch.univariate.ARCHInMean.forecast.rng (Python parameter) — Custom random number generator to use in simulation-based forecasts. Must produce random samples using the syntax rng(size) where size the 2-element tuple (simulations, horizon)."><span class="n"><span class="pre">rng</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.12)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCHInMean.forecast.random_state" title="arch.univariate.ARCHInMean.forecast.random_state (Python parameter) — NumPy RandomState instance to use when method is 'bootstrap'"><span class="n"><span class="pre">random_state</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.26)"><span class="pre">RandomState</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCHInMean.forecast.reindex" title="arch.univariate.ARCHInMean.forecast.reindex (Python parameter) — Whether to reindex the forecasts to have the same dimension as the series being forecast. Prior to 4.18 this was the default. As of 4.19 this is now optional. If not provided, a warning is raised about the future change in the default which will occur after September 2021."><span class="n"><span class="pre">reindex</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCHInMean.forecast.x" title="arch.univariate.ARCHInMean.forecast.x (Python parameter) — Values to use for exogenous regressors if any are included in the model. Three formats are accepted:"><span class="n"><span class="pre">x</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Hashable" title="(in Python v3.12)"><span class="pre">Hashable</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference internal" href="../arch.univariate.base.ARCHModelForecast.html#arch.univariate.base.ARCHModelForecast" title="arch.univariate.base.ARCHModelForecast (Python class) — Container for forecasts from an ARCH Model"><span class="pre">ARCHModelForecast</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/mean.html#ARCHInMean.forecast"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.ARCHInMean.forecast" title="Link to this definition">¶</a></dt>
 <dd><p>Construct forecasts from estimated model</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.ARCHInMean.forecast-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.ARCHInMean.forecast-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.ARCHInMean.forecast.params"><span class="n sig-name">params</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.univariate.ARCHInMean.forecast.params" title="Permalink to this definition">¶</a></dt><dd><p>Parameters required to forecast. Must be identical in
+<dt id="arch.univariate.ARCHInMean.forecast.params"><span class="n sig-name">params</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a></span></span><a class="headerlink" href="#arch.univariate.ARCHInMean.forecast.params" title="Permalink to this definition">¶</a></dt><dd><p>Parameters required to forecast. Must be identical in
 shape to the parameters computed by fitting the model.</p>
 </dd>
 <dt id="arch.univariate.ARCHInMean.forecast.horizon"><span class="n sig-name">horizon</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code><a class="headerlink" href="#arch.univariate.ARCHInMean.forecast.horizon" title="Permalink to this definition">¶</a></dt><dd><p>Number of steps to forecast</p>
 </dd>
-<dt id="arch.univariate.ARCHInMean.forecast.start"><span class="n sig-name">start</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)">str</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.12)">datetime</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)">datetime64</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.1)">Timestamp</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ARCHInMean.forecast.start" title="Permalink to this definition">¶</a></dt><dd><p>An integer, datetime or str indicating the first observation to
+<dt id="arch.univariate.ARCHInMean.forecast.start"><span class="n sig-name">start</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)">str</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.12)">datetime</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)">datetime64</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.4)">Timestamp</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ARCHInMean.forecast.start" title="Permalink to this definition">¶</a></dt><dd><p>An integer, datetime or str indicating the first observation to
 produce the forecast for.  Datetimes can only be used with pandas
 inputs that have a datetime index. Strings must be convertible
 to a date time, such as in ‘1945-01-01’.</p>
@@ -1403,7 +1403,7 @@ <h1 id="arch-univariate-archinmean-forecast">arch.univariate.ARCHInMean.forecast
 </div>
 </p>
 </dd>
-<dt id="arch.univariate.ARCHInMean.forecast.x"><span class="n sig-name">x</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)">dict</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Hashable" title="(in Python v3.12)">Hashable</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ARCHInMean.forecast.x" title="Permalink to this definition">¶</a></dt><dd><p><p>Values to use for exogenous regressors if any are included in the
+<dt id="arch.univariate.ARCHInMean.forecast.x"><span class="n sig-name">x</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)">dict</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Hashable" title="(in Python v3.12)">Hashable</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ARCHInMean.forecast.x" title="Permalink to this definition">¶</a></dt><dd><p><p>Values to use for exogenous regressors if any are included in the
 model. Three formats are accepted:</p>
 <ul class="simple">
 <li><p>2-d array-like: This format can be used when there is a single
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index c35753e2c5..2e9ce66b31 100644
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@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ARCHInMean.name - arch v6.2.1 (+21)</title>
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+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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+            arch v6.2.1 (+23)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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index b05c401764..24f29be406 100644
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+++ b/devel/univariate/generated/generated/arch.univariate.ARCHInMean.num_params.html
@@ -15,7 +15,7 @@
     
   
       
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+        <title>arch.univariate.ARCHInMean.num_params - arch v6.2.1 (+23)</title>
       
     
 
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-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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index aa8b229a0b..b8d8ca908a 100644
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@@ -92,7 +92,7 @@
 
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+            arch v6.2.1 (+23)
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diff --git a/devel/univariate/generated/generated/arch.univariate.ARCHInMean.resids.html b/devel/univariate/generated/generated/arch.univariate.ARCHInMean.resids.html
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 <h1 id="arch-univariate-archinmean-resids">arch.univariate.ARCHInMean.resids<a class="headerlink" href="#arch-univariate-archinmean-resids" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.ARCHInMean.resids">
-<span class="sig-prename descclassname"><span class="pre">ARCHInMean.</span></span><span class="sig-name descname"><span class="pre">resids</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCHInMean.resids.params" title="arch.univariate.ARCHInMean.resids.params (Python parameter) — Model parameters"><span class="n"><span class="pre">params</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCHInMean.resids.y" title="arch.univariate.ARCHInMean.resids.y (Python parameter) — Alternative values to use when computing model residuals"><span class="n"><span class="pre">y</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCHInMean.resids.regressors" title="arch.univariate.ARCHInMean.resids.regressors (Python parameter) — Alternative regressor values to use when computing model residuals"><span class="n"><span class="pre">regressors</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/mean.html#ARCHInMean.resids"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.ARCHInMean.resids" title="Link to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">ARCHInMean.</span></span><span class="sig-name descname"><span class="pre">resids</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCHInMean.resids.params" title="arch.univariate.ARCHInMean.resids.params (Python parameter) — Model parameters"><span class="n"><span class="pre">params</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCHInMean.resids.y" title="arch.univariate.ARCHInMean.resids.y (Python parameter) — Alternative values to use when computing model residuals"><span class="n"><span class="pre">y</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCHInMean.resids.regressors" title="arch.univariate.ARCHInMean.resids.regressors (Python parameter) — Alternative regressor values to use when computing model residuals"><span class="n"><span class="pre">regressors</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/mean.html#ARCHInMean.resids"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.ARCHInMean.resids" title="Link to this definition">¶</a></dt>
 <dd><p>Compute model residuals</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.ARCHInMean.resids-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.ARCHInMean.resids-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
 <dt id="arch.univariate.ARCHInMean.resids.params"><span class="n sig-name">params</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.ARCHInMean.resids.params" title="Permalink to this definition">¶</a></dt><dd><p>Model parameters</p>
 </dd>
-<dt id="arch.univariate.ARCHInMean.resids.y"><span class="n sig-name">y</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ARCHInMean.resids.y" title="Permalink to this definition">¶</a></dt><dd><p>Alternative values to use when computing model residuals</p>
+<dt id="arch.univariate.ARCHInMean.resids.y"><span class="n sig-name">y</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ARCHInMean.resids.y" title="Permalink to this definition">¶</a></dt><dd><p>Alternative values to use when computing model residuals</p>
 </dd>
-<dt id="arch.univariate.ARCHInMean.resids.regressors"><span class="n sig-name">regressors</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ARCHInMean.resids.regressors" title="Permalink to this definition">¶</a></dt><dd><p>Alternative regressor values to use when computing model residuals</p>
+<dt id="arch.univariate.ARCHInMean.resids.regressors"><span class="n sig-name">regressors</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ARCHInMean.resids.regressors" title="Permalink to this definition">¶</a></dt><dd><p>Alternative regressor values to use when computing model residuals</p>
 </dd>
 </dl>
 </dd>
@@ -1395,7 +1395,7 @@ <h1 id="arch-univariate-archinmean-resids">arch.univariate.ARCHInMean.resids<a c
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diff --git a/devel/univariate/generated/generated/arch.univariate.ARCHInMean.simulate.html b/devel/univariate/generated/generated/arch.univariate.ARCHInMean.simulate.html
index b388848b93..1de8f3a975 100644
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@@ -1301,12 +1301,12 @@
 <h1 id="arch-univariate-archinmean-simulate">arch.univariate.ARCHInMean.simulate<a class="headerlink" href="#arch-univariate-archinmean-simulate" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.ARCHInMean.simulate">
-<span class="sig-prename descclassname"><span class="pre">ARCHInMean.</span></span><span class="sig-name descname"><span class="pre">simulate</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCHInMean.simulate.params" title="arch.univariate.ARCHInMean.simulate.params (Python parameter) — Parameters to use when simulating the model.  Parameter order is [mean volatility distribution] where the parameters of the mean model are ordered [constant lag[0] lag[1] ... lag[p] ex[0] ... ex[k-1]] where lag[j] indicates the coefficient on the jth lag in the model and ex[j] is the coefficient on the jth exogenous variable."><span class="n"><span class="pre">params</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCHInMean.simulate.nobs" title="arch.univariate.ARCHInMean.simulate.nobs (Python parameter) — Length of series to simulate"><span class="n"><span class="pre">nobs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCHInMean.simulate.burn" title="arch.univariate.ARCHInMean.simulate.burn (Python parameter) — Number of values to simulate to initialize the model and remove dependence on initial values."><span class="n"><span class="pre">burn</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">500</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCHInMean.simulate.initial_value" title="arch.univariate.ARCHInMean.simulate.initial_value (Python parameter) — Either a scalar value or max(lags) array set of initial values to use when initializing the model.  If omitted, 0.0 is used."><span class="n"><span class="pre">initial_value</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCHInMean.simulate.x" title="arch.univariate.ARCHInMean.simulate.x (Python parameter) — nobs + burn by k array of exogenous variables to include in the simulation."><span class="n"><span class="pre">x</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCHInMean.simulate.initial_value_vol" title="arch.univariate.ARCHInMean.simulate.initial_value_vol (Python parameter) — An array or scalar to use when initializing the volatility process."><span class="n"><span class="pre">initial_value_vol</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a></span></span></span><a class="headerlink" href="#arch.univariate.ARCHInMean.simulate" title="Link to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">ARCHInMean.</span></span><span class="sig-name descname"><span class="pre">simulate</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCHInMean.simulate.params" title="arch.univariate.ARCHInMean.simulate.params (Python parameter) — Parameters to use when simulating the model.  Parameter order is [mean volatility distribution] where the parameters of the mean model are ordered [constant lag[0] lag[1] ... lag[p] ex[0] ... ex[k-1]] where lag[j] indicates the coefficient on the jth lag in the model and ex[j] is the coefficient on the jth exogenous variable."><span class="n"><span class="pre">params</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCHInMean.simulate.nobs" title="arch.univariate.ARCHInMean.simulate.nobs (Python parameter) — Length of series to simulate"><span class="n"><span class="pre">nobs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCHInMean.simulate.burn" title="arch.univariate.ARCHInMean.simulate.burn (Python parameter) — Number of values to simulate to initialize the model and remove dependence on initial values."><span class="n"><span class="pre">burn</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">500</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCHInMean.simulate.initial_value" title="arch.univariate.ARCHInMean.simulate.initial_value (Python parameter) — Either a scalar value or max(lags) array set of initial values to use when initializing the model.  If omitted, 0.0 is used."><span class="n"><span class="pre">initial_value</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCHInMean.simulate.x" title="arch.univariate.ARCHInMean.simulate.x (Python parameter) — nobs + burn by k array of exogenous variables to include in the simulation."><span class="n"><span class="pre">x</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARCHInMean.simulate.initial_value_vol" title="arch.univariate.ARCHInMean.simulate.initial_value_vol (Python parameter) — An array or scalar to use when initializing the volatility process."><span class="n"><span class="pre">initial_value_vol</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><span class="pre">DataFrame</span></a></span></span></span><a class="headerlink" href="#arch.univariate.ARCHInMean.simulate" title="Link to this definition">¶</a></dt>
 <dd><p>Simulates data from a linear regression, AR or HAR models</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.ARCHInMean.simulate-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.ARCHInMean.simulate-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.ARCHInMean.simulate.params"><span class="n sig-name">params</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="p">]</span></span></span><a class="headerlink" href="#arch.univariate.ARCHInMean.simulate.params" title="Permalink to this definition">¶</a></dt><dd><p>Parameters to use when simulating the model.  Parameter order is
+<dt id="arch.univariate.ARCHInMean.simulate.params"><span class="n sig-name">params</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="p">]</span></span></span><a class="headerlink" href="#arch.univariate.ARCHInMean.simulate.params" title="Permalink to this definition">¶</a></dt><dd><p>Parameters to use when simulating the model.  Parameter order is
 [mean volatility distribution] where the parameters of the mean
 model are ordered [constant lag[0] lag[1] … lag[p] ex[0] …
 ex[k-1]] where lag[j] indicates the coefficient on the jth lag in
@@ -1321,7 +1321,7 @@ <h1 id="arch-univariate-archinmean-simulate">arch.univariate.ARCHInMean.simulate
 <dt id="arch.univariate.ARCHInMean.simulate.initial_value"><span class="n sig-name">initial_value</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ARCHInMean.simulate.initial_value" title="Permalink to this definition">¶</a></dt><dd><p>Either a scalar value or <cite>max(lags)</cite> array set of initial values to
 use when initializing the model.  If omitted, 0.0 is used.</p>
 </dd>
-<dt id="arch.univariate.ARCHInMean.simulate.x"><span class="n sig-name">x</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ARCHInMean.simulate.x" title="Permalink to this definition">¶</a></dt><dd><p>nobs + burn by k array of exogenous variables to include in the
+<dt id="arch.univariate.ARCHInMean.simulate.x"><span class="n sig-name">x</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ARCHInMean.simulate.x" title="Permalink to this definition">¶</a></dt><dd><p>nobs + burn by k array of exogenous variables to include in the
 simulation.</p>
 </dd>
 <dt id="arch.univariate.ARCHInMean.simulate.initial_value_vol"><span class="n sig-name">initial_value_vol</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ARCHInMean.simulate.initial_value_vol" title="Permalink to this definition">¶</a></dt><dd><p>An array or scalar to use when initializing the volatility process.</p>
@@ -1334,7 +1334,7 @@ <h1 id="arch-univariate-archinmean-simulate">arch.univariate.ARCHInMean.simulate
 containing the errors used in the simulation</p>
 </dd>
 <dt class="field-odd" id="arch.univariate.ARCHInMean.simulate-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.ARCHInMean.simulate-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><code class="xref py py-class docutils literal notranslate"><span class="pre">pandas.DataFrame</span></code></a></p>
+<dd class="field-odd"><p><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><code class="xref py py-class docutils literal notranslate"><span class="pre">pandas.DataFrame</span></code></a></p>
 </dd>
 </dl>
 <p class="example rubric">Examples</p>
@@ -1474,7 +1474,7 @@ <h1 id="arch-univariate-archinmean-simulate">arch.univariate.ARCHInMean.simulate
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/devel/univariate/generated/generated/arch.univariate.ARCHInMean.starting_values.html b/devel/univariate/generated/generated/arch.univariate.ARCHInMean.starting_values.html
index b2c48b827d..efd24e241f 100644
--- a/devel/univariate/generated/generated/arch.univariate.ARCHInMean.starting_values.html
+++ b/devel/univariate/generated/generated/arch.univariate.ARCHInMean.starting_values.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ARCHInMean.starting_values - arch v6.2.1 (+21)</title>
+        <title>arch.univariate.ARCHInMean.starting_values - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -1318,7 +1318,7 @@ <h1 id="arch-univariate-archinmean-starting-values">arch.univariate.ARCHInMean.s
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/devel/univariate/generated/generated/arch.univariate.ARCHInMean.volatility.html b/devel/univariate/generated/generated/arch.univariate.ARCHInMean.volatility.html
index 472154726a..886d3fde11 100644
--- a/devel/univariate/generated/generated/arch.univariate.ARCHInMean.volatility.html
+++ b/devel/univariate/generated/generated/arch.univariate.ARCHInMean.volatility.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ARCHInMean.volatility - arch v6.2.1 (+21)</title>
+        <title>arch.univariate.ARCHInMean.volatility - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -1270,7 +1270,7 @@ <h1 id="arch-univariate-archinmean-volatility">arch.univariate.ARCHInMean.volati
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/devel/univariate/generated/generated/arch.univariate.ARCHInMean.x.html b/devel/univariate/generated/generated/arch.univariate.ARCHInMean.x.html
index 46c2f88b22..398b1358c6 100644
--- a/devel/univariate/generated/generated/arch.univariate.ARCHInMean.x.html
+++ b/devel/univariate/generated/generated/arch.univariate.ARCHInMean.x.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ARCHInMean.x - arch v6.2.1 (+21)</title>
+        <title>arch.univariate.ARCHInMean.x - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -1151,7 +1151,7 @@
 <h1 id="arch-univariate-archinmean-x">arch.univariate.ARCHInMean.x<a class="headerlink" href="#arch-univariate-archinmean-x" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.ARCHInMean.x">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARCHInMean.</span></span><span class="sig-name descname"><span class="pre">x</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span><a class="headerlink" href="#arch.univariate.ARCHInMean.x" title="Link to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARCHInMean.</span></span><span class="sig-name descname"><span class="pre">x</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span><a class="headerlink" href="#arch.univariate.ARCHInMean.x" title="Link to this definition">¶</a></dt>
 <dd><p>Gets the value of the exogenous regressors in the model</p>
 </dd></dl>
 
@@ -1269,7 +1269,7 @@ <h1 id="arch-univariate-archinmean-x">arch.univariate.ARCHInMean.x<a class="head
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
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diff --git a/devel/univariate/generated/generated/arch.univariate.ARCHInMean.y.html b/devel/univariate/generated/generated/arch.univariate.ARCHInMean.y.html
index 40dd7fcd2c..c5b5f68bee 100644
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@@ -15,7 +15,7 @@
     
   
       
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+    arch v6.2.1 (+23)
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@@ -1151,7 +1151,7 @@
 <h1 id="arch-univariate-archinmean-y">arch.univariate.ARCHInMean.y<a class="headerlink" href="#arch-univariate-archinmean-y" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.ARCHInMean.y">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARCHInMean.</span></span><span class="sig-name descname"><span class="pre">y</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span><a class="headerlink" href="#arch.univariate.ARCHInMean.y" title="Link to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARCHInMean.</span></span><span class="sig-name descname"><span class="pre">y</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span><a class="headerlink" href="#arch.univariate.ARCHInMean.y" title="Link to this definition">¶</a></dt>
 <dd><p>Returns the dependent variable</p>
 </dd></dl>
 
@@ -1269,7 +1269,7 @@ <h1 id="arch-univariate-archinmean-y">arch.univariate.ARCHInMean.y<a class="head
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diff --git a/devel/univariate/generated/generated/arch.univariate.ARX.bounds.html b/devel/univariate/generated/generated/arch.univariate.ARX.bounds.html
index 35311eaf21..a45671088b 100644
--- a/devel/univariate/generated/generated/arch.univariate.ARX.bounds.html
+++ b/devel/univariate/generated/generated/arch.univariate.ARX.bounds.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ARX.bounds - arch v6.2.1 (+21)</title>
+        <title>arch.univariate.ARX.bounds - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
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+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
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@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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@@ -1262,7 +1262,7 @@ <h1 id="arch-univariate-arx-bounds">arch.univariate.ARX.bounds<a class="headerli
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diff --git a/devel/univariate/generated/generated/arch.univariate.ARX.compute_param_cov.html b/devel/univariate/generated/generated/arch.univariate.ARX.compute_param_cov.html
index bbd3004d14..80100d7ef2 100644
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+++ b/devel/univariate/generated/generated/arch.univariate.ARX.compute_param_cov.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ARX.compute_param_cov - arch v6.2.1 (+21)</title>
+        <title>arch.univariate.ARX.compute_param_cov - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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diff --git a/devel/univariate/generated/generated/arch.univariate.ARX.constraints.html b/devel/univariate/generated/generated/arch.univariate.ARX.constraints.html
index 5927b1c9ed..e63cc85a1a 100644
--- a/devel/univariate/generated/generated/arch.univariate.ARX.constraints.html
+++ b/devel/univariate/generated/generated/arch.univariate.ARX.constraints.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ARX.constraints - arch v6.2.1 (+21)</title>
+        <title>arch.univariate.ARX.constraints - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
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-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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diff --git a/devel/univariate/generated/generated/arch.univariate.ARX.distribution.html b/devel/univariate/generated/generated/arch.univariate.ARX.distribution.html
index d5596d7339..5c748976aa 100644
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+++ b/devel/univariate/generated/generated/arch.univariate.ARX.distribution.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ARX.distribution - arch v6.2.1 (+21)</title>
+        <title>arch.univariate.ARX.distribution - arch v6.2.1 (+23)</title>
       
     
 
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@@ -102,7 +102,7 @@
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+            arch v6.2.1 (+23)
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@@ -189,10 +189,10 @@
 
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diff --git a/devel/univariate/generated/generated/arch.univariate.ARX.fit.html b/devel/univariate/generated/generated/arch.univariate.ARX.fit.html
index 88db11fa51..056670200d 100644
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-        <title>arch.univariate.ARX.fit - arch v6.2.1 (+21)</title>
+        <title>arch.univariate.ARX.fit - arch v6.2.1 (+23)</title>
       
     
 
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@@ -1302,7 +1302,7 @@
 <h1 id="arch-univariate-arx-fit">arch.univariate.ARX.fit<a class="headerlink" href="#arch-univariate-arx-fit" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.ARX.fit">
-<span class="sig-prename descclassname"><span class="pre">ARX.</span></span><span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARX.fit.update_freq" title="arch.univariate.ARX.fit.update_freq (Python parameter) — Frequency of iteration updates.  Output is generated every update_freq iterations. Set to 0 to disable iterative output."><span class="n"><span class="pre">update_freq</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARX.fit.disp" title="arch.univariate.ARX.fit.disp (Python parameter) — Either 'final' to print optimization result or 'off' to display nothing. If using a boolean, False is &quot;off&quot; and True is &quot;final&quot;"><span class="n"><span class="pre">disp</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'off'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'final'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;final&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARX.fit.starting_values" title="arch.univariate.ARX.fit.starting_values (Python parameter) — Array of starting values to use.  If not provided, starting values are constructed by the model components."><span class="n"><span class="pre">starting_values</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARX.fit.cov_type" title="arch.univariate.ARX.fit.cov_type (Python parameter) — Estimation method of parameter covariance.  Supported options are 'robust', which does not assume the Information Matrix Equality holds and 'classic' which does.  In the ARCH literature, 'robust' corresponds to Bollerslev-Wooldridge covariance estimator."><span class="n"><span class="pre">cov_type</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'robust'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'classic'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;robust&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARX.fit.show_warning" title="arch.univariate.ARX.fit.show_warning (Python parameter) — Flag indicating whether convergence warnings should be shown."><span class="n"><span class="pre">show_warning</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARX.fit.first_obs" title="arch.univariate.ARX.fit.first_obs (Python parameter) — First observation to use when estimating model"><span class="n"><span class="pre">first_obs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.12)"><span class="pre">datetime</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)"><span class="pre">datetime64</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.1)"><span class="pre">Timestamp</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARX.fit.last_obs" title="arch.univariate.ARX.fit.last_obs (Python parameter) — Last observation to use when estimating model"><span class="n"><span class="pre">last_obs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.12)"><span class="pre">datetime</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)"><span class="pre">datetime64</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.1)"><span class="pre">Timestamp</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARX.fit.tol" title="arch.univariate.ARX.fit.tol (Python parameter) — Tolerance for termination."><span class="n"><span class="pre">tol</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARX.fit.options" title="arch.univariate.ARX.fit.options (Python parameter) — Options to pass to scipy.optimize.minimize.  Valid entries include 'ftol', 'eps', 'disp', and 'maxiter'."><span class="n"><span class="pre">options</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.12)"><span class="pre">Any</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARX.fit.backcast" title="arch.univariate.ARX.fit.backcast (Python parameter) — Value to use as backcast. Should be measure \sigma^2_0 since model-specific non-linear transformations are applied to value before computing the variance recursions."><span class="n"><span class="pre">backcast</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference internal" href="../arch.univariate.base.ARCHModelResult.html#arch.univariate.base.ARCHModelResult" title="arch.univariate.base.ARCHModelResult (Python class) — Results from estimation of an ARCHModel model"><span class="pre">ARCHModelResult</span></a></span></span></span><a class="headerlink" href="#arch.univariate.ARX.fit" title="Link to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">ARX.</span></span><span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARX.fit.update_freq" title="arch.univariate.ARX.fit.update_freq (Python parameter) — Frequency of iteration updates.  Output is generated every update_freq iterations. Set to 0 to disable iterative output."><span class="n"><span class="pre">update_freq</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARX.fit.disp" title="arch.univariate.ARX.fit.disp (Python parameter) — Either 'final' to print optimization result or 'off' to display nothing. If using a boolean, False is &quot;off&quot; and True is &quot;final&quot;"><span class="n"><span class="pre">disp</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'off'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'final'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;final&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARX.fit.starting_values" title="arch.univariate.ARX.fit.starting_values (Python parameter) — Array of starting values to use.  If not provided, starting values are constructed by the model components."><span class="n"><span class="pre">starting_values</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARX.fit.cov_type" title="arch.univariate.ARX.fit.cov_type (Python parameter) — Estimation method of parameter covariance.  Supported options are 'robust', which does not assume the Information Matrix Equality holds and 'classic' which does.  In the ARCH literature, 'robust' corresponds to Bollerslev-Wooldridge covariance estimator."><span class="n"><span class="pre">cov_type</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'robust'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'classic'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;robust&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARX.fit.show_warning" title="arch.univariate.ARX.fit.show_warning (Python parameter) — Flag indicating whether convergence warnings should be shown."><span class="n"><span class="pre">show_warning</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARX.fit.first_obs" title="arch.univariate.ARX.fit.first_obs (Python parameter) — First observation to use when estimating model"><span class="n"><span class="pre">first_obs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.12)"><span class="pre">datetime</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)"><span class="pre">datetime64</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.4)"><span class="pre">Timestamp</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARX.fit.last_obs" title="arch.univariate.ARX.fit.last_obs (Python parameter) — Last observation to use when estimating model"><span class="n"><span class="pre">last_obs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.12)"><span class="pre">datetime</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)"><span class="pre">datetime64</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.4)"><span class="pre">Timestamp</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARX.fit.tol" title="arch.univariate.ARX.fit.tol (Python parameter) — Tolerance for termination."><span class="n"><span class="pre">tol</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARX.fit.options" title="arch.univariate.ARX.fit.options (Python parameter) — Options to pass to scipy.optimize.minimize.  Valid entries include 'ftol', 'eps', 'disp', and 'maxiter'."><span class="n"><span class="pre">options</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.12)"><span class="pre">Any</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARX.fit.backcast" title="arch.univariate.ARX.fit.backcast (Python parameter) — Value to use as backcast. Should be measure \sigma^2_0 since model-specific non-linear transformations are applied to value before computing the variance recursions."><span class="n"><span class="pre">backcast</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference internal" href="../arch.univariate.base.ARCHModelResult.html#arch.univariate.base.ARCHModelResult" title="arch.univariate.base.ARCHModelResult (Python class) — Results from estimation of an ARCHModel model"><span class="pre">ARCHModelResult</span></a></span></span></span><a class="headerlink" href="#arch.univariate.ARX.fit" title="Link to this definition">¶</a></dt>
 <dd><p>Estimate model parameters</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.ARX.fit-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.ARX.fit-parameters" title="Permalink to this headline">¶</a></dt>
@@ -1313,7 +1313,7 @@ <h1 id="arch-univariate-arx-fit">arch.univariate.ARX.fit<a class="headerlink" hr
 <dt id="arch.univariate.ARX.fit.disp"><span class="n sig-name">disp</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)">bool</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'off'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'final'</span></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;final&#39;</span></code><a class="headerlink" href="#arch.univariate.ARX.fit.disp" title="Permalink to this definition">¶</a></dt><dd><p>Either ‘final’ to print optimization result or ‘off’ to display
 nothing. If using a boolean, False is “off” and True is “final”</p>
 </dd>
-<dt id="arch.univariate.ARX.fit.starting_values"><span class="n sig-name">starting_values</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ARX.fit.starting_values" title="Permalink to this definition">¶</a></dt><dd><p>Array of starting values to use.  If not provided, starting values
+<dt id="arch.univariate.ARX.fit.starting_values"><span class="n sig-name">starting_values</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ARX.fit.starting_values" title="Permalink to this definition">¶</a></dt><dd><p>Array of starting values to use.  If not provided, starting values
 are constructed by the model components.</p>
 </dd>
 <dt id="arch.univariate.ARX.fit.cov_type"><span class="n sig-name">cov_type</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><span class="s">'robust'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'classic'</span></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;robust&#39;</span></code><a class="headerlink" href="#arch.univariate.ARX.fit.cov_type" title="Permalink to this definition">¶</a></dt><dd><p>Estimation method of parameter covariance.  Supported options are
@@ -1323,9 +1323,9 @@ <h1 id="arch-univariate-arx-fit">arch.univariate.ARX.fit<a class="headerlink" hr
 </dd>
 <dt id="arch.univariate.ARX.fit.show_warning"><span class="n sig-name">show_warning</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)">bool</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code><a class="headerlink" href="#arch.univariate.ARX.fit.show_warning" title="Permalink to this definition">¶</a></dt><dd><p>Flag indicating whether convergence warnings should be shown.</p>
 </dd>
-<dt id="arch.univariate.ARX.fit.first_obs"><span class="n sig-name">first_obs</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)">str</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.12)">datetime</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)">datetime64</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.1)">Timestamp</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ARX.fit.first_obs" title="Permalink to this definition">¶</a></dt><dd><p>First observation to use when estimating model</p>
+<dt id="arch.univariate.ARX.fit.first_obs"><span class="n sig-name">first_obs</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)">str</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.12)">datetime</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)">datetime64</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.4)">Timestamp</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ARX.fit.first_obs" title="Permalink to this definition">¶</a></dt><dd><p>First observation to use when estimating model</p>
 </dd>
-<dt id="arch.univariate.ARX.fit.last_obs"><span class="n sig-name">last_obs</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)">str</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.12)">datetime</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)">datetime64</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.1)">Timestamp</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ARX.fit.last_obs" title="Permalink to this definition">¶</a></dt><dd><p>Last observation to use when estimating model</p>
+<dt id="arch.univariate.ARX.fit.last_obs"><span class="n sig-name">last_obs</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)">str</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.12)">datetime</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)">datetime64</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.4)">Timestamp</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ARX.fit.last_obs" title="Permalink to this definition">¶</a></dt><dd><p>Last observation to use when estimating model</p>
 </dd>
 <dt id="arch.univariate.ARX.fit.tol"><span class="n sig-name">tol</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ARX.fit.tol" title="Permalink to this definition">¶</a></dt><dd><p>Tolerance for termination.</p>
 </dd>
@@ -1465,7 +1465,7 @@ <h1 id="arch-univariate-arx-fit">arch.univariate.ARX.fit<a class="headerlink" hr
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@@ -1204,18 +1204,18 @@
 <h1 id="arch-univariate-arx-fix">arch.univariate.ARX.fix<a class="headerlink" href="#arch-univariate-arx-fix" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.ARX.fix">
-<span class="sig-prename descclassname"><span class="pre">ARX.</span></span><span class="sig-name descname"><span class="pre">fix</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARX.fix.params" title="arch.univariate.ARX.fix.params (Python parameter) — User specified parameters to use when generating the result. Must have the correct number of parameters for a given choice of mean model, volatility model and distribution."><span class="n"><span class="pre">params</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARX.fix.first_obs" title="arch.univariate.ARX.fix.first_obs (Python parameter) — First observation to use when fixing model"><span class="n"><span class="pre">first_obs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.12)"><span class="pre">datetime</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)"><span class="pre">datetime64</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.1)"><span class="pre">Timestamp</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARX.fix.last_obs" title="arch.univariate.ARX.fix.last_obs (Python parameter) — Last observation to use when fixing model"><span class="n"><span class="pre">last_obs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.12)"><span class="pre">datetime</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)"><span class="pre">datetime64</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.1)"><span class="pre">Timestamp</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference internal" href="../arch.univariate.base.ARCHModelFixedResult.html#arch.univariate.base.ARCHModelFixedResult" title="arch.univariate.base.ARCHModelFixedResult (Python class) — Results for fixed parameters for an ARCHModel model"><span class="pre">ARCHModelFixedResult</span></a></span></span></span><a class="headerlink" href="#arch.univariate.ARX.fix" title="Link to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">ARX.</span></span><span class="sig-name descname"><span class="pre">fix</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARX.fix.params" title="arch.univariate.ARX.fix.params (Python parameter) — User specified parameters to use when generating the result. Must have the correct number of parameters for a given choice of mean model, volatility model and distribution."><span class="n"><span class="pre">params</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARX.fix.first_obs" title="arch.univariate.ARX.fix.first_obs (Python parameter) — First observation to use when fixing model"><span class="n"><span class="pre">first_obs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.12)"><span class="pre">datetime</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)"><span class="pre">datetime64</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.4)"><span class="pre">Timestamp</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARX.fix.last_obs" title="arch.univariate.ARX.fix.last_obs (Python parameter) — Last observation to use when fixing model"><span class="n"><span class="pre">last_obs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.12)"><span class="pre">datetime</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)"><span class="pre">datetime64</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.4)"><span class="pre">Timestamp</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference internal" href="../arch.univariate.base.ARCHModelFixedResult.html#arch.univariate.base.ARCHModelFixedResult" title="arch.univariate.base.ARCHModelFixedResult (Python class) — Results for fixed parameters for an ARCHModel model"><span class="pre">ARCHModelFixedResult</span></a></span></span></span><a class="headerlink" href="#arch.univariate.ARX.fix" title="Link to this definition">¶</a></dt>
 <dd><p>Allows an ARCHModelFixedResult to be constructed from fixed parameters.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.ARX.fix-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.ARX.fix-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.ARX.fix.params"><span class="n sig-name">params</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.univariate.ARX.fix.params" title="Permalink to this definition">¶</a></dt><dd><p>User specified parameters to use when generating the result. Must
+<dt id="arch.univariate.ARX.fix.params"><span class="n sig-name">params</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a></span></span><a class="headerlink" href="#arch.univariate.ARX.fix.params" title="Permalink to this definition">¶</a></dt><dd><p>User specified parameters to use when generating the result. Must
 have the correct number of parameters for a given choice of mean
 model, volatility model and distribution.</p>
 </dd>
-<dt id="arch.univariate.ARX.fix.first_obs"><span class="n sig-name">first_obs</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)">str</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.12)">datetime</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)">datetime64</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.1)">Timestamp</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ARX.fix.first_obs" title="Permalink to this definition">¶</a></dt><dd><p>First observation to use when fixing model</p>
+<dt id="arch.univariate.ARX.fix.first_obs"><span class="n sig-name">first_obs</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)">str</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.12)">datetime</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)">datetime64</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.4)">Timestamp</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ARX.fix.first_obs" title="Permalink to this definition">¶</a></dt><dd><p>First observation to use when fixing model</p>
 </dd>
-<dt id="arch.univariate.ARX.fix.last_obs"><span class="n sig-name">last_obs</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)">str</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.12)">datetime</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)">datetime64</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.1)">Timestamp</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ARX.fix.last_obs" title="Permalink to this definition">¶</a></dt><dd><p>Last observation to use when fixing model</p>
+<dt id="arch.univariate.ARX.fix.last_obs"><span class="n sig-name">last_obs</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)">str</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.12)">datetime</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)">datetime64</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.4)">Timestamp</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ARX.fix.last_obs" title="Permalink to this definition">¶</a></dt><dd><p>Last observation to use when fixing model</p>
 </dd>
 </dl>
 </dd>
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 <h1 id="arch-univariate-arx-forecast">arch.univariate.ARX.forecast<a class="headerlink" href="#arch-univariate-arx-forecast" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.ARX.forecast">
-<span class="sig-prename descclassname"><span class="pre">ARX.</span></span><span class="sig-name descname"><span class="pre">forecast</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARX.forecast.params" title="arch.univariate.ARX.forecast.params (Python parameter) — Parameters required to forecast. Must be identical in shape to the parameters computed by fitting the model."><span class="n"><span class="pre">params</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARX.forecast.horizon" title="arch.univariate.ARX.forecast.horizon (Python parameter) — Number of steps to forecast"><span class="n"><span class="pre">horizon</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARX.forecast.start" title="arch.univariate.ARX.forecast.start (Python parameter) — An integer, datetime or str indicating the first observation to produce the forecast for.  Datetimes can only be used with pandas inputs that have a datetime index. Strings must be convertible to a date time, such as in '1945-01-01'."><span class="n"><span class="pre">start</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.12)"><span class="pre">datetime</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)"><span class="pre">datetime64</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.1)"><span class="pre">Timestamp</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARX.forecast.align" title="arch.univariate.ARX.forecast.align (Python parameter) — Either 'origin' or 'target'.  When set of 'origin', the t-th row of forecasts contains the forecasts for t+1, t+2, ..., t+h. When set to 'target', the t-th row contains the 1-step ahead forecast from time t-1, the 2 step from time t-2, ..., and the h-step from time t-h.  'target' simplified computing forecast errors since the realization and h-step forecast are aligned."><span class="n"><span class="pre">align</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'origin'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'target'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;origin&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARX.forecast.method" title="arch.univariate.ARX.forecast.method (Python parameter) — Method to use when producing the forecast. The default is analytic. The method only affects the variance forecast generation.  Not all volatility models support all methods. In particular, volatility models that do not evolve in squares such as EGARCH or TARCH do not support the 'analytic' method for horizons &gt; 1."><span class="n"><span class="pre">method</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'analytic'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'simulation'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'bootstrap'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;analytic&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARX.forecast.simulations" title="arch.univariate.ARX.forecast.simulations (Python parameter) — Number of simulations to run when computing the forecast using either simulation or bootstrap."><span class="n"><span class="pre">simulations</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARX.forecast.rng" title="arch.univariate.ARX.forecast.rng (Python parameter) — Custom random number generator to use in simulation-based forecasts. Must produce random samples using the syntax rng(size) where size the 2-element tuple (simulations, horizon)."><span class="n"><span class="pre">rng</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.12)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARX.forecast.random_state" title="arch.univariate.ARX.forecast.random_state (Python parameter) — NumPy RandomState instance to use when method is 'bootstrap'"><span class="n"><span class="pre">random_state</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.26)"><span class="pre">RandomState</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARX.forecast.reindex" title="arch.univariate.ARX.forecast.reindex (Python parameter) — Whether to reindex the forecasts to have the same dimension as the series being forecast. Prior to 4.18 this was the default. As of 4.19 this is now optional. If not provided, a warning is raised about the future change in the default which will occur after September 2021."><span class="n"><span class="pre">reindex</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARX.forecast.x" title="arch.univariate.ARX.forecast.x (Python parameter) — Values to use for exogenous regressors if any are included in the model. Three formats are accepted:"><span class="n"><span class="pre">x</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Hashable" title="(in Python v3.12)"><span class="pre">Hashable</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference internal" href="../arch.univariate.base.ARCHModelForecast.html#arch.univariate.base.ARCHModelForecast" title="arch.univariate.base.ARCHModelForecast (Python class) — Container for forecasts from an ARCH Model"><span class="pre">ARCHModelForecast</span></a></span></span></span><a class="headerlink" href="#arch.univariate.ARX.forecast" title="Link to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">ARX.</span></span><span class="sig-name descname"><span class="pre">forecast</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARX.forecast.params" title="arch.univariate.ARX.forecast.params (Python parameter) — Parameters required to forecast. Must be identical in shape to the parameters computed by fitting the model."><span class="n"><span class="pre">params</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARX.forecast.horizon" title="arch.univariate.ARX.forecast.horizon (Python parameter) — Number of steps to forecast"><span class="n"><span class="pre">horizon</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARX.forecast.start" title="arch.univariate.ARX.forecast.start (Python parameter) — An integer, datetime or str indicating the first observation to produce the forecast for.  Datetimes can only be used with pandas inputs that have a datetime index. Strings must be convertible to a date time, such as in '1945-01-01'."><span class="n"><span class="pre">start</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.12)"><span class="pre">datetime</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)"><span class="pre">datetime64</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.4)"><span class="pre">Timestamp</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARX.forecast.align" title="arch.univariate.ARX.forecast.align (Python parameter) — Either 'origin' or 'target'.  When set of 'origin', the t-th row of forecasts contains the forecasts for t+1, t+2, ..., t+h. When set to 'target', the t-th row contains the 1-step ahead forecast from time t-1, the 2 step from time t-2, ..., and the h-step from time t-h.  'target' simplified computing forecast errors since the realization and h-step forecast are aligned."><span class="n"><span class="pre">align</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'origin'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'target'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;origin&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARX.forecast.method" title="arch.univariate.ARX.forecast.method (Python parameter) — Method to use when producing the forecast. The default is analytic. The method only affects the variance forecast generation.  Not all volatility models support all methods. In particular, volatility models that do not evolve in squares such as EGARCH or TARCH do not support the 'analytic' method for horizons &gt; 1."><span class="n"><span class="pre">method</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'analytic'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'simulation'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'bootstrap'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;analytic&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARX.forecast.simulations" title="arch.univariate.ARX.forecast.simulations (Python parameter) — Number of simulations to run when computing the forecast using either simulation or bootstrap."><span class="n"><span class="pre">simulations</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARX.forecast.rng" title="arch.univariate.ARX.forecast.rng (Python parameter) — Custom random number generator to use in simulation-based forecasts. Must produce random samples using the syntax rng(size) where size the 2-element tuple (simulations, horizon)."><span class="n"><span class="pre">rng</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.12)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARX.forecast.random_state" title="arch.univariate.ARX.forecast.random_state (Python parameter) — NumPy RandomState instance to use when method is 'bootstrap'"><span class="n"><span class="pre">random_state</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.26)"><span class="pre">RandomState</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARX.forecast.reindex" title="arch.univariate.ARX.forecast.reindex (Python parameter) — Whether to reindex the forecasts to have the same dimension as the series being forecast. Prior to 4.18 this was the default. As of 4.19 this is now optional. If not provided, a warning is raised about the future change in the default which will occur after September 2021."><span class="n"><span class="pre">reindex</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARX.forecast.x" title="arch.univariate.ARX.forecast.x (Python parameter) — Values to use for exogenous regressors if any are included in the model. Three formats are accepted:"><span class="n"><span class="pre">x</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Hashable" title="(in Python v3.12)"><span class="pre">Hashable</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference internal" href="../arch.univariate.base.ARCHModelForecast.html#arch.univariate.base.ARCHModelForecast" title="arch.univariate.base.ARCHModelForecast (Python class) — Container for forecasts from an ARCH Model"><span class="pre">ARCHModelForecast</span></a></span></span></span><a class="headerlink" href="#arch.univariate.ARX.forecast" title="Link to this definition">¶</a></dt>
 <dd><p>Construct forecasts from estimated model</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.ARX.forecast-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.ARX.forecast-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.ARX.forecast.params"><span class="n sig-name">params</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.univariate.ARX.forecast.params" title="Permalink to this definition">¶</a></dt><dd><p>Parameters required to forecast. Must be identical in
+<dt id="arch.univariate.ARX.forecast.params"><span class="n sig-name">params</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a></span></span><a class="headerlink" href="#arch.univariate.ARX.forecast.params" title="Permalink to this definition">¶</a></dt><dd><p>Parameters required to forecast. Must be identical in
 shape to the parameters computed by fitting the model.</p>
 </dd>
 <dt id="arch.univariate.ARX.forecast.horizon"><span class="n sig-name">horizon</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code><a class="headerlink" href="#arch.univariate.ARX.forecast.horizon" title="Permalink to this definition">¶</a></dt><dd><p>Number of steps to forecast</p>
 </dd>
-<dt id="arch.univariate.ARX.forecast.start"><span class="n sig-name">start</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)">str</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.12)">datetime</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)">datetime64</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.1)">Timestamp</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ARX.forecast.start" title="Permalink to this definition">¶</a></dt><dd><p>An integer, datetime or str indicating the first observation to
+<dt id="arch.univariate.ARX.forecast.start"><span class="n sig-name">start</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)">str</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.12)">datetime</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)">datetime64</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.4)">Timestamp</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ARX.forecast.start" title="Permalink to this definition">¶</a></dt><dd><p>An integer, datetime or str indicating the first observation to
 produce the forecast for.  Datetimes can only be used with pandas
 inputs that have a datetime index. Strings must be convertible
 to a date time, such as in ‘1945-01-01’.</p>
@@ -1348,7 +1348,7 @@ <h1 id="arch-univariate-arx-forecast">arch.univariate.ARX.forecast<a class="head
 </div>
 </p>
 </dd>
-<dt id="arch.univariate.ARX.forecast.x"><span class="n sig-name">x</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)">dict</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Hashable" title="(in Python v3.12)">Hashable</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ARX.forecast.x" title="Permalink to this definition">¶</a></dt><dd><p><p>Values to use for exogenous regressors if any are included in the
+<dt id="arch.univariate.ARX.forecast.x"><span class="n sig-name">x</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)">dict</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Hashable" title="(in Python v3.12)">Hashable</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ARX.forecast.x" title="Permalink to this definition">¶</a></dt><dd><p><p>Values to use for exogenous regressors if any are included in the
 model. Three formats are accepted:</p>
 <ul class="simple">
 <li><p>2-d array-like: This format can be used when there is a single
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index 1639ff039b..d9eb07ea06 100644
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+++ b/devel/univariate/generated/generated/arch.univariate.ARX.name.html
@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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+            arch v6.2.1 (+23)
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@@ -189,10 +189,10 @@
 
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+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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diff --git a/devel/univariate/generated/generated/arch.univariate.ARX.num_params.html b/devel/univariate/generated/generated/arch.univariate.ARX.num_params.html
index 59aa0ab377..4f33fd1f2d 100644
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+++ b/devel/univariate/generated/generated/arch.univariate.ARX.num_params.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ARX.num_params - arch v6.2.1 (+21)</title>
+        <title>arch.univariate.ARX.num_params - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
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@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
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           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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@@ -189,10 +189,10 @@
 
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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diff --git a/devel/univariate/generated/generated/arch.univariate.ARX.parameter_names.html b/devel/univariate/generated/generated/arch.univariate.ARX.parameter_names.html
index b99bad211c..4127b1e45e 100644
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+++ b/devel/univariate/generated/generated/arch.univariate.ARX.parameter_names.html
@@ -15,7 +15,7 @@
     
   
       
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+            arch v6.2.1 (+23)
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@@ -189,10 +189,10 @@
 
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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@@ -1262,7 +1262,7 @@ <h1 id="arch-univariate-arx-parameter-names">arch.univariate.ARX.parameter_names
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diff --git a/devel/univariate/generated/generated/arch.univariate.ARX.resids.html b/devel/univariate/generated/generated/arch.univariate.ARX.resids.html
index a061c01891..71372a0173 100644
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+++ b/devel/univariate/generated/generated/arch.univariate.ARX.resids.html
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-        <title>arch.univariate.ARX.resids - arch v6.2.1 (+21)</title>
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@@ -92,7 +92,7 @@
 
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-            arch v6.2.1 (+21)
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-    arch v6.2.1 (+21)
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 <h1 id="arch-univariate-arx-resids">arch.univariate.ARX.resids<a class="headerlink" href="#arch-univariate-arx-resids" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.ARX.resids">
-<span class="sig-prename descclassname"><span class="pre">ARX.</span></span><span class="sig-name descname"><span class="pre">resids</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARX.resids.params" title="arch.univariate.ARX.resids.params (Python parameter) — Model parameters"><span class="n"><span class="pre">params</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARX.resids.y" title="arch.univariate.ARX.resids.y (Python parameter) — Alternative values to use when computing model residuals"><span class="n"><span class="pre">y</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARX.resids.regressors" title="arch.univariate.ARX.resids.regressors (Python parameter) — Alternative regressor values to use when computing model residuals"><span class="n"><span class="pre">regressors</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></span><a class="headerlink" href="#arch.univariate.ARX.resids" title="Link to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">ARX.</span></span><span class="sig-name descname"><span class="pre">resids</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARX.resids.params" title="arch.univariate.ARX.resids.params (Python parameter) — Model parameters"><span class="n"><span class="pre">params</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARX.resids.y" title="arch.univariate.ARX.resids.y (Python parameter) — Alternative values to use when computing model residuals"><span class="n"><span class="pre">y</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARX.resids.regressors" title="arch.univariate.ARX.resids.regressors (Python parameter) — Alternative regressor values to use when computing model residuals"><span class="n"><span class="pre">regressors</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a></span></span></span><a class="headerlink" href="#arch.univariate.ARX.resids" title="Link to this definition">¶</a></dt>
 <dd><p>Compute model residuals</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.ARX.resids-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.ARX.resids-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
 <dt id="arch.univariate.ARX.resids.params"><span class="n sig-name">params</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.ARX.resids.params" title="Permalink to this definition">¶</a></dt><dd><p>Model parameters</p>
 </dd>
-<dt id="arch.univariate.ARX.resids.y"><span class="n sig-name">y</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ARX.resids.y" title="Permalink to this definition">¶</a></dt><dd><p>Alternative values to use when computing model residuals</p>
+<dt id="arch.univariate.ARX.resids.y"><span class="n sig-name">y</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ARX.resids.y" title="Permalink to this definition">¶</a></dt><dd><p>Alternative values to use when computing model residuals</p>
 </dd>
-<dt id="arch.univariate.ARX.resids.regressors"><span class="n sig-name">regressors</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ARX.resids.regressors" title="Permalink to this definition">¶</a></dt><dd><p>Alternative regressor values to use when computing model residuals</p>
+<dt id="arch.univariate.ARX.resids.regressors"><span class="n sig-name">regressors</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ARX.resids.regressors" title="Permalink to this definition">¶</a></dt><dd><p>Alternative regressor values to use when computing model residuals</p>
 </dd>
 </dl>
 </dd>
@@ -1340,7 +1340,7 @@ <h1 id="arch-univariate-arx-resids">arch.univariate.ARX.resids<a class="headerli
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diff --git a/devel/univariate/generated/generated/arch.univariate.ARX.simulate.html b/devel/univariate/generated/generated/arch.univariate.ARX.simulate.html
index f974c1eef7..f0818a06fa 100644
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@@ -15,7 +15,7 @@
     
   
       
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@@ -102,7 +102,7 @@
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 <h1 id="arch-univariate-arx-simulate">arch.univariate.ARX.simulate<a class="headerlink" href="#arch-univariate-arx-simulate" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.ARX.simulate">
-<span class="sig-prename descclassname"><span class="pre">ARX.</span></span><span class="sig-name descname"><span class="pre">simulate</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARX.simulate.params" title="arch.univariate.ARX.simulate.params (Python parameter) — Parameters to use when simulating the model.  Parameter order is [mean volatility distribution] where the parameters of the mean model are ordered [constant lag[0] lag[1] ... lag[p] ex[0] ... ex[k-1]] where lag[j] indicates the coefficient on the jth lag in the model and ex[j] is the coefficient on the jth exogenous variable."><span class="n"><span class="pre">params</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARX.simulate.nobs" title="arch.univariate.ARX.simulate.nobs (Python parameter) — Length of series to simulate"><span class="n"><span class="pre">nobs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARX.simulate.burn" title="arch.univariate.ARX.simulate.burn (Python parameter) — Number of values to simulate to initialize the model and remove dependence on initial values."><span class="n"><span class="pre">burn</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">500</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARX.simulate.initial_value" title="arch.univariate.ARX.simulate.initial_value (Python parameter) — Either a scalar value or max(lags) array set of initial values to use when initializing the model.  If omitted, 0.0 is used."><span class="n"><span class="pre">initial_value</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARX.simulate.x" title="arch.univariate.ARX.simulate.x (Python parameter) — nobs + burn by k array of exogenous variables to include in the simulation."><span class="n"><span class="pre">x</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARX.simulate.initial_value_vol" title="arch.univariate.ARX.simulate.initial_value_vol (Python parameter) — An array or scalar to use when initializing the volatility process."><span class="n"><span class="pre">initial_value_vol</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a></span></span></span><a class="headerlink" href="#arch.univariate.ARX.simulate" title="Link to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">ARX.</span></span><span class="sig-name descname"><span class="pre">simulate</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARX.simulate.params" title="arch.univariate.ARX.simulate.params (Python parameter) — Parameters to use when simulating the model.  Parameter order is [mean volatility distribution] where the parameters of the mean model are ordered [constant lag[0] lag[1] ... lag[p] ex[0] ... ex[k-1]] where lag[j] indicates the coefficient on the jth lag in the model and ex[j] is the coefficient on the jth exogenous variable."><span class="n"><span class="pre">params</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARX.simulate.nobs" title="arch.univariate.ARX.simulate.nobs (Python parameter) — Length of series to simulate"><span class="n"><span class="pre">nobs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARX.simulate.burn" title="arch.univariate.ARX.simulate.burn (Python parameter) — Number of values to simulate to initialize the model and remove dependence on initial values."><span class="n"><span class="pre">burn</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">500</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARX.simulate.initial_value" title="arch.univariate.ARX.simulate.initial_value (Python parameter) — Either a scalar value or max(lags) array set of initial values to use when initializing the model.  If omitted, 0.0 is used."><span class="n"><span class="pre">initial_value</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARX.simulate.x" title="arch.univariate.ARX.simulate.x (Python parameter) — nobs + burn by k array of exogenous variables to include in the simulation."><span class="n"><span class="pre">x</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ARX.simulate.initial_value_vol" title="arch.univariate.ARX.simulate.initial_value_vol (Python parameter) — An array or scalar to use when initializing the volatility process."><span class="n"><span class="pre">initial_value_vol</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><span class="pre">DataFrame</span></a></span></span></span><a class="headerlink" href="#arch.univariate.ARX.simulate" title="Link to this definition">¶</a></dt>
 <dd><p>Simulates data from a linear regression, AR or HAR models</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.ARX.simulate-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.ARX.simulate-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.ARX.simulate.params"><span class="n sig-name">params</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="p">]</span></span></span><a class="headerlink" href="#arch.univariate.ARX.simulate.params" title="Permalink to this definition">¶</a></dt><dd><p>Parameters to use when simulating the model.  Parameter order is
+<dt id="arch.univariate.ARX.simulate.params"><span class="n sig-name">params</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="p">]</span></span></span><a class="headerlink" href="#arch.univariate.ARX.simulate.params" title="Permalink to this definition">¶</a></dt><dd><p>Parameters to use when simulating the model.  Parameter order is
 [mean volatility distribution] where the parameters of the mean
 model are ordered [constant lag[0] lag[1] … lag[p] ex[0] …
 ex[k-1]] where lag[j] indicates the coefficient on the jth lag in
@@ -1266,7 +1266,7 @@ <h1 id="arch-univariate-arx-simulate">arch.univariate.ARX.simulate<a class="head
 <dt id="arch.univariate.ARX.simulate.initial_value"><span class="n sig-name">initial_value</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ARX.simulate.initial_value" title="Permalink to this definition">¶</a></dt><dd><p>Either a scalar value or <cite>max(lags)</cite> array set of initial values to
 use when initializing the model.  If omitted, 0.0 is used.</p>
 </dd>
-<dt id="arch.univariate.ARX.simulate.x"><span class="n sig-name">x</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ARX.simulate.x" title="Permalink to this definition">¶</a></dt><dd><p>nobs + burn by k array of exogenous variables to include in the
+<dt id="arch.univariate.ARX.simulate.x"><span class="n sig-name">x</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ARX.simulate.x" title="Permalink to this definition">¶</a></dt><dd><p>nobs + burn by k array of exogenous variables to include in the
 simulation.</p>
 </dd>
 <dt id="arch.univariate.ARX.simulate.initial_value_vol"><span class="n sig-name">initial_value_vol</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ARX.simulate.initial_value_vol" title="Permalink to this definition">¶</a></dt><dd><p>An array or scalar to use when initializing the volatility process.</p>
@@ -1279,7 +1279,7 @@ <h1 id="arch-univariate-arx-simulate">arch.univariate.ARX.simulate<a class="head
 containing the errors used in the simulation</p>
 </dd>
 <dt class="field-odd" id="arch.univariate.ARX.simulate-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.ARX.simulate-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><code class="xref py py-class docutils literal notranslate"><span class="pre">pandas.DataFrame</span></code></a></p>
+<dd class="field-odd"><p><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><code class="xref py py-class docutils literal notranslate"><span class="pre">pandas.DataFrame</span></code></a></p>
 </dd>
 </dl>
 <p class="example rubric">Examples</p>
@@ -1419,7 +1419,7 @@ <h1 id="arch-univariate-arx-simulate">arch.univariate.ARX.simulate<a class="head
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/devel/univariate/generated/generated/arch.univariate.ARX.starting_values.html b/devel/univariate/generated/generated/arch.univariate.ARX.starting_values.html
index 54b38b26a5..1d13d52fc4 100644
--- a/devel/univariate/generated/generated/arch.univariate.ARX.starting_values.html
+++ b/devel/univariate/generated/generated/arch.univariate.ARX.starting_values.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ARX.starting_values - arch v6.2.1 (+21)</title>
+        <title>arch.univariate.ARX.starting_values - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -1263,7 +1263,7 @@ <h1 id="arch-univariate-arx-starting-values">arch.univariate.ARX.starting_values
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/devel/univariate/generated/generated/arch.univariate.ARX.volatility.html b/devel/univariate/generated/generated/arch.univariate.ARX.volatility.html
index 736db280c0..5672caad7e 100644
--- a/devel/univariate/generated/generated/arch.univariate.ARX.volatility.html
+++ b/devel/univariate/generated/generated/arch.univariate.ARX.volatility.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ARX.volatility - arch v6.2.1 (+21)</title>
+        <title>arch.univariate.ARX.volatility - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -1215,7 +1215,7 @@ <h1 id="arch-univariate-arx-volatility">arch.univariate.ARX.volatility<a class="
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
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   </body>
diff --git a/devel/univariate/generated/generated/arch.univariate.ARX.x.html b/devel/univariate/generated/generated/arch.univariate.ARX.x.html
index f5c6d9cee6..201fa0242a 100644
--- a/devel/univariate/generated/generated/arch.univariate.ARX.x.html
+++ b/devel/univariate/generated/generated/arch.univariate.ARX.x.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ARX.x - arch v6.2.1 (+21)</title>
+        <title>arch.univariate.ARX.x - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -1096,7 +1096,7 @@
 <h1 id="arch-univariate-arx-x">arch.univariate.ARX.x<a class="headerlink" href="#arch-univariate-arx-x" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.ARX.x">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARX.</span></span><span class="sig-name descname"><span class="pre">x</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span><a class="headerlink" href="#arch.univariate.ARX.x" title="Link to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARX.</span></span><span class="sig-name descname"><span class="pre">x</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span><a class="headerlink" href="#arch.univariate.ARX.x" title="Link to this definition">¶</a></dt>
 <dd><p>Gets the value of the exogenous regressors in the model</p>
 </dd></dl>
 
@@ -1214,7 +1214,7 @@ <h1 id="arch-univariate-arx-x">arch.univariate.ARX.x<a class="headerlink" href="
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diff --git a/devel/univariate/generated/generated/arch.univariate.ARX.y.html b/devel/univariate/generated/generated/arch.univariate.ARX.y.html
index 528f771460..193dbc7e22 100644
--- a/devel/univariate/generated/generated/arch.univariate.ARX.y.html
+++ b/devel/univariate/generated/generated/arch.univariate.ARX.y.html
@@ -15,7 +15,7 @@
     
   
       
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@@ -47,7 +47,7 @@
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@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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@@ -1096,7 +1096,7 @@
 <h1 id="arch-univariate-arx-y">arch.univariate.ARX.y<a class="headerlink" href="#arch-univariate-arx-y" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.ARX.y">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARX.</span></span><span class="sig-name descname"><span class="pre">y</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span><a class="headerlink" href="#arch.univariate.ARX.y" title="Link to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARX.</span></span><span class="sig-name descname"><span class="pre">y</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span><a class="headerlink" href="#arch.univariate.ARX.y" title="Link to this definition">¶</a></dt>
 <dd><p>Returns the dependent variable</p>
 </dd></dl>
 
@@ -1214,7 +1214,7 @@ <h1 id="arch-univariate-arx-y">arch.univariate.ARX.y<a class="headerlink" href="
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diff --git a/devel/univariate/generated/generated/arch.univariate.ConstantMean.bounds.html b/devel/univariate/generated/generated/arch.univariate.ConstantMean.bounds.html
index 054f298632..88cde0da8d 100644
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.2.1 (+21)
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@@ -189,10 +189,10 @@
 
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+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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diff --git a/devel/univariate/generated/generated/arch.univariate.ConstantMean.compute_param_cov.html b/devel/univariate/generated/generated/arch.univariate.ConstantMean.compute_param_cov.html
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+++ b/devel/univariate/generated/generated/arch.univariate.ConstantMean.compute_param_cov.html
@@ -15,7 +15,7 @@
     
   
       
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+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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-    arch v6.2.1 (+21)
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diff --git a/devel/univariate/generated/generated/arch.univariate.ConstantMean.constraints.html b/devel/univariate/generated/generated/arch.univariate.ConstantMean.constraints.html
index 13cdc9c56c..df21801075 100644
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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@@ -102,7 +102,7 @@
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           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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@@ -189,10 +189,10 @@
 
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+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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@@ -1251,7 +1251,7 @@ <h1 id="arch-univariate-constantmean-constraints">arch.univariate.ConstantMean.c
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diff --git a/devel/univariate/generated/generated/arch.univariate.ConstantMean.distribution.html b/devel/univariate/generated/generated/arch.univariate.ConstantMean.distribution.html
index 96adede9ec..af424138cb 100644
--- a/devel/univariate/generated/generated/arch.univariate.ConstantMean.distribution.html
+++ b/devel/univariate/generated/generated/arch.univariate.ConstantMean.distribution.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ConstantMean.distribution - arch v6.2.1 (+21)</title>
+        <title>arch.univariate.ConstantMean.distribution - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
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@@ -92,7 +92,7 @@
 
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@@ -102,7 +102,7 @@
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           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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@@ -189,10 +189,10 @@
 
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+    arch v6.2.1 (+23)
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diff --git a/devel/univariate/generated/generated/arch.univariate.ConstantMean.fit.html b/devel/univariate/generated/generated/arch.univariate.ConstantMean.fit.html
index 984b724c89..9d56faa44c 100644
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+++ b/devel/univariate/generated/generated/arch.univariate.ConstantMean.fit.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ConstantMean.fit - arch v6.2.1 (+21)</title>
+        <title>arch.univariate.ConstantMean.fit - arch v6.2.1 (+23)</title>
       
     
 
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-            arch v6.2.1 (+21)
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@@ -1302,7 +1302,7 @@
 <h1 id="arch-univariate-constantmean-fit">arch.univariate.ConstantMean.fit<a class="headerlink" href="#arch-univariate-constantmean-fit" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.ConstantMean.fit">
-<span class="sig-prename descclassname"><span class="pre">ConstantMean.</span></span><span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantMean.fit.update_freq" title="arch.univariate.ConstantMean.fit.update_freq (Python parameter) — Frequency of iteration updates.  Output is generated every update_freq iterations. Set to 0 to disable iterative output."><span class="n"><span class="pre">update_freq</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantMean.fit.disp" title="arch.univariate.ConstantMean.fit.disp (Python parameter) — Either 'final' to print optimization result or 'off' to display nothing. If using a boolean, False is &quot;off&quot; and True is &quot;final&quot;"><span class="n"><span class="pre">disp</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'off'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'final'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;final&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantMean.fit.starting_values" title="arch.univariate.ConstantMean.fit.starting_values (Python parameter) — Array of starting values to use.  If not provided, starting values are constructed by the model components."><span class="n"><span class="pre">starting_values</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantMean.fit.cov_type" title="arch.univariate.ConstantMean.fit.cov_type (Python parameter) — Estimation method of parameter covariance.  Supported options are 'robust', which does not assume the Information Matrix Equality holds and 'classic' which does.  In the ARCH literature, 'robust' corresponds to Bollerslev-Wooldridge covariance estimator."><span class="n"><span class="pre">cov_type</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'robust'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'classic'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;robust&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantMean.fit.show_warning" title="arch.univariate.ConstantMean.fit.show_warning (Python parameter) — Flag indicating whether convergence warnings should be shown."><span class="n"><span class="pre">show_warning</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantMean.fit.first_obs" title="arch.univariate.ConstantMean.fit.first_obs (Python parameter) — First observation to use when estimating model"><span class="n"><span class="pre">first_obs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.12)"><span class="pre">datetime</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)"><span class="pre">datetime64</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.1)"><span class="pre">Timestamp</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantMean.fit.last_obs" title="arch.univariate.ConstantMean.fit.last_obs (Python parameter) — Last observation to use when estimating model"><span class="n"><span class="pre">last_obs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.12)"><span class="pre">datetime</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)"><span class="pre">datetime64</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.1)"><span class="pre">Timestamp</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantMean.fit.tol" title="arch.univariate.ConstantMean.fit.tol (Python parameter) — Tolerance for termination."><span class="n"><span class="pre">tol</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantMean.fit.options" title="arch.univariate.ConstantMean.fit.options (Python parameter) — Options to pass to scipy.optimize.minimize.  Valid entries include 'ftol', 'eps', 'disp', and 'maxiter'."><span class="n"><span class="pre">options</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.12)"><span class="pre">Any</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantMean.fit.backcast" title="arch.univariate.ConstantMean.fit.backcast (Python parameter) — Value to use as backcast. Should be measure \sigma^2_0 since model-specific non-linear transformations are applied to value before computing the variance recursions."><span class="n"><span class="pre">backcast</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference internal" href="../arch.univariate.base.ARCHModelResult.html#arch.univariate.base.ARCHModelResult" title="arch.univariate.base.ARCHModelResult (Python class) — Results from estimation of an ARCHModel model"><span class="pre">ARCHModelResult</span></a></span></span></span><a class="headerlink" href="#arch.univariate.ConstantMean.fit" title="Link to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">ConstantMean.</span></span><span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantMean.fit.update_freq" title="arch.univariate.ConstantMean.fit.update_freq (Python parameter) — Frequency of iteration updates.  Output is generated every update_freq iterations. Set to 0 to disable iterative output."><span class="n"><span class="pre">update_freq</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantMean.fit.disp" title="arch.univariate.ConstantMean.fit.disp (Python parameter) — Either 'final' to print optimization result or 'off' to display nothing. If using a boolean, False is &quot;off&quot; and True is &quot;final&quot;"><span class="n"><span class="pre">disp</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'off'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'final'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;final&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantMean.fit.starting_values" title="arch.univariate.ConstantMean.fit.starting_values (Python parameter) — Array of starting values to use.  If not provided, starting values are constructed by the model components."><span class="n"><span class="pre">starting_values</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantMean.fit.cov_type" title="arch.univariate.ConstantMean.fit.cov_type (Python parameter) — Estimation method of parameter covariance.  Supported options are 'robust', which does not assume the Information Matrix Equality holds and 'classic' which does.  In the ARCH literature, 'robust' corresponds to Bollerslev-Wooldridge covariance estimator."><span class="n"><span class="pre">cov_type</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'robust'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'classic'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;robust&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantMean.fit.show_warning" title="arch.univariate.ConstantMean.fit.show_warning (Python parameter) — Flag indicating whether convergence warnings should be shown."><span class="n"><span class="pre">show_warning</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantMean.fit.first_obs" title="arch.univariate.ConstantMean.fit.first_obs (Python parameter) — First observation to use when estimating model"><span class="n"><span class="pre">first_obs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.12)"><span class="pre">datetime</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)"><span class="pre">datetime64</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.4)"><span class="pre">Timestamp</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantMean.fit.last_obs" title="arch.univariate.ConstantMean.fit.last_obs (Python parameter) — Last observation to use when estimating model"><span class="n"><span class="pre">last_obs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.12)"><span class="pre">datetime</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)"><span class="pre">datetime64</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.4)"><span class="pre">Timestamp</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantMean.fit.tol" title="arch.univariate.ConstantMean.fit.tol (Python parameter) — Tolerance for termination."><span class="n"><span class="pre">tol</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantMean.fit.options" title="arch.univariate.ConstantMean.fit.options (Python parameter) — Options to pass to scipy.optimize.minimize.  Valid entries include 'ftol', 'eps', 'disp', and 'maxiter'."><span class="n"><span class="pre">options</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.12)"><span class="pre">Any</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantMean.fit.backcast" title="arch.univariate.ConstantMean.fit.backcast (Python parameter) — Value to use as backcast. Should be measure \sigma^2_0 since model-specific non-linear transformations are applied to value before computing the variance recursions."><span class="n"><span class="pre">backcast</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference internal" href="../arch.univariate.base.ARCHModelResult.html#arch.univariate.base.ARCHModelResult" title="arch.univariate.base.ARCHModelResult (Python class) — Results from estimation of an ARCHModel model"><span class="pre">ARCHModelResult</span></a></span></span></span><a class="headerlink" href="#arch.univariate.ConstantMean.fit" title="Link to this definition">¶</a></dt>
 <dd><p>Estimate model parameters</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.ConstantMean.fit-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.ConstantMean.fit-parameters" title="Permalink to this headline">¶</a></dt>
@@ -1313,7 +1313,7 @@ <h1 id="arch-univariate-constantmean-fit">arch.univariate.ConstantMean.fit<a cla
 <dt id="arch.univariate.ConstantMean.fit.disp"><span class="n sig-name">disp</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)">bool</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'off'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'final'</span></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;final&#39;</span></code><a class="headerlink" href="#arch.univariate.ConstantMean.fit.disp" title="Permalink to this definition">¶</a></dt><dd><p>Either ‘final’ to print optimization result or ‘off’ to display
 nothing. If using a boolean, False is “off” and True is “final”</p>
 </dd>
-<dt id="arch.univariate.ConstantMean.fit.starting_values"><span class="n sig-name">starting_values</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ConstantMean.fit.starting_values" title="Permalink to this definition">¶</a></dt><dd><p>Array of starting values to use.  If not provided, starting values
+<dt id="arch.univariate.ConstantMean.fit.starting_values"><span class="n sig-name">starting_values</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ConstantMean.fit.starting_values" title="Permalink to this definition">¶</a></dt><dd><p>Array of starting values to use.  If not provided, starting values
 are constructed by the model components.</p>
 </dd>
 <dt id="arch.univariate.ConstantMean.fit.cov_type"><span class="n sig-name">cov_type</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><span class="s">'robust'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'classic'</span></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;robust&#39;</span></code><a class="headerlink" href="#arch.univariate.ConstantMean.fit.cov_type" title="Permalink to this definition">¶</a></dt><dd><p>Estimation method of parameter covariance.  Supported options are
@@ -1323,9 +1323,9 @@ <h1 id="arch-univariate-constantmean-fit">arch.univariate.ConstantMean.fit<a cla
 </dd>
 <dt id="arch.univariate.ConstantMean.fit.show_warning"><span class="n sig-name">show_warning</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)">bool</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code><a class="headerlink" href="#arch.univariate.ConstantMean.fit.show_warning" title="Permalink to this definition">¶</a></dt><dd><p>Flag indicating whether convergence warnings should be shown.</p>
 </dd>
-<dt id="arch.univariate.ConstantMean.fit.first_obs"><span class="n sig-name">first_obs</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)">str</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.12)">datetime</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)">datetime64</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.1)">Timestamp</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ConstantMean.fit.first_obs" title="Permalink to this definition">¶</a></dt><dd><p>First observation to use when estimating model</p>
+<dt id="arch.univariate.ConstantMean.fit.first_obs"><span class="n sig-name">first_obs</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)">str</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.12)">datetime</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)">datetime64</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.4)">Timestamp</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ConstantMean.fit.first_obs" title="Permalink to this definition">¶</a></dt><dd><p>First observation to use when estimating model</p>
 </dd>
-<dt id="arch.univariate.ConstantMean.fit.last_obs"><span class="n sig-name">last_obs</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)">str</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.12)">datetime</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)">datetime64</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.1)">Timestamp</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ConstantMean.fit.last_obs" title="Permalink to this definition">¶</a></dt><dd><p>Last observation to use when estimating model</p>
+<dt id="arch.univariate.ConstantMean.fit.last_obs"><span class="n sig-name">last_obs</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)">str</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.12)">datetime</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)">datetime64</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.4)">Timestamp</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ConstantMean.fit.last_obs" title="Permalink to this definition">¶</a></dt><dd><p>Last observation to use when estimating model</p>
 </dd>
 <dt id="arch.univariate.ConstantMean.fit.tol"><span class="n sig-name">tol</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ConstantMean.fit.tol" title="Permalink to this definition">¶</a></dt><dd><p>Tolerance for termination.</p>
 </dd>
@@ -1465,7 +1465,7 @@ <h1 id="arch-univariate-constantmean-fit">arch.univariate.ConstantMean.fit<a cla
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index 16e0e919c6..dd3f0d9bc9 100644
--- a/devel/univariate/generated/generated/arch.univariate.ConstantMean.fix.html
+++ b/devel/univariate/generated/generated/arch.univariate.ConstantMean.fix.html
@@ -15,7 +15,7 @@
     
   
       
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-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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@@ -102,7 +102,7 @@
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@@ -1204,18 +1204,18 @@
 <h1 id="arch-univariate-constantmean-fix">arch.univariate.ConstantMean.fix<a class="headerlink" href="#arch-univariate-constantmean-fix" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.ConstantMean.fix">
-<span class="sig-prename descclassname"><span class="pre">ConstantMean.</span></span><span class="sig-name descname"><span class="pre">fix</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantMean.fix.params" title="arch.univariate.ConstantMean.fix.params (Python parameter) — User specified parameters to use when generating the result. Must have the correct number of parameters for a given choice of mean model, volatility model and distribution."><span class="n"><span class="pre">params</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantMean.fix.first_obs" title="arch.univariate.ConstantMean.fix.first_obs (Python parameter) — First observation to use when fixing model"><span class="n"><span class="pre">first_obs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.12)"><span class="pre">datetime</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)"><span class="pre">datetime64</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.1)"><span class="pre">Timestamp</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantMean.fix.last_obs" title="arch.univariate.ConstantMean.fix.last_obs (Python parameter) — Last observation to use when fixing model"><span class="n"><span class="pre">last_obs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.12)"><span class="pre">datetime</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)"><span class="pre">datetime64</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.1)"><span class="pre">Timestamp</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference internal" href="../arch.univariate.base.ARCHModelFixedResult.html#arch.univariate.base.ARCHModelFixedResult" title="arch.univariate.base.ARCHModelFixedResult (Python class) — Results for fixed parameters for an ARCHModel model"><span class="pre">ARCHModelFixedResult</span></a></span></span></span><a class="headerlink" href="#arch.univariate.ConstantMean.fix" title="Link to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">ConstantMean.</span></span><span class="sig-name descname"><span class="pre">fix</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantMean.fix.params" title="arch.univariate.ConstantMean.fix.params (Python parameter) — User specified parameters to use when generating the result. Must have the correct number of parameters for a given choice of mean model, volatility model and distribution."><span class="n"><span class="pre">params</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantMean.fix.first_obs" title="arch.univariate.ConstantMean.fix.first_obs (Python parameter) — First observation to use when fixing model"><span class="n"><span class="pre">first_obs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.12)"><span class="pre">datetime</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)"><span class="pre">datetime64</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.4)"><span class="pre">Timestamp</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantMean.fix.last_obs" title="arch.univariate.ConstantMean.fix.last_obs (Python parameter) — Last observation to use when fixing model"><span class="n"><span class="pre">last_obs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.12)"><span class="pre">datetime</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)"><span class="pre">datetime64</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.4)"><span class="pre">Timestamp</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference internal" href="../arch.univariate.base.ARCHModelFixedResult.html#arch.univariate.base.ARCHModelFixedResult" title="arch.univariate.base.ARCHModelFixedResult (Python class) — Results for fixed parameters for an ARCHModel model"><span class="pre">ARCHModelFixedResult</span></a></span></span></span><a class="headerlink" href="#arch.univariate.ConstantMean.fix" title="Link to this definition">¶</a></dt>
 <dd><p>Allows an ARCHModelFixedResult to be constructed from fixed parameters.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.ConstantMean.fix-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.ConstantMean.fix-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.ConstantMean.fix.params"><span class="n sig-name">params</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.univariate.ConstantMean.fix.params" title="Permalink to this definition">¶</a></dt><dd><p>User specified parameters to use when generating the result. Must
+<dt id="arch.univariate.ConstantMean.fix.params"><span class="n sig-name">params</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a></span></span><a class="headerlink" href="#arch.univariate.ConstantMean.fix.params" title="Permalink to this definition">¶</a></dt><dd><p>User specified parameters to use when generating the result. Must
 have the correct number of parameters for a given choice of mean
 model, volatility model and distribution.</p>
 </dd>
-<dt id="arch.univariate.ConstantMean.fix.first_obs"><span class="n sig-name">first_obs</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)">str</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.12)">datetime</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)">datetime64</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.1)">Timestamp</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ConstantMean.fix.first_obs" title="Permalink to this definition">¶</a></dt><dd><p>First observation to use when fixing model</p>
+<dt id="arch.univariate.ConstantMean.fix.first_obs"><span class="n sig-name">first_obs</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)">str</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.12)">datetime</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)">datetime64</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.4)">Timestamp</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ConstantMean.fix.first_obs" title="Permalink to this definition">¶</a></dt><dd><p>First observation to use when fixing model</p>
 </dd>
-<dt id="arch.univariate.ConstantMean.fix.last_obs"><span class="n sig-name">last_obs</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)">str</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.12)">datetime</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)">datetime64</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.1)">Timestamp</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ConstantMean.fix.last_obs" title="Permalink to this definition">¶</a></dt><dd><p>Last observation to use when fixing model</p>
+<dt id="arch.univariate.ConstantMean.fix.last_obs"><span class="n sig-name">last_obs</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)">str</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.12)">datetime</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)">datetime64</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.4)">Timestamp</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ConstantMean.fix.last_obs" title="Permalink to this definition">¶</a></dt><dd><p>Last observation to use when fixing model</p>
 </dd>
 </dl>
 </dd>
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diff --git a/devel/univariate/generated/generated/arch.univariate.ConstantMean.forecast.html b/devel/univariate/generated/generated/arch.univariate.ConstantMean.forecast.html
index 6d6f769876..3e101823d8 100644
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+++ b/devel/univariate/generated/generated/arch.univariate.ConstantMean.forecast.html
@@ -15,7 +15,7 @@
     
   
       
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@@ -1302,17 +1302,17 @@
 <h1 id="arch-univariate-constantmean-forecast">arch.univariate.ConstantMean.forecast<a class="headerlink" href="#arch-univariate-constantmean-forecast" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.ConstantMean.forecast">
-<span class="sig-prename descclassname"><span class="pre">ConstantMean.</span></span><span class="sig-name descname"><span class="pre">forecast</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantMean.forecast.params" title="arch.univariate.ConstantMean.forecast.params (Python parameter) — Parameters required to forecast. Must be identical in shape to the parameters computed by fitting the model."><span class="n"><span class="pre">params</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantMean.forecast.horizon" title="arch.univariate.ConstantMean.forecast.horizon (Python parameter) — Number of steps to forecast"><span class="n"><span class="pre">horizon</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantMean.forecast.start" title="arch.univariate.ConstantMean.forecast.start (Python parameter) — An integer, datetime or str indicating the first observation to produce the forecast for.  Datetimes can only be used with pandas inputs that have a datetime index. Strings must be convertible to a date time, such as in '1945-01-01'."><span class="n"><span class="pre">start</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.12)"><span class="pre">datetime</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)"><span class="pre">datetime64</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.1)"><span class="pre">Timestamp</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantMean.forecast.align" title="arch.univariate.ConstantMean.forecast.align (Python parameter) — Either 'origin' or 'target'.  When set of 'origin', the t-th row of forecasts contains the forecasts for t+1, t+2, ..., t+h. When set to 'target', the t-th row contains the 1-step ahead forecast from time t-1, the 2 step from time t-2, ..., and the h-step from time t-h.  'target' simplified computing forecast errors since the realization and h-step forecast are aligned."><span class="n"><span class="pre">align</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'origin'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'target'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;origin&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantMean.forecast.method" title="arch.univariate.ConstantMean.forecast.method (Python parameter) — Method to use when producing the forecast. The default is analytic. The method only affects the variance forecast generation.  Not all volatility models support all methods. In particular, volatility models that do not evolve in squares such as EGARCH or TARCH do not support the 'analytic' method for horizons &gt; 1."><span class="n"><span class="pre">method</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'analytic'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'simulation'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'bootstrap'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;analytic&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantMean.forecast.simulations" title="arch.univariate.ConstantMean.forecast.simulations (Python parameter) — Number of simulations to run when computing the forecast using either simulation or bootstrap."><span class="n"><span class="pre">simulations</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantMean.forecast.rng" title="arch.univariate.ConstantMean.forecast.rng (Python parameter) — Custom random number generator to use in simulation-based forecasts. Must produce random samples using the syntax rng(size) where size the 2-element tuple (simulations, horizon)."><span class="n"><span class="pre">rng</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.12)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantMean.forecast.random_state" title="arch.univariate.ConstantMean.forecast.random_state (Python parameter) — NumPy RandomState instance to use when method is 'bootstrap'"><span class="n"><span class="pre">random_state</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.26)"><span class="pre">RandomState</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantMean.forecast.reindex" title="arch.univariate.ConstantMean.forecast.reindex (Python parameter) — Whether to reindex the forecasts to have the same dimension as the series being forecast. Prior to 4.18 this was the default. As of 4.19 this is now optional. If not provided, a warning is raised about the future change in the default which will occur after September 2021."><span class="n"><span class="pre">reindex</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantMean.forecast.x" title="arch.univariate.ConstantMean.forecast.x (Python parameter) — Values to use for exogenous regressors if any are included in the model. Three formats are accepted:"><span class="n"><span class="pre">x</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Hashable" title="(in Python v3.12)"><span class="pre">Hashable</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference internal" href="../arch.univariate.base.ARCHModelForecast.html#arch.univariate.base.ARCHModelForecast" title="arch.univariate.base.ARCHModelForecast (Python class) — Container for forecasts from an ARCH Model"><span class="pre">ARCHModelForecast</span></a></span></span></span><a class="headerlink" href="#arch.univariate.ConstantMean.forecast" title="Link to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">ConstantMean.</span></span><span class="sig-name descname"><span class="pre">forecast</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantMean.forecast.params" title="arch.univariate.ConstantMean.forecast.params (Python parameter) — Parameters required to forecast. Must be identical in shape to the parameters computed by fitting the model."><span class="n"><span class="pre">params</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantMean.forecast.horizon" title="arch.univariate.ConstantMean.forecast.horizon (Python parameter) — Number of steps to forecast"><span class="n"><span class="pre">horizon</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantMean.forecast.start" title="arch.univariate.ConstantMean.forecast.start (Python parameter) — An integer, datetime or str indicating the first observation to produce the forecast for.  Datetimes can only be used with pandas inputs that have a datetime index. Strings must be convertible to a date time, such as in '1945-01-01'."><span class="n"><span class="pre">start</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.12)"><span class="pre">datetime</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)"><span class="pre">datetime64</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.4)"><span class="pre">Timestamp</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantMean.forecast.align" title="arch.univariate.ConstantMean.forecast.align (Python parameter) — Either 'origin' or 'target'.  When set of 'origin', the t-th row of forecasts contains the forecasts for t+1, t+2, ..., t+h. When set to 'target', the t-th row contains the 1-step ahead forecast from time t-1, the 2 step from time t-2, ..., and the h-step from time t-h.  'target' simplified computing forecast errors since the realization and h-step forecast are aligned."><span class="n"><span class="pre">align</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'origin'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'target'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;origin&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantMean.forecast.method" title="arch.univariate.ConstantMean.forecast.method (Python parameter) — Method to use when producing the forecast. The default is analytic. The method only affects the variance forecast generation.  Not all volatility models support all methods. In particular, volatility models that do not evolve in squares such as EGARCH or TARCH do not support the 'analytic' method for horizons &gt; 1."><span class="n"><span class="pre">method</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'analytic'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'simulation'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'bootstrap'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;analytic&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantMean.forecast.simulations" title="arch.univariate.ConstantMean.forecast.simulations (Python parameter) — Number of simulations to run when computing the forecast using either simulation or bootstrap."><span class="n"><span class="pre">simulations</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantMean.forecast.rng" title="arch.univariate.ConstantMean.forecast.rng (Python parameter) — Custom random number generator to use in simulation-based forecasts. Must produce random samples using the syntax rng(size) where size the 2-element tuple (simulations, horizon)."><span class="n"><span class="pre">rng</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.12)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantMean.forecast.random_state" title="arch.univariate.ConstantMean.forecast.random_state (Python parameter) — NumPy RandomState instance to use when method is 'bootstrap'"><span class="n"><span class="pre">random_state</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.26)"><span class="pre">RandomState</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantMean.forecast.reindex" title="arch.univariate.ConstantMean.forecast.reindex (Python parameter) — Whether to reindex the forecasts to have the same dimension as the series being forecast. Prior to 4.18 this was the default. As of 4.19 this is now optional. If not provided, a warning is raised about the future change in the default which will occur after September 2021."><span class="n"><span class="pre">reindex</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantMean.forecast.x" title="arch.univariate.ConstantMean.forecast.x (Python parameter) — Values to use for exogenous regressors if any are included in the model. Three formats are accepted:"><span class="n"><span class="pre">x</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Hashable" title="(in Python v3.12)"><span class="pre">Hashable</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference internal" href="../arch.univariate.base.ARCHModelForecast.html#arch.univariate.base.ARCHModelForecast" title="arch.univariate.base.ARCHModelForecast (Python class) — Container for forecasts from an ARCH Model"><span class="pre">ARCHModelForecast</span></a></span></span></span><a class="headerlink" href="#arch.univariate.ConstantMean.forecast" title="Link to this definition">¶</a></dt>
 <dd><p>Construct forecasts from estimated model</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.ConstantMean.forecast-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.ConstantMean.forecast-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.ConstantMean.forecast.params"><span class="n sig-name">params</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.univariate.ConstantMean.forecast.params" title="Permalink to this definition">¶</a></dt><dd><p>Parameters required to forecast. Must be identical in
+<dt id="arch.univariate.ConstantMean.forecast.params"><span class="n sig-name">params</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a></span></span><a class="headerlink" href="#arch.univariate.ConstantMean.forecast.params" title="Permalink to this definition">¶</a></dt><dd><p>Parameters required to forecast. Must be identical in
 shape to the parameters computed by fitting the model.</p>
 </dd>
 <dt id="arch.univariate.ConstantMean.forecast.horizon"><span class="n sig-name">horizon</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code><a class="headerlink" href="#arch.univariate.ConstantMean.forecast.horizon" title="Permalink to this definition">¶</a></dt><dd><p>Number of steps to forecast</p>
 </dd>
-<dt id="arch.univariate.ConstantMean.forecast.start"><span class="n sig-name">start</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)">str</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.12)">datetime</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)">datetime64</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.1)">Timestamp</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ConstantMean.forecast.start" title="Permalink to this definition">¶</a></dt><dd><p>An integer, datetime or str indicating the first observation to
+<dt id="arch.univariate.ConstantMean.forecast.start"><span class="n sig-name">start</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)">str</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.12)">datetime</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)">datetime64</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.4)">Timestamp</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ConstantMean.forecast.start" title="Permalink to this definition">¶</a></dt><dd><p>An integer, datetime or str indicating the first observation to
 produce the forecast for.  Datetimes can only be used with pandas
 inputs that have a datetime index. Strings must be convertible
 to a date time, such as in ‘1945-01-01’.</p>
@@ -1348,7 +1348,7 @@ <h1 id="arch-univariate-constantmean-forecast">arch.univariate.ConstantMean.fore
 </div>
 </p>
 </dd>
-<dt id="arch.univariate.ConstantMean.forecast.x"><span class="n sig-name">x</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)">dict</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Hashable" title="(in Python v3.12)">Hashable</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ConstantMean.forecast.x" title="Permalink to this definition">¶</a></dt><dd><p><p>Values to use for exogenous regressors if any are included in the
+<dt id="arch.univariate.ConstantMean.forecast.x"><span class="n sig-name">x</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)">dict</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Hashable" title="(in Python v3.12)">Hashable</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ConstantMean.forecast.x" title="Permalink to this definition">¶</a></dt><dd><p><p>Values to use for exogenous regressors if any are included in the
 model. Three formats are accepted:</p>
 <ul class="simple">
 <li><p>2-d array-like: This format can be used when there is a single
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+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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diff --git a/devel/univariate/generated/generated/arch.univariate.ConstantMean.num_params.html b/devel/univariate/generated/generated/arch.univariate.ConstantMean.num_params.html
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@@ -15,7 +15,7 @@
     
   
       
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+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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@@ -189,10 +189,10 @@
 
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+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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diff --git a/devel/univariate/generated/generated/arch.univariate.ConstantMean.parameter_names.html b/devel/univariate/generated/generated/arch.univariate.ConstantMean.parameter_names.html
index c4e7f550bf..f3341e8b03 100644
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+++ b/devel/univariate/generated/generated/arch.univariate.ConstantMean.parameter_names.html
@@ -15,7 +15,7 @@
     
   
       
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+            arch v6.2.1 (+23)
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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@@ -1262,7 +1262,7 @@ <h1 id="arch-univariate-constantmean-parameter-names">arch.univariate.ConstantMe
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diff --git a/devel/univariate/generated/generated/arch.univariate.ConstantMean.resids.html b/devel/univariate/generated/generated/arch.univariate.ConstantMean.resids.html
index a17df8d925..56d144672f 100644
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-        <title>arch.univariate.ConstantMean.resids - arch v6.2.1 (+21)</title>
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@@ -92,7 +92,7 @@
 
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 <h1 id="arch-univariate-constantmean-resids">arch.univariate.ConstantMean.resids<a class="headerlink" href="#arch-univariate-constantmean-resids" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.ConstantMean.resids">
-<span class="sig-prename descclassname"><span class="pre">ConstantMean.</span></span><span class="sig-name descname"><span class="pre">resids</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantMean.resids.params" title="arch.univariate.ConstantMean.resids.params (Python parameter) — Model parameters"><span class="n"><span class="pre">params</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantMean.resids.y" title="arch.univariate.ConstantMean.resids.y (Python parameter) — Alternative values to use when computing model residuals"><span class="n"><span class="pre">y</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantMean.resids.regressors" title="arch.univariate.ConstantMean.resids.regressors (Python parameter) — Alternative regressor values to use when computing model residuals"><span class="n"><span class="pre">regressors</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/mean.html#ConstantMean.resids"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.ConstantMean.resids" title="Link to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">ConstantMean.</span></span><span class="sig-name descname"><span class="pre">resids</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantMean.resids.params" title="arch.univariate.ConstantMean.resids.params (Python parameter) — Model parameters"><span class="n"><span class="pre">params</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantMean.resids.y" title="arch.univariate.ConstantMean.resids.y (Python parameter) — Alternative values to use when computing model residuals"><span class="n"><span class="pre">y</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantMean.resids.regressors" title="arch.univariate.ConstantMean.resids.regressors (Python parameter) — Alternative regressor values to use when computing model residuals"><span class="n"><span class="pre">regressors</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/mean.html#ConstantMean.resids"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.ConstantMean.resids" title="Link to this definition">¶</a></dt>
 <dd><p>Compute model residuals</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.ConstantMean.resids-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.ConstantMean.resids-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
 <dt id="arch.univariate.ConstantMean.resids.params"><span class="n sig-name">params</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.ConstantMean.resids.params" title="Permalink to this definition">¶</a></dt><dd><p>Model parameters</p>
 </dd>
-<dt id="arch.univariate.ConstantMean.resids.y"><span class="n sig-name">y</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ConstantMean.resids.y" title="Permalink to this definition">¶</a></dt><dd><p>Alternative values to use when computing model residuals</p>
+<dt id="arch.univariate.ConstantMean.resids.y"><span class="n sig-name">y</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ConstantMean.resids.y" title="Permalink to this definition">¶</a></dt><dd><p>Alternative values to use when computing model residuals</p>
 </dd>
-<dt id="arch.univariate.ConstantMean.resids.regressors"><span class="n sig-name">regressors</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ConstantMean.resids.regressors" title="Permalink to this definition">¶</a></dt><dd><p>Alternative regressor values to use when computing model residuals</p>
+<dt id="arch.univariate.ConstantMean.resids.regressors"><span class="n sig-name">regressors</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ConstantMean.resids.regressors" title="Permalink to this definition">¶</a></dt><dd><p>Alternative regressor values to use when computing model residuals</p>
 </dd>
 </dl>
 </dd>
@@ -1340,7 +1340,7 @@ <h1 id="arch-univariate-constantmean-resids">arch.univariate.ConstantMean.resids
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index b73e2bee5c..9407016066 100644
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 <h1 id="arch-univariate-constantmean-simulate">arch.univariate.ConstantMean.simulate<a class="headerlink" href="#arch-univariate-constantmean-simulate" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.ConstantMean.simulate">
-<span class="sig-prename descclassname"><span class="pre">ConstantMean.</span></span><span class="sig-name descname"><span class="pre">simulate</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantMean.simulate.params" title="arch.univariate.ConstantMean.simulate.params (Python parameter) — Parameters to use when simulating the model.  Parameter order is [mean volatility distribution]. There is one parameter in the mean model, mu."><span class="n"><span class="pre">params</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantMean.simulate.nobs" title="arch.univariate.ConstantMean.simulate.nobs (Python parameter) — Length of series to simulate"><span class="n"><span class="pre">nobs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantMean.simulate.burn" title="arch.univariate.ConstantMean.simulate.burn (Python parameter) — Number of values to simulate to initialize the model and remove dependence on initial values."><span class="n"><span class="pre">burn</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">500</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantMean.simulate.initial_value" title="arch.univariate.ConstantMean.simulate.initial_value (Python parameter) — This value is not used."><span class="n"><span class="pre">initial_value</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantMean.simulate.x" title="arch.univariate.ConstantMean.simulate.x (Python parameter) — This value is not used."><span class="n"><span class="pre">x</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantMean.simulate.initial_value_vol" title="arch.univariate.ConstantMean.simulate.initial_value_vol (Python parameter) — An array or scalar to use when initializing the volatility process."><span class="n"><span class="pre">initial_value_vol</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/mean.html#ConstantMean.simulate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.ConstantMean.simulate" title="Link to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">ConstantMean.</span></span><span class="sig-name descname"><span class="pre">simulate</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantMean.simulate.params" title="arch.univariate.ConstantMean.simulate.params (Python parameter) — Parameters to use when simulating the model.  Parameter order is [mean volatility distribution]. There is one parameter in the mean model, mu."><span class="n"><span class="pre">params</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantMean.simulate.nobs" title="arch.univariate.ConstantMean.simulate.nobs (Python parameter) — Length of series to simulate"><span class="n"><span class="pre">nobs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantMean.simulate.burn" title="arch.univariate.ConstantMean.simulate.burn (Python parameter) — Number of values to simulate to initialize the model and remove dependence on initial values."><span class="n"><span class="pre">burn</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">500</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantMean.simulate.initial_value" title="arch.univariate.ConstantMean.simulate.initial_value (Python parameter) — This value is not used."><span class="n"><span class="pre">initial_value</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantMean.simulate.x" title="arch.univariate.ConstantMean.simulate.x (Python parameter) — This value is not used."><span class="n"><span class="pre">x</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantMean.simulate.initial_value_vol" title="arch.univariate.ConstantMean.simulate.initial_value_vol (Python parameter) — An array or scalar to use when initializing the volatility process."><span class="n"><span class="pre">initial_value_vol</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><span class="pre">DataFrame</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/mean.html#ConstantMean.simulate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.ConstantMean.simulate" title="Link to this definition">¶</a></dt>
 <dd><p>Simulated data from a constant mean model</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.ConstantMean.simulate-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.ConstantMean.simulate-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.ConstantMean.simulate.params"><span class="n sig-name">params</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="p">]</span></span></span><a class="headerlink" href="#arch.univariate.ConstantMean.simulate.params" title="Permalink to this definition">¶</a></dt><dd><p>Parameters to use when simulating the model.  Parameter order is
+<dt id="arch.univariate.ConstantMean.simulate.params"><span class="n sig-name">params</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="p">]</span></span></span><a class="headerlink" href="#arch.univariate.ConstantMean.simulate.params" title="Permalink to this definition">¶</a></dt><dd><p>Parameters to use when simulating the model.  Parameter order is
 [mean volatility distribution]. There is one parameter in the mean
 model, mu.</p>
 </dd>
@@ -1262,7 +1262,7 @@ <h1 id="arch-univariate-constantmean-simulate">arch.univariate.ConstantMean.simu
 </dd>
 <dt id="arch.univariate.ConstantMean.simulate.initial_value"><span class="n sig-name">initial_value</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ConstantMean.simulate.initial_value" title="Permalink to this definition">¶</a></dt><dd><p>This value is not used.</p>
 </dd>
-<dt id="arch.univariate.ConstantMean.simulate.x"><span class="n sig-name">x</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ConstantMean.simulate.x" title="Permalink to this definition">¶</a></dt><dd><p>This value is not used.</p>
+<dt id="arch.univariate.ConstantMean.simulate.x"><span class="n sig-name">x</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ConstantMean.simulate.x" title="Permalink to this definition">¶</a></dt><dd><p>This value is not used.</p>
 </dd>
 <dt id="arch.univariate.ConstantMean.simulate.initial_value_vol"><span class="n sig-name">initial_value_vol</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ConstantMean.simulate.initial_value_vol" title="Permalink to this definition">¶</a></dt><dd><p>An array or scalar to use when initializing the volatility process.</p>
 </dd>
@@ -1274,7 +1274,7 @@ <h1 id="arch-univariate-constantmean-simulate">arch.univariate.ConstantMean.simu
 containing the errors used in the simulation</p>
 </dd>
 <dt class="field-odd" id="arch.univariate.ConstantMean.simulate-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.ConstantMean.simulate-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><code class="xref py py-class docutils literal notranslate"><span class="pre">pandas.DataFrame</span></code></a></p>
+<dd class="field-odd"><p><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><code class="xref py py-class docutils literal notranslate"><span class="pre">pandas.DataFrame</span></code></a></p>
 </dd>
 </dl>
 <p class="example rubric">Examples</p>
@@ -1405,7 +1405,7 @@ <h1 id="arch-univariate-constantmean-simulate">arch.univariate.ConstantMean.simu
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/devel/univariate/generated/generated/arch.univariate.ConstantMean.starting_values.html b/devel/univariate/generated/generated/arch.univariate.ConstantMean.starting_values.html
index 05ecb49d7d..099b7f3c01 100644
--- a/devel/univariate/generated/generated/arch.univariate.ConstantMean.starting_values.html
+++ b/devel/univariate/generated/generated/arch.univariate.ConstantMean.starting_values.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ConstantMean.starting_values - arch v6.2.1 (+21)</title>
+        <title>arch.univariate.ConstantMean.starting_values - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -1263,7 +1263,7 @@ <h1 id="arch-univariate-constantmean-starting-values">arch.univariate.ConstantMe
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/devel/univariate/generated/generated/arch.univariate.ConstantMean.volatility.html b/devel/univariate/generated/generated/arch.univariate.ConstantMean.volatility.html
index b5f96d88ad..b0fae0e015 100644
--- a/devel/univariate/generated/generated/arch.univariate.ConstantMean.volatility.html
+++ b/devel/univariate/generated/generated/arch.univariate.ConstantMean.volatility.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ConstantMean.volatility - arch v6.2.1 (+21)</title>
+        <title>arch.univariate.ConstantMean.volatility - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -1215,7 +1215,7 @@ <h1 id="arch-univariate-constantmean-volatility">arch.univariate.ConstantMean.vo
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/devel/univariate/generated/generated/arch.univariate.ConstantMean.x.html b/devel/univariate/generated/generated/arch.univariate.ConstantMean.x.html
index 27c82ac0df..5c9c841482 100644
--- a/devel/univariate/generated/generated/arch.univariate.ConstantMean.x.html
+++ b/devel/univariate/generated/generated/arch.univariate.ConstantMean.x.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ConstantMean.x - arch v6.2.1 (+21)</title>
+        <title>arch.univariate.ConstantMean.x - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -1096,7 +1096,7 @@
 <h1 id="arch-univariate-constantmean-x">arch.univariate.ConstantMean.x<a class="headerlink" href="#arch-univariate-constantmean-x" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.ConstantMean.x">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ConstantMean.</span></span><span class="sig-name descname"><span class="pre">x</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span><a class="headerlink" href="#arch.univariate.ConstantMean.x" title="Link to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ConstantMean.</span></span><span class="sig-name descname"><span class="pre">x</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span><a class="headerlink" href="#arch.univariate.ConstantMean.x" title="Link to this definition">¶</a></dt>
 <dd><p>Gets the value of the exogenous regressors in the model</p>
 </dd></dl>
 
@@ -1214,7 +1214,7 @@ <h1 id="arch-univariate-constantmean-x">arch.univariate.ConstantMean.x<a class="
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diff --git a/devel/univariate/generated/generated/arch.univariate.ConstantMean.y.html b/devel/univariate/generated/generated/arch.univariate.ConstantMean.y.html
index 84d5d64f8c..9385ac4a37 100644
--- a/devel/univariate/generated/generated/arch.univariate.ConstantMean.y.html
+++ b/devel/univariate/generated/generated/arch.univariate.ConstantMean.y.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ConstantMean.y - arch v6.2.1 (+21)</title>
+        <title>arch.univariate.ConstantMean.y - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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     <div class="md-nav__source">
@@ -1096,7 +1096,7 @@
 <h1 id="arch-univariate-constantmean-y">arch.univariate.ConstantMean.y<a class="headerlink" href="#arch-univariate-constantmean-y" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.ConstantMean.y">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ConstantMean.</span></span><span class="sig-name descname"><span class="pre">y</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span><a class="headerlink" href="#arch.univariate.ConstantMean.y" title="Link to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ConstantMean.</span></span><span class="sig-name descname"><span class="pre">y</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span><a class="headerlink" href="#arch.univariate.ConstantMean.y" title="Link to this definition">¶</a></dt>
 <dd><p>Returns the dependent variable</p>
 </dd></dl>
 
@@ -1214,7 +1214,7 @@ <h1 id="arch-univariate-constantmean-y">arch.univariate.ConstantMean.y<a class="
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-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
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diff --git a/devel/univariate/generated/generated/arch.univariate.ConstantVariance.backcast.html b/devel/univariate/generated/generated/arch.univariate.ConstantVariance.backcast.html
index a4f4836cb8..1ae99050bc 100644
--- a/devel/univariate/generated/generated/arch.univariate.ConstantVariance.backcast.html
+++ b/devel/univariate/generated/generated/arch.univariate.ConstantVariance.backcast.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ConstantVariance.backcast - arch v6.2.1 (+21)</title>
+        <title>arch.univariate.ConstantVariance.backcast - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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     <div class="md-nav__source">
@@ -1383,7 +1383,7 @@ <h1 id="arch-univariate-constantvariance-backcast">arch.univariate.ConstantVaria
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-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
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   </body>
diff --git a/devel/univariate/generated/generated/arch.univariate.ConstantVariance.backcast_transform.html b/devel/univariate/generated/generated/arch.univariate.ConstantVariance.backcast_transform.html
index 0c3185b376..e46631fbf1 100644
--- a/devel/univariate/generated/generated/arch.univariate.ConstantVariance.backcast_transform.html
+++ b/devel/univariate/generated/generated/arch.univariate.ConstantVariance.backcast_transform.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ConstantVariance.backcast_transform - arch v6.2.1 (+21)</title>
+        <title>arch.univariate.ConstantVariance.backcast_transform - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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     <div class="md-nav__source">
@@ -1383,7 +1383,7 @@ <h1 id="arch-univariate-constantvariance-backcast-transform">arch.univariate.Con
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
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+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
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diff --git a/devel/univariate/generated/generated/arch.univariate.ConstantVariance.bounds.html b/devel/univariate/generated/generated/arch.univariate.ConstantVariance.bounds.html
index d22a0ac44b..ceb91d42d1 100644
--- a/devel/univariate/generated/generated/arch.univariate.ConstantVariance.bounds.html
+++ b/devel/univariate/generated/generated/arch.univariate.ConstantVariance.bounds.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ConstantVariance.bounds - arch v6.2.1 (+21)</title>
+        <title>arch.univariate.ConstantVariance.bounds - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
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@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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@@ -1383,7 +1383,7 @@ <h1 id="arch-univariate-constantvariance-bounds">arch.univariate.ConstantVarianc
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diff --git a/devel/univariate/generated/generated/arch.univariate.ConstantVariance.compute_variance.html b/devel/univariate/generated/generated/arch.univariate.ConstantVariance.compute_variance.html
index b30809bcbd..653791c856 100644
--- a/devel/univariate/generated/generated/arch.univariate.ConstantVariance.compute_variance.html
+++ b/devel/univariate/generated/generated/arch.univariate.ConstantVariance.compute_variance.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ConstantVariance.compute_variance - arch v6.2.1 (+21)</title>
+        <title>arch.univariate.ConstantVariance.compute_variance - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
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-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
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@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
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-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
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-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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@@ -1414,7 +1414,7 @@ <h1 id="arch-univariate-constantvariance-compute-variance">arch.univariate.Const
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diff --git a/devel/univariate/generated/generated/arch.univariate.ConstantVariance.constraints.html b/devel/univariate/generated/generated/arch.univariate.ConstantVariance.constraints.html
index 05ea301c67..7039b8c7b9 100644
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+++ b/devel/univariate/generated/generated/arch.univariate.ConstantVariance.constraints.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ConstantVariance.constraints - arch v6.2.1 (+21)</title>
+        <title>arch.univariate.ConstantVariance.constraints - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
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+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
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-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
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+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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@@ -1328,7 +1328,7 @@ <h1 id="arch-univariate-constantvariance-constraints">arch.univariate.ConstantVa
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diff --git a/devel/univariate/generated/generated/arch.univariate.ConstantVariance.forecast.html b/devel/univariate/generated/generated/arch.univariate.ConstantVariance.forecast.html
index 9577dd57b5..f0184ccc76 100644
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+++ b/devel/univariate/generated/generated/arch.univariate.ConstantVariance.forecast.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ConstantVariance.forecast - arch v6.2.1 (+21)</title>
+        <title>arch.univariate.ConstantVariance.forecast - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
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-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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@@ -189,10 +189,10 @@
 
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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 <h1 id="arch-univariate-constantvariance-forecast">arch.univariate.ConstantVariance.forecast<a class="headerlink" href="#arch-univariate-constantvariance-forecast" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.ConstantVariance.forecast">
-<span class="sig-prename descclassname"><span class="pre">ConstantVariance.</span></span><span class="sig-name descname"><span class="pre">forecast</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantVariance.forecast.parameters" title="arch.univariate.ConstantVariance.forecast.parameters (Python parameter) — Parameters required to forecast the volatility model"><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantVariance.forecast.resids" title="arch.univariate.ConstantVariance.forecast.resids (Python parameter) — Residuals to use in the recursion"><span class="n"><span class="pre">resids</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantVariance.forecast.backcast" title="arch.univariate.ConstantVariance.forecast.backcast (Python parameter) — Value to use when initializing the recursion"><span class="n"><span class="pre">backcast</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantVariance.forecast.var_bounds" title="arch.univariate.ConstantVariance.forecast.var_bounds (Python parameter) — Array containing columns of lower and upper bounds"><span class="n"><span class="pre">var_bounds</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantVariance.forecast.start" title="arch.univariate.ConstantVariance.forecast.start (Python parameter) — Index of the first observation to use as the starting point for the forecast.  Default is len(resids)."><span class="n"><span class="pre">start</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantVariance.forecast.horizon" title="arch.univariate.ConstantVariance.forecast.horizon (Python parameter) — Forecast horizon.  Must be 1 or larger.  Forecasts are produced for horizons in [1, horizon]."><span class="n"><span class="pre">horizon</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantVariance.forecast.method" title="arch.univariate.ConstantVariance.forecast.method (Python parameter) — Method to use when producing the forecast. The default is analytic."><span class="n"><span class="pre">method</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'analytic'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'simulation'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'bootstrap'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;analytic&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantVariance.forecast.simulations" title="arch.univariate.ConstantVariance.forecast.simulations (Python parameter) — Number of simulations to run when computing the forecast using either simulation or bootstrap."><span class="n"><span class="pre">simulations</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantVariance.forecast.rng" title="arch.univariate.ConstantVariance.forecast.rng (Python parameter) — Callable random number generator required if method is 'simulation'. Must take a single shape input and return random samples numbers with that shape."><span class="n"><span class="pre">rng</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.12)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantVariance.forecast.random_state" title="arch.univariate.ConstantVariance.forecast.random_state (Python parameter) — NumPy RandomState instance to use when method is 'bootstrap'"><span class="n"><span class="pre">random_state</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.26)"><span class="pre">RandomState</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><span class="pre">VarianceForecast</span></span></span></span><a class="headerlink" href="#arch.univariate.ConstantVariance.forecast" title="Link to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">ConstantVariance.</span></span><span class="sig-name descname"><span class="pre">forecast</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantVariance.forecast.parameters" title="arch.univariate.ConstantVariance.forecast.parameters (Python parameter) — Parameters required to forecast the volatility model"><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantVariance.forecast.resids" title="arch.univariate.ConstantVariance.forecast.resids (Python parameter) — Residuals to use in the recursion"><span class="n"><span class="pre">resids</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantVariance.forecast.backcast" title="arch.univariate.ConstantVariance.forecast.backcast (Python parameter) — Value to use when initializing the recursion"><span class="n"><span class="pre">backcast</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantVariance.forecast.var_bounds" title="arch.univariate.ConstantVariance.forecast.var_bounds (Python parameter) — Array containing columns of lower and upper bounds"><span class="n"><span class="pre">var_bounds</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantVariance.forecast.start" title="arch.univariate.ConstantVariance.forecast.start (Python parameter) — Index of the first observation to use as the starting point for the forecast.  Default is len(resids)."><span class="n"><span class="pre">start</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantVariance.forecast.horizon" title="arch.univariate.ConstantVariance.forecast.horizon (Python parameter) — Forecast horizon.  Must be 1 or larger.  Forecasts are produced for horizons in [1, horizon]."><span class="n"><span class="pre">horizon</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantVariance.forecast.method" title="arch.univariate.ConstantVariance.forecast.method (Python parameter) — Method to use when producing the forecast. The default is analytic."><span class="n"><span class="pre">method</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'analytic'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'simulation'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'bootstrap'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;analytic&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantVariance.forecast.simulations" title="arch.univariate.ConstantVariance.forecast.simulations (Python parameter) — Number of simulations to run when computing the forecast using either simulation or bootstrap."><span class="n"><span class="pre">simulations</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantVariance.forecast.rng" title="arch.univariate.ConstantVariance.forecast.rng (Python parameter) — Callable random number generator required if method is 'simulation'. Must take a single shape input and return random samples numbers with that shape."><span class="n"><span class="pre">rng</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.12)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantVariance.forecast.random_state" title="arch.univariate.ConstantVariance.forecast.random_state (Python parameter) — NumPy RandomState instance to use when method is 'bootstrap'"><span class="n"><span class="pre">random_state</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.26)"><span class="pre">RandomState</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><span class="pre">VarianceForecast</span></span></span></span><a class="headerlink" href="#arch.univariate.ConstantVariance.forecast" title="Link to this definition">¶</a></dt>
 <dd><p>Forecast volatility from the model</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.ConstantVariance.forecast-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.ConstantVariance.forecast-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.ConstantVariance.forecast.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.univariate.ConstantVariance.forecast.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Parameters required to forecast the volatility model</p>
+<dt id="arch.univariate.ConstantVariance.forecast.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a></span></span><a class="headerlink" href="#arch.univariate.ConstantVariance.forecast.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Parameters required to forecast the volatility model</p>
 </dd>
 <dt id="arch.univariate.ConstantVariance.forecast.resids"><span class="n sig-name">resids</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.ConstantVariance.forecast.resids" title="Permalink to this definition">¶</a></dt><dd><p>Residuals to use in the recursion</p>
 </dd>
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@@ -1289,7 +1289,7 @@ <h1 id="arch-univariate-constantvariance-name">arch.univariate.ConstantVariance.
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diff --git a/devel/univariate/generated/generated/arch.univariate.ConstantVariance.num_params.html b/devel/univariate/generated/generated/arch.univariate.ConstantVariance.num_params.html
index 69983a2c8f..4c3fee79ea 100644
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@@ -15,7 +15,7 @@
     
   
       
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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@@ -1289,7 +1289,7 @@ <h1 id="arch-univariate-constantvariance-num-params">arch.univariate.ConstantVar
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diff --git a/devel/univariate/generated/generated/arch.univariate.ConstantVariance.parameter_names.html b/devel/univariate/generated/generated/arch.univariate.ConstantVariance.parameter_names.html
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-        <title>arch.univariate.ConstantVariance.parameter_names - arch v6.2.1 (+21)</title>
+        <title>arch.univariate.ConstantVariance.parameter_names - arch v6.2.1 (+23)</title>
       
     
 
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@@ -102,7 +102,7 @@
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-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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@@ -189,10 +189,10 @@
 
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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@@ -1337,7 +1337,7 @@ <h1 id="arch-univariate-constantvariance-parameter-names">arch.univariate.Consta
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diff --git a/devel/univariate/generated/generated/arch.univariate.ConstantVariance.simulate.html b/devel/univariate/generated/generated/arch.univariate.ConstantVariance.simulate.html
index 69a3a05ad7..eeb6753d73 100644
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@@ -92,7 +92,7 @@
 
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 <h1 id="arch-univariate-constantvariance-simulate">arch.univariate.ConstantVariance.simulate<a class="headerlink" href="#arch-univariate-constantvariance-simulate" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.ConstantVariance.simulate">
-<span class="sig-prename descclassname"><span class="pre">ConstantVariance.</span></span><span class="sig-name descname"><span class="pre">simulate</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantVariance.simulate.parameters" title="arch.univariate.ConstantVariance.simulate.parameters (Python parameter) — Parameters required to simulate the volatility model"><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantVariance.simulate.nobs" title="arch.univariate.ConstantVariance.simulate.nobs (Python parameter) — Number of data points to simulate"><span class="n"><span class="pre">nobs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantVariance.simulate.rng" title="arch.univariate.ConstantVariance.simulate.rng (Python parameter) — Callable function that takes a single integer input and returns a vector of random numbers"><span class="n"><span class="pre">rng</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.12)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantVariance.simulate.burn" title="arch.univariate.ConstantVariance.simulate.burn (Python parameter) — Number of additional observations to generate when initializing the simulation"><span class="n"><span class="pre">burn</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">500</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantVariance.simulate.initial_value" title="arch.univariate.ConstantVariance.simulate.initial_value (Python parameter) — Scalar or array of initial values to use when initializing the simulation"><span class="n"><span class="pre">initial_value</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#ConstantVariance.simulate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.ConstantVariance.simulate" title="Link to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">ConstantVariance.</span></span><span class="sig-name descname"><span class="pre">simulate</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantVariance.simulate.parameters" title="arch.univariate.ConstantVariance.simulate.parameters (Python parameter) — Parameters required to simulate the volatility model"><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantVariance.simulate.nobs" title="arch.univariate.ConstantVariance.simulate.nobs (Python parameter) — Number of data points to simulate"><span class="n"><span class="pre">nobs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantVariance.simulate.rng" title="arch.univariate.ConstantVariance.simulate.rng (Python parameter) — Callable function that takes a single integer input and returns a vector of random numbers"><span class="n"><span class="pre">rng</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.12)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantVariance.simulate.burn" title="arch.univariate.ConstantVariance.simulate.burn (Python parameter) — Number of additional observations to generate when initializing the simulation"><span class="n"><span class="pre">burn</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">500</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ConstantVariance.simulate.initial_value" title="arch.univariate.ConstantVariance.simulate.initial_value (Python parameter) — Scalar or array of initial values to use when initializing the simulation"><span class="n"><span class="pre">initial_value</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#ConstantVariance.simulate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.ConstantVariance.simulate" title="Link to this definition">¶</a></dt>
 <dd><p>Simulate data from the model</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.ConstantVariance.simulate-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.ConstantVariance.simulate-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.ConstantVariance.simulate.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.univariate.ConstantVariance.simulate.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Parameters required to simulate the volatility model</p>
+<dt id="arch.univariate.ConstantVariance.simulate.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a></span></span><a class="headerlink" href="#arch.univariate.ConstantVariance.simulate.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Parameters required to simulate the volatility model</p>
 </dd>
 <dt id="arch.univariate.ConstantVariance.simulate.nobs"><span class="n sig-name">nobs</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a></span></span><a class="headerlink" href="#arch.univariate.ConstantVariance.simulate.nobs" title="Permalink to this definition">¶</a></dt><dd><p>Number of data points to simulate</p>
 </dd>
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+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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diff --git a/devel/univariate/generated/generated/arch.univariate.ConstantVariance.starting_values.html b/devel/univariate/generated/generated/arch.univariate.ConstantVariance.starting_values.html
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+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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@@ -15,7 +15,7 @@
     
   
       
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+    arch v6.2.1 (+23)
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index 8de207a464..c5fc4a34fe 100644
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@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.EGARCH.backcast - arch v6.2.1 (+21)</title>
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index c85820ef7f..fd06239d8b 100644
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+++ b/devel/univariate/generated/generated/arch.univariate.EGARCH.backcast_transform.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.EGARCH.backcast_transform - arch v6.2.1 (+21)</title>
+        <title>arch.univariate.EGARCH.backcast_transform - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
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@@ -92,7 +92,7 @@
 
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@@ -189,10 +189,10 @@
 
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+    arch v6.2.1 (+23)
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index 841268c3c1..2eafc7b45d 100644
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@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.EGARCH.bounds - arch v6.2.1 (+21)</title>
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@@ -47,7 +47,7 @@
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@@ -92,7 +92,7 @@
 
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
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index 90f167387d..e14c8f11ca 100644
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+++ b/devel/univariate/generated/generated/arch.univariate.EGARCH.compute_variance.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.EGARCH.compute_variance - arch v6.2.1 (+21)</title>
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@@ -47,7 +47,7 @@
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@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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+    arch v6.2.1 (+23)
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-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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diff --git a/devel/univariate/generated/generated/arch.univariate.EGARCH.constraints.html b/devel/univariate/generated/generated/arch.univariate.EGARCH.constraints.html
index 494792d391..3753c56743 100644
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@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.EGARCH.constraints - arch v6.2.1 (+21)</title>
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@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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 <h1 id="arch-univariate-egarch-forecast">arch.univariate.EGARCH.forecast<a class="headerlink" href="#arch-univariate-egarch-forecast" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.EGARCH.forecast">
-<span class="sig-prename descclassname"><span class="pre">EGARCH.</span></span><span class="sig-name descname"><span class="pre">forecast</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EGARCH.forecast.parameters" title="arch.univariate.EGARCH.forecast.parameters (Python parameter) — Parameters required to forecast the volatility model"><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EGARCH.forecast.resids" title="arch.univariate.EGARCH.forecast.resids (Python parameter) — Residuals to use in the recursion"><span class="n"><span class="pre">resids</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EGARCH.forecast.backcast" title="arch.univariate.EGARCH.forecast.backcast (Python parameter) — Value to use when initializing the recursion"><span class="n"><span class="pre">backcast</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EGARCH.forecast.var_bounds" title="arch.univariate.EGARCH.forecast.var_bounds (Python parameter) — Array containing columns of lower and upper bounds"><span class="n"><span class="pre">var_bounds</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EGARCH.forecast.start" title="arch.univariate.EGARCH.forecast.start (Python parameter) — Index of the first observation to use as the starting point for the forecast.  Default is len(resids)."><span class="n"><span class="pre">start</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EGARCH.forecast.horizon" title="arch.univariate.EGARCH.forecast.horizon (Python parameter) — Forecast horizon.  Must be 1 or larger.  Forecasts are produced for horizons in [1, horizon]."><span class="n"><span class="pre">horizon</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EGARCH.forecast.method" title="arch.univariate.EGARCH.forecast.method (Python parameter) — Method to use when producing the forecast. The default is analytic."><span class="n"><span class="pre">method</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'analytic'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'simulation'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'bootstrap'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;analytic&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EGARCH.forecast.simulations" title="arch.univariate.EGARCH.forecast.simulations (Python parameter) — Number of simulations to run when computing the forecast using either simulation or bootstrap."><span class="n"><span class="pre">simulations</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EGARCH.forecast.rng" title="arch.univariate.EGARCH.forecast.rng (Python parameter) — Callable random number generator required if method is 'simulation'. Must take a single shape input and return random samples numbers with that shape."><span class="n"><span class="pre">rng</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.12)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EGARCH.forecast.random_state" title="arch.univariate.EGARCH.forecast.random_state (Python parameter) — NumPy RandomState instance to use when method is 'bootstrap'"><span class="n"><span class="pre">random_state</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.26)"><span class="pre">RandomState</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><span class="pre">VarianceForecast</span></span></span></span><a class="headerlink" href="#arch.univariate.EGARCH.forecast" title="Link to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">EGARCH.</span></span><span class="sig-name descname"><span class="pre">forecast</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EGARCH.forecast.parameters" title="arch.univariate.EGARCH.forecast.parameters (Python parameter) — Parameters required to forecast the volatility model"><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EGARCH.forecast.resids" title="arch.univariate.EGARCH.forecast.resids (Python parameter) — Residuals to use in the recursion"><span class="n"><span class="pre">resids</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EGARCH.forecast.backcast" title="arch.univariate.EGARCH.forecast.backcast (Python parameter) — Value to use when initializing the recursion"><span class="n"><span class="pre">backcast</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EGARCH.forecast.var_bounds" title="arch.univariate.EGARCH.forecast.var_bounds (Python parameter) — Array containing columns of lower and upper bounds"><span class="n"><span class="pre">var_bounds</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EGARCH.forecast.start" title="arch.univariate.EGARCH.forecast.start (Python parameter) — Index of the first observation to use as the starting point for the forecast.  Default is len(resids)."><span class="n"><span class="pre">start</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EGARCH.forecast.horizon" title="arch.univariate.EGARCH.forecast.horizon (Python parameter) — Forecast horizon.  Must be 1 or larger.  Forecasts are produced for horizons in [1, horizon]."><span class="n"><span class="pre">horizon</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EGARCH.forecast.method" title="arch.univariate.EGARCH.forecast.method (Python parameter) — Method to use when producing the forecast. The default is analytic."><span class="n"><span class="pre">method</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'analytic'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'simulation'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'bootstrap'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;analytic&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EGARCH.forecast.simulations" title="arch.univariate.EGARCH.forecast.simulations (Python parameter) — Number of simulations to run when computing the forecast using either simulation or bootstrap."><span class="n"><span class="pre">simulations</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EGARCH.forecast.rng" title="arch.univariate.EGARCH.forecast.rng (Python parameter) — Callable random number generator required if method is 'simulation'. Must take a single shape input and return random samples numbers with that shape."><span class="n"><span class="pre">rng</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.12)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EGARCH.forecast.random_state" title="arch.univariate.EGARCH.forecast.random_state (Python parameter) — NumPy RandomState instance to use when method is 'bootstrap'"><span class="n"><span class="pre">random_state</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.26)"><span class="pre">RandomState</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><span class="pre">VarianceForecast</span></span></span></span><a class="headerlink" href="#arch.univariate.EGARCH.forecast" title="Link to this definition">¶</a></dt>
 <dd><p>Forecast volatility from the model</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.EGARCH.forecast-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.EGARCH.forecast-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.EGARCH.forecast.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.univariate.EGARCH.forecast.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Parameters required to forecast the volatility model</p>
+<dt id="arch.univariate.EGARCH.forecast.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a></span></span><a class="headerlink" href="#arch.univariate.EGARCH.forecast.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Parameters required to forecast the volatility model</p>
 </dd>
 <dt id="arch.univariate.EGARCH.forecast.resids"><span class="n sig-name">resids</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.EGARCH.forecast.resids" title="Permalink to this definition">¶</a></dt><dd><p>Residuals to use in the recursion</p>
 </dd>
@@ -1562,7 +1562,7 @@ <h1 id="arch-univariate-egarch-forecast">arch.univariate.EGARCH.forecast<a class
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diff --git a/devel/univariate/generated/generated/arch.univariate.EGARCH.name.html b/devel/univariate/generated/generated/arch.univariate.EGARCH.name.html
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-        <title>arch.univariate.EGARCH.name - arch v6.2.1 (+21)</title>
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+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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diff --git a/devel/univariate/generated/generated/arch.univariate.EGARCH.num_params.html b/devel/univariate/generated/generated/arch.univariate.EGARCH.num_params.html
index 5e510ececb..8211ecd5d0 100644
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+++ b/devel/univariate/generated/generated/arch.univariate.EGARCH.num_params.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.EGARCH.num_params - arch v6.2.1 (+21)</title>
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+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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-    arch v6.2.1 (+21)
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diff --git a/devel/univariate/generated/generated/arch.univariate.EGARCH.parameter_names.html b/devel/univariate/generated/generated/arch.univariate.EGARCH.parameter_names.html
index 25116d78a9..b2837ddd43 100644
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+++ b/devel/univariate/generated/generated/arch.univariate.EGARCH.parameter_names.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.EGARCH.parameter_names - arch v6.2.1 (+21)</title>
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-    arch v6.2.1 (+21)
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@@ -1293,12 +1293,12 @@
 <h1 id="arch-univariate-egarch-simulate">arch.univariate.EGARCH.simulate<a class="headerlink" href="#arch-univariate-egarch-simulate" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.EGARCH.simulate">
-<span class="sig-prename descclassname"><span class="pre">EGARCH.</span></span><span class="sig-name descname"><span class="pre">simulate</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EGARCH.simulate.parameters" title="arch.univariate.EGARCH.simulate.parameters (Python parameter) — Parameters required to simulate the volatility model"><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EGARCH.simulate.nobs" title="arch.univariate.EGARCH.simulate.nobs (Python parameter) — Number of data points to simulate"><span class="n"><span class="pre">nobs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EGARCH.simulate.rng" title="arch.univariate.EGARCH.simulate.rng (Python parameter) — Callable function that takes a single integer input and returns a vector of random numbers"><span class="n"><span class="pre">rng</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.12)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EGARCH.simulate.burn" title="arch.univariate.EGARCH.simulate.burn (Python parameter) — Number of additional observations to generate when initializing the simulation"><span class="n"><span class="pre">burn</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">500</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EGARCH.simulate.initial_value" title="arch.univariate.EGARCH.simulate.initial_value (Python parameter) — Scalar or array of initial values to use when initializing the simulation"><span class="n"><span class="pre">initial_value</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#EGARCH.simulate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.EGARCH.simulate" title="Link to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">EGARCH.</span></span><span class="sig-name descname"><span class="pre">simulate</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EGARCH.simulate.parameters" title="arch.univariate.EGARCH.simulate.parameters (Python parameter) — Parameters required to simulate the volatility model"><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EGARCH.simulate.nobs" title="arch.univariate.EGARCH.simulate.nobs (Python parameter) — Number of data points to simulate"><span class="n"><span class="pre">nobs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EGARCH.simulate.rng" title="arch.univariate.EGARCH.simulate.rng (Python parameter) — Callable function that takes a single integer input and returns a vector of random numbers"><span class="n"><span class="pre">rng</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.12)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EGARCH.simulate.burn" title="arch.univariate.EGARCH.simulate.burn (Python parameter) — Number of additional observations to generate when initializing the simulation"><span class="n"><span class="pre">burn</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">500</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EGARCH.simulate.initial_value" title="arch.univariate.EGARCH.simulate.initial_value (Python parameter) — Scalar or array of initial values to use when initializing the simulation"><span class="n"><span class="pre">initial_value</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#EGARCH.simulate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.EGARCH.simulate" title="Link to this definition">¶</a></dt>
 <dd><p>Simulate data from the model</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.EGARCH.simulate-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.EGARCH.simulate-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.EGARCH.simulate.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.univariate.EGARCH.simulate.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Parameters required to simulate the volatility model</p>
+<dt id="arch.univariate.EGARCH.simulate.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a></span></span><a class="headerlink" href="#arch.univariate.EGARCH.simulate.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Parameters required to simulate the volatility model</p>
 </dd>
 <dt id="arch.univariate.EGARCH.simulate.nobs"><span class="n sig-name">nobs</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a></span></span><a class="headerlink" href="#arch.univariate.EGARCH.simulate.nobs" title="Permalink to this definition">¶</a></dt><dd><p>Number of data points to simulate</p>
 </dd>
@@ -1437,7 +1437,7 @@ <h1 id="arch-univariate-egarch-simulate">arch.univariate.EGARCH.simulate<a class
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index e3c049681f..f8d817ee05 100644
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index f12bafb9a6..0d435d782a 100644
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@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.EGARCH.starting_values - arch v6.2.1 (+21)</title>
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+    arch v6.2.1 (+23)
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index 02020461c2..5d1de6fefb 100644
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@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.EGARCH.stop - arch v6.2.1 (+21)</title>
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@@ -47,7 +47,7 @@
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@@ -92,7 +92,7 @@
 
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
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+    arch v6.2.1 (+23)
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index fd7f4f4009..26f84e9b79 100644
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-        <title>arch.univariate.EGARCH.update - arch v6.2.1 (+21)</title>
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@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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+    arch v6.2.1 (+23)
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-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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index ca0a95d545..90fec54ed0 100644
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@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.EGARCH.updateable - arch v6.2.1 (+21)</title>
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-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
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index 2436585eeb..47da55d881 100644
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-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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@@ -1438,7 +1438,7 @@ <h1 id="arch-univariate-ewmavariance-bounds">arch.univariate.EWMAVariance.bounds
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diff --git a/devel/univariate/generated/generated/arch.univariate.EWMAVariance.compute_variance.html b/devel/univariate/generated/generated/arch.univariate.EWMAVariance.compute_variance.html
index c09b062ec0..237f56193e 100644
--- a/devel/univariate/generated/generated/arch.univariate.EWMAVariance.compute_variance.html
+++ b/devel/univariate/generated/generated/arch.univariate.EWMAVariance.compute_variance.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.EWMAVariance.compute_variance - arch v6.2.1 (+21)</title>
+        <title>arch.univariate.EWMAVariance.compute_variance - arch v6.2.1 (+23)</title>
       
     
 
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
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+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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@@ -1469,7 +1469,7 @@ <h1 id="arch-univariate-ewmavariance-compute-variance">arch.univariate.EWMAVaria
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diff --git a/devel/univariate/generated/generated/arch.univariate.EWMAVariance.constraints.html b/devel/univariate/generated/generated/arch.univariate.EWMAVariance.constraints.html
index e5c6790214..6bc21b2a73 100644
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@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.EWMAVariance.constraints - arch v6.2.1 (+21)</title>
+        <title>arch.univariate.EWMAVariance.constraints - arch v6.2.1 (+23)</title>
       
     
 
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+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
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+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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diff --git a/devel/univariate/generated/generated/arch.univariate.EWMAVariance.forecast.html b/devel/univariate/generated/generated/arch.univariate.EWMAVariance.forecast.html
index 6e8e943a13..3add64ffa3 100644
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-        <title>arch.univariate.EWMAVariance.forecast - arch v6.2.1 (+21)</title>
+        <title>arch.univariate.EWMAVariance.forecast - arch v6.2.1 (+23)</title>
       
     
 
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+            arch v6.2.1 (+23)
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-    arch v6.2.1 (+21)
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 <h1 id="arch-univariate-ewmavariance-forecast">arch.univariate.EWMAVariance.forecast<a class="headerlink" href="#arch-univariate-ewmavariance-forecast" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.EWMAVariance.forecast">
-<span class="sig-prename descclassname"><span class="pre">EWMAVariance.</span></span><span class="sig-name descname"><span class="pre">forecast</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EWMAVariance.forecast.parameters" title="arch.univariate.EWMAVariance.forecast.parameters (Python parameter) — Parameters required to forecast the volatility model"><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EWMAVariance.forecast.resids" title="arch.univariate.EWMAVariance.forecast.resids (Python parameter) — Residuals to use in the recursion"><span class="n"><span class="pre">resids</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EWMAVariance.forecast.backcast" title="arch.univariate.EWMAVariance.forecast.backcast (Python parameter) — Value to use when initializing the recursion"><span class="n"><span class="pre">backcast</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EWMAVariance.forecast.var_bounds" title="arch.univariate.EWMAVariance.forecast.var_bounds (Python parameter) — Array containing columns of lower and upper bounds"><span class="n"><span class="pre">var_bounds</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EWMAVariance.forecast.start" title="arch.univariate.EWMAVariance.forecast.start (Python parameter) — Index of the first observation to use as the starting point for the forecast.  Default is len(resids)."><span class="n"><span class="pre">start</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EWMAVariance.forecast.horizon" title="arch.univariate.EWMAVariance.forecast.horizon (Python parameter) — Forecast horizon.  Must be 1 or larger.  Forecasts are produced for horizons in [1, horizon]."><span class="n"><span class="pre">horizon</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EWMAVariance.forecast.method" title="arch.univariate.EWMAVariance.forecast.method (Python parameter) — Method to use when producing the forecast. The default is analytic."><span class="n"><span class="pre">method</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'analytic'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'simulation'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'bootstrap'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;analytic&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EWMAVariance.forecast.simulations" title="arch.univariate.EWMAVariance.forecast.simulations (Python parameter) — Number of simulations to run when computing the forecast using either simulation or bootstrap."><span class="n"><span class="pre">simulations</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EWMAVariance.forecast.rng" title="arch.univariate.EWMAVariance.forecast.rng (Python parameter) — Callable random number generator required if method is 'simulation'. Must take a single shape input and return random samples numbers with that shape."><span class="n"><span class="pre">rng</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.12)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EWMAVariance.forecast.random_state" title="arch.univariate.EWMAVariance.forecast.random_state (Python parameter) — NumPy RandomState instance to use when method is 'bootstrap'"><span class="n"><span class="pre">random_state</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.26)"><span class="pre">RandomState</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><span class="pre">VarianceForecast</span></span></span></span><a class="headerlink" href="#arch.univariate.EWMAVariance.forecast" title="Link to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">EWMAVariance.</span></span><span class="sig-name descname"><span class="pre">forecast</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EWMAVariance.forecast.parameters" title="arch.univariate.EWMAVariance.forecast.parameters (Python parameter) — Parameters required to forecast the volatility model"><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EWMAVariance.forecast.resids" title="arch.univariate.EWMAVariance.forecast.resids (Python parameter) — Residuals to use in the recursion"><span class="n"><span class="pre">resids</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EWMAVariance.forecast.backcast" title="arch.univariate.EWMAVariance.forecast.backcast (Python parameter) — Value to use when initializing the recursion"><span class="n"><span class="pre">backcast</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EWMAVariance.forecast.var_bounds" title="arch.univariate.EWMAVariance.forecast.var_bounds (Python parameter) — Array containing columns of lower and upper bounds"><span class="n"><span class="pre">var_bounds</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EWMAVariance.forecast.start" title="arch.univariate.EWMAVariance.forecast.start (Python parameter) — Index of the first observation to use as the starting point for the forecast.  Default is len(resids)."><span class="n"><span class="pre">start</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EWMAVariance.forecast.horizon" title="arch.univariate.EWMAVariance.forecast.horizon (Python parameter) — Forecast horizon.  Must be 1 or larger.  Forecasts are produced for horizons in [1, horizon]."><span class="n"><span class="pre">horizon</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EWMAVariance.forecast.method" title="arch.univariate.EWMAVariance.forecast.method (Python parameter) — Method to use when producing the forecast. The default is analytic."><span class="n"><span class="pre">method</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'analytic'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'simulation'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'bootstrap'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;analytic&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EWMAVariance.forecast.simulations" title="arch.univariate.EWMAVariance.forecast.simulations (Python parameter) — Number of simulations to run when computing the forecast using either simulation or bootstrap."><span class="n"><span class="pre">simulations</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EWMAVariance.forecast.rng" title="arch.univariate.EWMAVariance.forecast.rng (Python parameter) — Callable random number generator required if method is 'simulation'. Must take a single shape input and return random samples numbers with that shape."><span class="n"><span class="pre">rng</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.12)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EWMAVariance.forecast.random_state" title="arch.univariate.EWMAVariance.forecast.random_state (Python parameter) — NumPy RandomState instance to use when method is 'bootstrap'"><span class="n"><span class="pre">random_state</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.26)"><span class="pre">RandomState</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><span class="pre">VarianceForecast</span></span></span></span><a class="headerlink" href="#arch.univariate.EWMAVariance.forecast" title="Link to this definition">¶</a></dt>
 <dd><p>Forecast volatility from the model</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.EWMAVariance.forecast-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.EWMAVariance.forecast-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.EWMAVariance.forecast.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.univariate.EWMAVariance.forecast.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Parameters required to forecast the volatility model</p>
+<dt id="arch.univariate.EWMAVariance.forecast.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a></span></span><a class="headerlink" href="#arch.univariate.EWMAVariance.forecast.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Parameters required to forecast the volatility model</p>
 </dd>
 <dt id="arch.univariate.EWMAVariance.forecast.resids"><span class="n sig-name">resids</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.EWMAVariance.forecast.resids" title="Permalink to this definition">¶</a></dt><dd><p>Residuals to use in the recursion</p>
 </dd>
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@@ -1344,7 +1344,7 @@ <h1 id="arch-univariate-ewmavariance-name">arch.univariate.EWMAVariance.name<a c
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diff --git a/devel/univariate/generated/generated/arch.univariate.EWMAVariance.parameter_names.html b/devel/univariate/generated/generated/arch.univariate.EWMAVariance.parameter_names.html
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+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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-    arch v6.2.1 (+21)
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diff --git a/devel/univariate/generated/generated/arch.univariate.EWMAVariance.simulate.html b/devel/univariate/generated/generated/arch.univariate.EWMAVariance.simulate.html
index 61f525ad69..4f67c2a1c7 100644
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 <h1 id="arch-univariate-ewmavariance-simulate">arch.univariate.EWMAVariance.simulate<a class="headerlink" href="#arch-univariate-ewmavariance-simulate" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.EWMAVariance.simulate">
-<span class="sig-prename descclassname"><span class="pre">EWMAVariance.</span></span><span class="sig-name descname"><span class="pre">simulate</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EWMAVariance.simulate.parameters" title="arch.univariate.EWMAVariance.simulate.parameters (Python parameter) — Parameters required to simulate the volatility model"><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EWMAVariance.simulate.nobs" title="arch.univariate.EWMAVariance.simulate.nobs (Python parameter) — Number of data points to simulate"><span class="n"><span class="pre">nobs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EWMAVariance.simulate.rng" title="arch.univariate.EWMAVariance.simulate.rng (Python parameter) — Callable function that takes a single integer input and returns a vector of random numbers"><span class="n"><span class="pre">rng</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.12)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EWMAVariance.simulate.burn" title="arch.univariate.EWMAVariance.simulate.burn (Python parameter) — Number of additional observations to generate when initializing the simulation"><span class="n"><span class="pre">burn</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">500</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EWMAVariance.simulate.initial_value" title="arch.univariate.EWMAVariance.simulate.initial_value (Python parameter) — Scalar or array of initial values to use when initializing the simulation"><span class="n"><span class="pre">initial_value</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#EWMAVariance.simulate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.EWMAVariance.simulate" title="Link to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">EWMAVariance.</span></span><span class="sig-name descname"><span class="pre">simulate</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EWMAVariance.simulate.parameters" title="arch.univariate.EWMAVariance.simulate.parameters (Python parameter) — Parameters required to simulate the volatility model"><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EWMAVariance.simulate.nobs" title="arch.univariate.EWMAVariance.simulate.nobs (Python parameter) — Number of data points to simulate"><span class="n"><span class="pre">nobs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EWMAVariance.simulate.rng" title="arch.univariate.EWMAVariance.simulate.rng (Python parameter) — Callable function that takes a single integer input and returns a vector of random numbers"><span class="n"><span class="pre">rng</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.12)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EWMAVariance.simulate.burn" title="arch.univariate.EWMAVariance.simulate.burn (Python parameter) — Number of additional observations to generate when initializing the simulation"><span class="n"><span class="pre">burn</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">500</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.EWMAVariance.simulate.initial_value" title="arch.univariate.EWMAVariance.simulate.initial_value (Python parameter) — Scalar or array of initial values to use when initializing the simulation"><span class="n"><span class="pre">initial_value</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#EWMAVariance.simulate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.EWMAVariance.simulate" title="Link to this definition">¶</a></dt>
 <dd><p>Simulate data from the model</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.EWMAVariance.simulate-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.EWMAVariance.simulate-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.EWMAVariance.simulate.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.univariate.EWMAVariance.simulate.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Parameters required to simulate the volatility model</p>
+<dt id="arch.univariate.EWMAVariance.simulate.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a></span></span><a class="headerlink" href="#arch.univariate.EWMAVariance.simulate.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Parameters required to simulate the volatility model</p>
 </dd>
 <dt id="arch.univariate.EWMAVariance.simulate.nobs"><span class="n sig-name">nobs</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a></span></span><a class="headerlink" href="#arch.univariate.EWMAVariance.simulate.nobs" title="Permalink to this definition">¶</a></dt><dd><p>Number of data points to simulate</p>
 </dd>
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diff --git a/devel/univariate/generated/generated/arch.univariate.EWMAVariance.starting_values.html b/devel/univariate/generated/generated/arch.univariate.EWMAVariance.starting_values.html
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+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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@@ -15,7 +15,7 @@
     
   
       
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index e300058445..642a433da4 100644
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+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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diff --git a/devel/univariate/generated/generated/arch.univariate.FIGARCH.backcast.html b/devel/univariate/generated/generated/arch.univariate.FIGARCH.backcast.html
index c327e9c01e..83c9bee581 100644
--- a/devel/univariate/generated/generated/arch.univariate.FIGARCH.backcast.html
+++ b/devel/univariate/generated/generated/arch.univariate.FIGARCH.backcast.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.FIGARCH.backcast - arch v6.2.1 (+21)</title>
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@@ -47,7 +47,7 @@
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@@ -92,7 +92,7 @@
 
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@@ -102,7 +102,7 @@
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-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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@@ -1398,7 +1398,7 @@ <h1 id="arch-univariate-figarch-backcast">arch.univariate.FIGARCH.backcast<a cla
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-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
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diff --git a/devel/univariate/generated/generated/arch.univariate.FIGARCH.backcast_transform.html b/devel/univariate/generated/generated/arch.univariate.FIGARCH.backcast_transform.html
index 1464e89afd..ad4e6c3ede 100644
--- a/devel/univariate/generated/generated/arch.univariate.FIGARCH.backcast_transform.html
+++ b/devel/univariate/generated/generated/arch.univariate.FIGARCH.backcast_transform.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.FIGARCH.backcast_transform - arch v6.2.1 (+21)</title>
+        <title>arch.univariate.FIGARCH.backcast_transform - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
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@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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@@ -189,10 +189,10 @@
 
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   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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@@ -1398,7 +1398,7 @@ <h1 id="arch-univariate-figarch-backcast-transform">arch.univariate.FIGARCH.back
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-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
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   </body>
diff --git a/devel/univariate/generated/generated/arch.univariate.FIGARCH.bounds.html b/devel/univariate/generated/generated/arch.univariate.FIGARCH.bounds.html
index 895ddf82cc..e97581a746 100644
--- a/devel/univariate/generated/generated/arch.univariate.FIGARCH.bounds.html
+++ b/devel/univariate/generated/generated/arch.univariate.FIGARCH.bounds.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.FIGARCH.bounds - arch v6.2.1 (+21)</title>
+        <title>arch.univariate.FIGARCH.bounds - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
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-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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     <div class="md-nav__source">
@@ -1398,7 +1398,7 @@ <h1 id="arch-univariate-figarch-bounds">arch.univariate.FIGARCH.bounds<a class="
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-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
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diff --git a/devel/univariate/generated/generated/arch.univariate.FIGARCH.compute_variance.html b/devel/univariate/generated/generated/arch.univariate.FIGARCH.compute_variance.html
index 7b82d8d169..55f60198a6 100644
--- a/devel/univariate/generated/generated/arch.univariate.FIGARCH.compute_variance.html
+++ b/devel/univariate/generated/generated/arch.univariate.FIGARCH.compute_variance.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.FIGARCH.compute_variance - arch v6.2.1 (+21)</title>
+        <title>arch.univariate.FIGARCH.compute_variance - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
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   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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     <div class="md-nav__source">
@@ -1429,7 +1429,7 @@ <h1 id="arch-univariate-figarch-compute-variance">arch.univariate.FIGARCH.comput
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-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
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   </body>
diff --git a/devel/univariate/generated/generated/arch.univariate.FIGARCH.constraints.html b/devel/univariate/generated/generated/arch.univariate.FIGARCH.constraints.html
index 3bbd2c31b2..0337b1e9f3 100644
--- a/devel/univariate/generated/generated/arch.univariate.FIGARCH.constraints.html
+++ b/devel/univariate/generated/generated/arch.univariate.FIGARCH.constraints.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.FIGARCH.constraints - arch v6.2.1 (+21)</title>
+        <title>arch.univariate.FIGARCH.constraints - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
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-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
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   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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@@ -1343,7 +1343,7 @@ <h1 id="arch-univariate-figarch-constraints">arch.univariate.FIGARCH.constraints
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 <h1 id="arch-univariate-figarch-forecast">arch.univariate.FIGARCH.forecast<a class="headerlink" href="#arch-univariate-figarch-forecast" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.FIGARCH.forecast">
-<span class="sig-prename descclassname"><span class="pre">FIGARCH.</span></span><span class="sig-name descname"><span class="pre">forecast</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FIGARCH.forecast.parameters" title="arch.univariate.FIGARCH.forecast.parameters (Python parameter) — Parameters required to forecast the volatility model"><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FIGARCH.forecast.resids" title="arch.univariate.FIGARCH.forecast.resids (Python parameter) — Residuals to use in the recursion"><span class="n"><span class="pre">resids</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FIGARCH.forecast.backcast" title="arch.univariate.FIGARCH.forecast.backcast (Python parameter) — Value to use when initializing the recursion"><span class="n"><span class="pre">backcast</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FIGARCH.forecast.var_bounds" title="arch.univariate.FIGARCH.forecast.var_bounds (Python parameter) — Array containing columns of lower and upper bounds"><span class="n"><span class="pre">var_bounds</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FIGARCH.forecast.start" title="arch.univariate.FIGARCH.forecast.start (Python parameter) — Index of the first observation to use as the starting point for the forecast.  Default is len(resids)."><span class="n"><span class="pre">start</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FIGARCH.forecast.horizon" title="arch.univariate.FIGARCH.forecast.horizon (Python parameter) — Forecast horizon.  Must be 1 or larger.  Forecasts are produced for horizons in [1, horizon]."><span class="n"><span class="pre">horizon</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FIGARCH.forecast.method" title="arch.univariate.FIGARCH.forecast.method (Python parameter) — Method to use when producing the forecast. The default is analytic."><span class="n"><span class="pre">method</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'analytic'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'simulation'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'bootstrap'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;analytic&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FIGARCH.forecast.simulations" title="arch.univariate.FIGARCH.forecast.simulations (Python parameter) — Number of simulations to run when computing the forecast using either simulation or bootstrap."><span class="n"><span class="pre">simulations</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FIGARCH.forecast.rng" title="arch.univariate.FIGARCH.forecast.rng (Python parameter) — Callable random number generator required if method is 'simulation'. Must take a single shape input and return random samples numbers with that shape."><span class="n"><span class="pre">rng</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.12)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FIGARCH.forecast.random_state" title="arch.univariate.FIGARCH.forecast.random_state (Python parameter) — NumPy RandomState instance to use when method is 'bootstrap'"><span class="n"><span class="pre">random_state</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.26)"><span class="pre">RandomState</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><span class="pre">VarianceForecast</span></span></span></span><a class="headerlink" href="#arch.univariate.FIGARCH.forecast" title="Link to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">FIGARCH.</span></span><span class="sig-name descname"><span class="pre">forecast</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FIGARCH.forecast.parameters" title="arch.univariate.FIGARCH.forecast.parameters (Python parameter) — Parameters required to forecast the volatility model"><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FIGARCH.forecast.resids" title="arch.univariate.FIGARCH.forecast.resids (Python parameter) — Residuals to use in the recursion"><span class="n"><span class="pre">resids</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FIGARCH.forecast.backcast" title="arch.univariate.FIGARCH.forecast.backcast (Python parameter) — Value to use when initializing the recursion"><span class="n"><span class="pre">backcast</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FIGARCH.forecast.var_bounds" title="arch.univariate.FIGARCH.forecast.var_bounds (Python parameter) — Array containing columns of lower and upper bounds"><span class="n"><span class="pre">var_bounds</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FIGARCH.forecast.start" title="arch.univariate.FIGARCH.forecast.start (Python parameter) — Index of the first observation to use as the starting point for the forecast.  Default is len(resids)."><span class="n"><span class="pre">start</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FIGARCH.forecast.horizon" title="arch.univariate.FIGARCH.forecast.horizon (Python parameter) — Forecast horizon.  Must be 1 or larger.  Forecasts are produced for horizons in [1, horizon]."><span class="n"><span class="pre">horizon</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FIGARCH.forecast.method" title="arch.univariate.FIGARCH.forecast.method (Python parameter) — Method to use when producing the forecast. The default is analytic."><span class="n"><span class="pre">method</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'analytic'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'simulation'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'bootstrap'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;analytic&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FIGARCH.forecast.simulations" title="arch.univariate.FIGARCH.forecast.simulations (Python parameter) — Number of simulations to run when computing the forecast using either simulation or bootstrap."><span class="n"><span class="pre">simulations</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FIGARCH.forecast.rng" title="arch.univariate.FIGARCH.forecast.rng (Python parameter) — Callable random number generator required if method is 'simulation'. Must take a single shape input and return random samples numbers with that shape."><span class="n"><span class="pre">rng</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.12)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FIGARCH.forecast.random_state" title="arch.univariate.FIGARCH.forecast.random_state (Python parameter) — NumPy RandomState instance to use when method is 'bootstrap'"><span class="n"><span class="pre">random_state</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.26)"><span class="pre">RandomState</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><span class="pre">VarianceForecast</span></span></span></span><a class="headerlink" href="#arch.univariate.FIGARCH.forecast" title="Link to this definition">¶</a></dt>
 <dd><p>Forecast volatility from the model</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.FIGARCH.forecast-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.FIGARCH.forecast-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.FIGARCH.forecast.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.univariate.FIGARCH.forecast.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Parameters required to forecast the volatility model</p>
+<dt id="arch.univariate.FIGARCH.forecast.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a></span></span><a class="headerlink" href="#arch.univariate.FIGARCH.forecast.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Parameters required to forecast the volatility model</p>
 </dd>
 <dt id="arch.univariate.FIGARCH.forecast.resids"><span class="n sig-name">resids</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.FIGARCH.forecast.resids" title="Permalink to this definition">¶</a></dt><dd><p>Residuals to use in the recursion</p>
 </dd>
@@ -1577,7 +1577,7 @@ <h1 id="arch-univariate-figarch-forecast">arch.univariate.FIGARCH.forecast<a cla
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diff --git a/devel/univariate/generated/generated/arch.univariate.FIGARCH.name.html b/devel/univariate/generated/generated/arch.univariate.FIGARCH.name.html
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@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.FIGARCH.name - arch v6.2.1 (+21)</title>
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+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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diff --git a/devel/univariate/generated/generated/arch.univariate.FIGARCH.num_params.html b/devel/univariate/generated/generated/arch.univariate.FIGARCH.num_params.html
index 63d40f24a5..197f7177f7 100644
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+++ b/devel/univariate/generated/generated/arch.univariate.FIGARCH.num_params.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.FIGARCH.num_params - arch v6.2.1 (+21)</title>
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+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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@@ -102,7 +102,7 @@
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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-    arch v6.2.1 (+21)
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diff --git a/devel/univariate/generated/generated/arch.univariate.FIGARCH.parameter_names.html b/devel/univariate/generated/generated/arch.univariate.FIGARCH.parameter_names.html
index 8277ab2c52..f3f6c1dff0 100644
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+++ b/devel/univariate/generated/generated/arch.univariate.FIGARCH.parameter_names.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.FIGARCH.parameter_names - arch v6.2.1 (+21)</title>
+        <title>arch.univariate.FIGARCH.parameter_names - arch v6.2.1 (+23)</title>
       
     
 
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@@ -92,7 +92,7 @@
 
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
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-    arch v6.2.1 (+21)
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diff --git a/devel/univariate/generated/generated/arch.univariate.FIGARCH.simulate.html b/devel/univariate/generated/generated/arch.univariate.FIGARCH.simulate.html
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@@ -1308,12 +1308,12 @@
 <h1 id="arch-univariate-figarch-simulate">arch.univariate.FIGARCH.simulate<a class="headerlink" href="#arch-univariate-figarch-simulate" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.FIGARCH.simulate">
-<span class="sig-prename descclassname"><span class="pre">FIGARCH.</span></span><span class="sig-name descname"><span class="pre">simulate</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FIGARCH.simulate.parameters" title="arch.univariate.FIGARCH.simulate.parameters (Python parameter) — Parameters required to simulate the volatility model"><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FIGARCH.simulate.nobs" title="arch.univariate.FIGARCH.simulate.nobs (Python parameter) — Number of data points to simulate"><span class="n"><span class="pre">nobs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FIGARCH.simulate.rng" title="arch.univariate.FIGARCH.simulate.rng (Python parameter) — Callable function that takes a single integer input and returns a vector of random numbers"><span class="n"><span class="pre">rng</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.12)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FIGARCH.simulate.burn" title="arch.univariate.FIGARCH.simulate.burn (Python parameter) — Number of additional observations to generate when initializing the simulation"><span class="n"><span class="pre">burn</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">500</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FIGARCH.simulate.initial_value" title="arch.univariate.FIGARCH.simulate.initial_value (Python parameter) — Scalar or array of initial values to use when initializing the simulation"><span class="n"><span class="pre">initial_value</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#FIGARCH.simulate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.FIGARCH.simulate" title="Link to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">FIGARCH.</span></span><span class="sig-name descname"><span class="pre">simulate</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FIGARCH.simulate.parameters" title="arch.univariate.FIGARCH.simulate.parameters (Python parameter) — Parameters required to simulate the volatility model"><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FIGARCH.simulate.nobs" title="arch.univariate.FIGARCH.simulate.nobs (Python parameter) — Number of data points to simulate"><span class="n"><span class="pre">nobs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FIGARCH.simulate.rng" title="arch.univariate.FIGARCH.simulate.rng (Python parameter) — Callable function that takes a single integer input and returns a vector of random numbers"><span class="n"><span class="pre">rng</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.12)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FIGARCH.simulate.burn" title="arch.univariate.FIGARCH.simulate.burn (Python parameter) — Number of additional observations to generate when initializing the simulation"><span class="n"><span class="pre">burn</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">500</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FIGARCH.simulate.initial_value" title="arch.univariate.FIGARCH.simulate.initial_value (Python parameter) — Scalar or array of initial values to use when initializing the simulation"><span class="n"><span class="pre">initial_value</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#FIGARCH.simulate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.FIGARCH.simulate" title="Link to this definition">¶</a></dt>
 <dd><p>Simulate data from the model</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.FIGARCH.simulate-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.FIGARCH.simulate-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.FIGARCH.simulate.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.univariate.FIGARCH.simulate.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Parameters required to simulate the volatility model</p>
+<dt id="arch.univariate.FIGARCH.simulate.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a></span></span><a class="headerlink" href="#arch.univariate.FIGARCH.simulate.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Parameters required to simulate the volatility model</p>
 </dd>
 <dt id="arch.univariate.FIGARCH.simulate.nobs"><span class="n sig-name">nobs</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a></span></span><a class="headerlink" href="#arch.univariate.FIGARCH.simulate.nobs" title="Permalink to this definition">¶</a></dt><dd><p>Number of data points to simulate</p>
 </dd>
@@ -1452,7 +1452,7 @@ <h1 id="arch-univariate-figarch-simulate">arch.univariate.FIGARCH.simulate<a cla
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index 49cdbf386f..a1805386e8 100644
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+            arch v6.2.1 (+23)
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@@ -189,10 +189,10 @@
 
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index b3a5e929f1..063b18b74b 100644
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@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.FIGARCH.starting_values - arch v6.2.1 (+21)</title>
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@@ -47,7 +47,7 @@
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+            arch v6.2.1 (+23)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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+    arch v6.2.1 (+23)
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-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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index ac82d8e0c3..53f5680c32 100644
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@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.FIGARCH.stop - arch v6.2.1 (+21)</title>
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@@ -47,7 +47,7 @@
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@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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+            arch v6.2.1 (+23)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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+    arch v6.2.1 (+23)
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-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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index b08cad04ec..ce279540ec 100644
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@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.FIGARCH.truncation - arch v6.2.1 (+21)</title>
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@@ -47,7 +47,7 @@
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@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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@@ -102,7 +102,7 @@
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+            arch v6.2.1 (+23)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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+    arch v6.2.1 (+23)
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-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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index 3e539ef943..c95dfa78c1 100644
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+++ b/devel/univariate/generated/generated/arch.univariate.FIGARCH.update.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.FIGARCH.update - arch v6.2.1 (+21)</title>
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@@ -47,7 +47,7 @@
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@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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+    arch v6.2.1 (+23)
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index 43c8e33013..2a16a6a5ea 100644
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@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.FIGARCH.updateable - arch v6.2.1 (+21)</title>
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@@ -189,10 +189,10 @@
 
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+    arch v6.2.1 (+23)
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-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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index d6cf6fad89..fe49b8f8a9 100644
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@@ -15,7 +15,7 @@
     
   
       
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@@ -47,7 +47,7 @@
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@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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+            arch v6.2.1 (+23)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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@@ -1419,7 +1419,7 @@ <h1 id="arch-univariate-figarch-variance-bounds">arch.univariate.FIGARCH.varianc
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-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
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diff --git a/devel/univariate/generated/generated/arch.univariate.FIGARCH.volatility_updater.html b/devel/univariate/generated/generated/arch.univariate.FIGARCH.volatility_updater.html
index 64666f7917..46f1518f08 100644
--- a/devel/univariate/generated/generated/arch.univariate.FIGARCH.volatility_updater.html
+++ b/devel/univariate/generated/generated/arch.univariate.FIGARCH.volatility_updater.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.FIGARCH.volatility_updater - arch v6.2.1 (+21)</title>
+        <title>arch.univariate.FIGARCH.volatility_updater - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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diff --git a/devel/univariate/generated/generated/arch.univariate.FixedVariance.backcast.html b/devel/univariate/generated/generated/arch.univariate.FixedVariance.backcast.html
index 15731fc773..58e3e7089a 100644
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+++ b/devel/univariate/generated/generated/arch.univariate.FixedVariance.backcast.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.FixedVariance.backcast - arch v6.2.1 (+21)</title>
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@@ -47,7 +47,7 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
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@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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@@ -1423,7 +1423,7 @@ <h1 id="arch-univariate-fixedvariance-backcast">arch.univariate.FixedVariance.ba
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-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
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diff --git a/devel/univariate/generated/generated/arch.univariate.FixedVariance.backcast_transform.html b/devel/univariate/generated/generated/arch.univariate.FixedVariance.backcast_transform.html
index ebbd0afc32..05df4a4814 100644
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+++ b/devel/univariate/generated/generated/arch.univariate.FixedVariance.backcast_transform.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.FixedVariance.backcast_transform - arch v6.2.1 (+21)</title>
+        <title>arch.univariate.FixedVariance.backcast_transform - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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     <div class="md-nav__source">
@@ -1423,7 +1423,7 @@ <h1 id="arch-univariate-fixedvariance-backcast-transform">arch.univariate.FixedV
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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diff --git a/devel/univariate/generated/generated/arch.univariate.FixedVariance.bounds.html b/devel/univariate/generated/generated/arch.univariate.FixedVariance.bounds.html
index 5acd33c192..79ff2c7737 100644
--- a/devel/univariate/generated/generated/arch.univariate.FixedVariance.bounds.html
+++ b/devel/univariate/generated/generated/arch.univariate.FixedVariance.bounds.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.FixedVariance.bounds - arch v6.2.1 (+21)</title>
+        <title>arch.univariate.FixedVariance.bounds - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
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-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
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   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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     <div class="md-nav__source">
@@ -1423,7 +1423,7 @@ <h1 id="arch-univariate-fixedvariance-bounds">arch.univariate.FixedVariance.boun
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-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
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diff --git a/devel/univariate/generated/generated/arch.univariate.FixedVariance.compute_variance.html b/devel/univariate/generated/generated/arch.univariate.FixedVariance.compute_variance.html
index b24f58f2a6..9b856c6723 100644
--- a/devel/univariate/generated/generated/arch.univariate.FixedVariance.compute_variance.html
+++ b/devel/univariate/generated/generated/arch.univariate.FixedVariance.compute_variance.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.FixedVariance.compute_variance - arch v6.2.1 (+21)</title>
+        <title>arch.univariate.FixedVariance.compute_variance - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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     <div class="md-nav__source">
@@ -1454,7 +1454,7 @@ <h1 id="arch-univariate-fixedvariance-compute-variance">arch.univariate.FixedVar
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-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
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diff --git a/devel/univariate/generated/generated/arch.univariate.FixedVariance.constraints.html b/devel/univariate/generated/generated/arch.univariate.FixedVariance.constraints.html
index bcddb755ca..d97065f5c0 100644
--- a/devel/univariate/generated/generated/arch.univariate.FixedVariance.constraints.html
+++ b/devel/univariate/generated/generated/arch.univariate.FixedVariance.constraints.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.FixedVariance.constraints - arch v6.2.1 (+21)</title>
+        <title>arch.univariate.FixedVariance.constraints - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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     <div class="md-nav__source">
@@ -1368,7 +1368,7 @@ <h1 id="arch-univariate-fixedvariance-constraints">arch.univariate.FixedVariance
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-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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   </body>
diff --git a/devel/univariate/generated/generated/arch.univariate.FixedVariance.forecast.html b/devel/univariate/generated/generated/arch.univariate.FixedVariance.forecast.html
index 6dfa640ea5..da41ca7778 100644
--- a/devel/univariate/generated/generated/arch.univariate.FixedVariance.forecast.html
+++ b/devel/univariate/generated/generated/arch.univariate.FixedVariance.forecast.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.FixedVariance.forecast - arch v6.2.1 (+21)</title>
+        <title>arch.univariate.FixedVariance.forecast - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
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-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
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   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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     <div class="md-nav__source">
@@ -1431,12 +1431,12 @@
 <h1 id="arch-univariate-fixedvariance-forecast">arch.univariate.FixedVariance.forecast<a class="headerlink" href="#arch-univariate-fixedvariance-forecast" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.FixedVariance.forecast">
-<span class="sig-prename descclassname"><span class="pre">FixedVariance.</span></span><span class="sig-name descname"><span class="pre">forecast</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FixedVariance.forecast.parameters" title="arch.univariate.FixedVariance.forecast.parameters (Python parameter) — Parameters required to forecast the volatility model"><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FixedVariance.forecast.resids" title="arch.univariate.FixedVariance.forecast.resids (Python parameter) — Residuals to use in the recursion"><span class="n"><span class="pre">resids</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FixedVariance.forecast.backcast" title="arch.univariate.FixedVariance.forecast.backcast (Python parameter) — Value to use when initializing the recursion"><span class="n"><span class="pre">backcast</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FixedVariance.forecast.var_bounds" title="arch.univariate.FixedVariance.forecast.var_bounds (Python parameter) — Array containing columns of lower and upper bounds"><span class="n"><span class="pre">var_bounds</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FixedVariance.forecast.start" title="arch.univariate.FixedVariance.forecast.start (Python parameter) — Index of the first observation to use as the starting point for the forecast.  Default is len(resids)."><span class="n"><span class="pre">start</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FixedVariance.forecast.horizon" title="arch.univariate.FixedVariance.forecast.horizon (Python parameter) — Forecast horizon.  Must be 1 or larger.  Forecasts are produced for horizons in [1, horizon]."><span class="n"><span class="pre">horizon</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FixedVariance.forecast.method" title="arch.univariate.FixedVariance.forecast.method (Python parameter) — Method to use when producing the forecast. The default is analytic."><span class="n"><span class="pre">method</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'analytic'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'simulation'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'bootstrap'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;analytic&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FixedVariance.forecast.simulations" title="arch.univariate.FixedVariance.forecast.simulations (Python parameter) — Number of simulations to run when computing the forecast using either simulation or bootstrap."><span class="n"><span class="pre">simulations</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FixedVariance.forecast.rng" title="arch.univariate.FixedVariance.forecast.rng (Python parameter) — Callable random number generator required if method is 'simulation'. Must take a single shape input and return random samples numbers with that shape."><span class="n"><span class="pre">rng</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.12)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FixedVariance.forecast.random_state" title="arch.univariate.FixedVariance.forecast.random_state (Python parameter) — NumPy RandomState instance to use when method is 'bootstrap'"><span class="n"><span class="pre">random_state</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.26)"><span class="pre">RandomState</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><span class="pre">VarianceForecast</span></span></span></span><a class="headerlink" href="#arch.univariate.FixedVariance.forecast" title="Link to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">FixedVariance.</span></span><span class="sig-name descname"><span class="pre">forecast</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FixedVariance.forecast.parameters" title="arch.univariate.FixedVariance.forecast.parameters (Python parameter) — Parameters required to forecast the volatility model"><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FixedVariance.forecast.resids" title="arch.univariate.FixedVariance.forecast.resids (Python parameter) — Residuals to use in the recursion"><span class="n"><span class="pre">resids</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FixedVariance.forecast.backcast" title="arch.univariate.FixedVariance.forecast.backcast (Python parameter) — Value to use when initializing the recursion"><span class="n"><span class="pre">backcast</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FixedVariance.forecast.var_bounds" title="arch.univariate.FixedVariance.forecast.var_bounds (Python parameter) — Array containing columns of lower and upper bounds"><span class="n"><span class="pre">var_bounds</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FixedVariance.forecast.start" title="arch.univariate.FixedVariance.forecast.start (Python parameter) — Index of the first observation to use as the starting point for the forecast.  Default is len(resids)."><span class="n"><span class="pre">start</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FixedVariance.forecast.horizon" title="arch.univariate.FixedVariance.forecast.horizon (Python parameter) — Forecast horizon.  Must be 1 or larger.  Forecasts are produced for horizons in [1, horizon]."><span class="n"><span class="pre">horizon</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FixedVariance.forecast.method" title="arch.univariate.FixedVariance.forecast.method (Python parameter) — Method to use when producing the forecast. The default is analytic."><span class="n"><span class="pre">method</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'analytic'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'simulation'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'bootstrap'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;analytic&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FixedVariance.forecast.simulations" title="arch.univariate.FixedVariance.forecast.simulations (Python parameter) — Number of simulations to run when computing the forecast using either simulation or bootstrap."><span class="n"><span class="pre">simulations</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FixedVariance.forecast.rng" title="arch.univariate.FixedVariance.forecast.rng (Python parameter) — Callable random number generator required if method is 'simulation'. Must take a single shape input and return random samples numbers with that shape."><span class="n"><span class="pre">rng</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.12)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FixedVariance.forecast.random_state" title="arch.univariate.FixedVariance.forecast.random_state (Python parameter) — NumPy RandomState instance to use when method is 'bootstrap'"><span class="n"><span class="pre">random_state</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.26)"><span class="pre">RandomState</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><span class="pre">VarianceForecast</span></span></span></span><a class="headerlink" href="#arch.univariate.FixedVariance.forecast" title="Link to this definition">¶</a></dt>
 <dd><p>Forecast volatility from the model</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.FixedVariance.forecast-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.FixedVariance.forecast-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.FixedVariance.forecast.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.univariate.FixedVariance.forecast.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Parameters required to forecast the volatility model</p>
+<dt id="arch.univariate.FixedVariance.forecast.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a></span></span><a class="headerlink" href="#arch.univariate.FixedVariance.forecast.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Parameters required to forecast the volatility model</p>
 </dd>
 <dt id="arch.univariate.FixedVariance.forecast.resids"><span class="n sig-name">resids</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.FixedVariance.forecast.resids" title="Permalink to this definition">¶</a></dt><dd><p>Residuals to use in the recursion</p>
 </dd>
@@ -1602,7 +1602,7 @@ <h1 id="arch-univariate-fixedvariance-forecast">arch.univariate.FixedVariance.fo
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+    arch v6.2.1 (+23)
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diff --git a/devel/univariate/generated/generated/arch.univariate.FixedVariance.parameter_names.html b/devel/univariate/generated/generated/arch.univariate.FixedVariance.parameter_names.html
index 3c0465bced..b183d8658a 100644
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+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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diff --git a/devel/univariate/generated/generated/arch.univariate.FixedVariance.simulate.html b/devel/univariate/generated/generated/arch.univariate.FixedVariance.simulate.html
index 2112a41474..63fb1ada2c 100644
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@@ -47,7 +47,7 @@
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 <h1 id="arch-univariate-fixedvariance-simulate">arch.univariate.FixedVariance.simulate<a class="headerlink" href="#arch-univariate-fixedvariance-simulate" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.FixedVariance.simulate">
-<span class="sig-prename descclassname"><span class="pre">FixedVariance.</span></span><span class="sig-name descname"><span class="pre">simulate</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FixedVariance.simulate.parameters" title="arch.univariate.FixedVariance.simulate.parameters (Python parameter) — Parameters required to simulate the volatility model"><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FixedVariance.simulate.nobs" title="arch.univariate.FixedVariance.simulate.nobs (Python parameter) — Number of data points to simulate"><span class="n"><span class="pre">nobs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FixedVariance.simulate.rng" title="arch.univariate.FixedVariance.simulate.rng (Python parameter) — Callable function that takes a single integer input and returns a vector of random numbers"><span class="n"><span class="pre">rng</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.12)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FixedVariance.simulate.burn" title="arch.univariate.FixedVariance.simulate.burn (Python parameter) — Number of additional observations to generate when initializing the simulation"><span class="n"><span class="pre">burn</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">500</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FixedVariance.simulate.initial_value" title="arch.univariate.FixedVariance.simulate.initial_value (Python parameter) — Scalar or array of initial values to use when initializing the simulation"><span class="n"><span class="pre">initial_value</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#FixedVariance.simulate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.FixedVariance.simulate" title="Link to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">FixedVariance.</span></span><span class="sig-name descname"><span class="pre">simulate</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FixedVariance.simulate.parameters" title="arch.univariate.FixedVariance.simulate.parameters (Python parameter) — Parameters required to simulate the volatility model"><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FixedVariance.simulate.nobs" title="arch.univariate.FixedVariance.simulate.nobs (Python parameter) — Number of data points to simulate"><span class="n"><span class="pre">nobs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FixedVariance.simulate.rng" title="arch.univariate.FixedVariance.simulate.rng (Python parameter) — Callable function that takes a single integer input and returns a vector of random numbers"><span class="n"><span class="pre">rng</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.12)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FixedVariance.simulate.burn" title="arch.univariate.FixedVariance.simulate.burn (Python parameter) — Number of additional observations to generate when initializing the simulation"><span class="n"><span class="pre">burn</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">500</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.FixedVariance.simulate.initial_value" title="arch.univariate.FixedVariance.simulate.initial_value (Python parameter) — Scalar or array of initial values to use when initializing the simulation"><span class="n"><span class="pre">initial_value</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#FixedVariance.simulate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.FixedVariance.simulate" title="Link to this definition">¶</a></dt>
 <dd><p>Simulate data from the model</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.FixedVariance.simulate-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.FixedVariance.simulate-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.FixedVariance.simulate.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.univariate.FixedVariance.simulate.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Parameters required to simulate the volatility model</p>
+<dt id="arch.univariate.FixedVariance.simulate.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a></span></span><a class="headerlink" href="#arch.univariate.FixedVariance.simulate.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Parameters required to simulate the volatility model</p>
 </dd>
 <dt id="arch.univariate.FixedVariance.simulate.nobs"><span class="n sig-name">nobs</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a></span></span><a class="headerlink" href="#arch.univariate.FixedVariance.simulate.nobs" title="Permalink to this definition">¶</a></dt><dd><p>Number of data points to simulate</p>
 </dd>
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@@ -15,7 +15,7 @@
     
   
       
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+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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index 24b46459ff..d4171226ae 100644
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@@ -92,7 +92,7 @@
 
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index dd1845fc75..ecdc808da3 100644
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@@ -15,7 +15,7 @@
     
   
       
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-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
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index 7741830160..0e8426aab6 100644
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-        <title>arch.univariate.GARCH.bounds - arch v6.2.1 (+21)</title>
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@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
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+    arch v6.2.1 (+23)
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index fd33521a90..d28c3db4f9 100644
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@@ -15,7 +15,7 @@
     
   
       
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+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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+    arch v6.2.1 (+23)
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-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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index 29776a598b..b1969cfee8 100644
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@@ -92,7 +92,7 @@
 
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 <h1 id="arch-univariate-garch-forecast">arch.univariate.GARCH.forecast<a class="headerlink" href="#arch-univariate-garch-forecast" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.GARCH.forecast">
-<span class="sig-prename descclassname"><span class="pre">GARCH.</span></span><span class="sig-name descname"><span class="pre">forecast</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.GARCH.forecast.parameters" title="arch.univariate.GARCH.forecast.parameters (Python parameter) — Parameters required to forecast the volatility model"><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.GARCH.forecast.resids" title="arch.univariate.GARCH.forecast.resids (Python parameter) — Residuals to use in the recursion"><span class="n"><span class="pre">resids</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.GARCH.forecast.backcast" title="arch.univariate.GARCH.forecast.backcast (Python parameter) — Value to use when initializing the recursion"><span class="n"><span class="pre">backcast</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.GARCH.forecast.var_bounds" title="arch.univariate.GARCH.forecast.var_bounds (Python parameter) — Array containing columns of lower and upper bounds"><span class="n"><span class="pre">var_bounds</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.GARCH.forecast.start" title="arch.univariate.GARCH.forecast.start (Python parameter) — Index of the first observation to use as the starting point for the forecast.  Default is len(resids)."><span class="n"><span class="pre">start</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.GARCH.forecast.horizon" title="arch.univariate.GARCH.forecast.horizon (Python parameter) — Forecast horizon.  Must be 1 or larger.  Forecasts are produced for horizons in [1, horizon]."><span class="n"><span class="pre">horizon</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.GARCH.forecast.method" title="arch.univariate.GARCH.forecast.method (Python parameter) — Method to use when producing the forecast. The default is analytic."><span class="n"><span class="pre">method</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'analytic'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'simulation'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'bootstrap'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;analytic&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.GARCH.forecast.simulations" title="arch.univariate.GARCH.forecast.simulations (Python parameter) — Number of simulations to run when computing the forecast using either simulation or bootstrap."><span class="n"><span class="pre">simulations</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.GARCH.forecast.rng" title="arch.univariate.GARCH.forecast.rng (Python parameter) — Callable random number generator required if method is 'simulation'. Must take a single shape input and return random samples numbers with that shape."><span class="n"><span class="pre">rng</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.12)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.GARCH.forecast.random_state" title="arch.univariate.GARCH.forecast.random_state (Python parameter) — NumPy RandomState instance to use when method is 'bootstrap'"><span class="n"><span class="pre">random_state</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.26)"><span class="pre">RandomState</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><span class="pre">VarianceForecast</span></span></span></span><a class="headerlink" href="#arch.univariate.GARCH.forecast" title="Link to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">GARCH.</span></span><span class="sig-name descname"><span class="pre">forecast</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.GARCH.forecast.parameters" title="arch.univariate.GARCH.forecast.parameters (Python parameter) — Parameters required to forecast the volatility model"><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.GARCH.forecast.resids" title="arch.univariate.GARCH.forecast.resids (Python parameter) — Residuals to use in the recursion"><span class="n"><span class="pre">resids</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.GARCH.forecast.backcast" title="arch.univariate.GARCH.forecast.backcast (Python parameter) — Value to use when initializing the recursion"><span class="n"><span class="pre">backcast</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.GARCH.forecast.var_bounds" title="arch.univariate.GARCH.forecast.var_bounds (Python parameter) — Array containing columns of lower and upper bounds"><span class="n"><span class="pre">var_bounds</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.GARCH.forecast.start" title="arch.univariate.GARCH.forecast.start (Python parameter) — Index of the first observation to use as the starting point for the forecast.  Default is len(resids)."><span class="n"><span class="pre">start</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.GARCH.forecast.horizon" title="arch.univariate.GARCH.forecast.horizon (Python parameter) — Forecast horizon.  Must be 1 or larger.  Forecasts are produced for horizons in [1, horizon]."><span class="n"><span class="pre">horizon</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.GARCH.forecast.method" title="arch.univariate.GARCH.forecast.method (Python parameter) — Method to use when producing the forecast. The default is analytic."><span class="n"><span class="pre">method</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'analytic'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'simulation'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'bootstrap'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;analytic&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.GARCH.forecast.simulations" title="arch.univariate.GARCH.forecast.simulations (Python parameter) — Number of simulations to run when computing the forecast using either simulation or bootstrap."><span class="n"><span class="pre">simulations</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.GARCH.forecast.rng" title="arch.univariate.GARCH.forecast.rng (Python parameter) — Callable random number generator required if method is 'simulation'. Must take a single shape input and return random samples numbers with that shape."><span class="n"><span class="pre">rng</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.12)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.GARCH.forecast.random_state" title="arch.univariate.GARCH.forecast.random_state (Python parameter) — NumPy RandomState instance to use when method is 'bootstrap'"><span class="n"><span class="pre">random_state</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.26)"><span class="pre">RandomState</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><span class="pre">VarianceForecast</span></span></span></span><a class="headerlink" href="#arch.univariate.GARCH.forecast" title="Link to this definition">¶</a></dt>
 <dd><p>Forecast volatility from the model</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.GARCH.forecast-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.GARCH.forecast-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.GARCH.forecast.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.univariate.GARCH.forecast.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Parameters required to forecast the volatility model</p>
+<dt id="arch.univariate.GARCH.forecast.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a></span></span><a class="headerlink" href="#arch.univariate.GARCH.forecast.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Parameters required to forecast the volatility model</p>
 </dd>
 <dt id="arch.univariate.GARCH.forecast.resids"><span class="n sig-name">resids</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.GARCH.forecast.resids" title="Permalink to this definition">¶</a></dt><dd><p>Residuals to use in the recursion</p>
 </dd>
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@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.GARCH.name - arch v6.2.1 (+21)</title>
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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index 961a3eacd6..4af81eb476 100644
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@@ -15,7 +15,7 @@
     
   
       
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-    arch v6.2.1 (+21)
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index 4553c6ea23..c1ee854a7a 100644
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@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.GARCH.parameter_names - arch v6.2.1 (+21)</title>
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-    arch v6.2.1 (+21)
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@@ -1293,12 +1293,12 @@
 <h1 id="arch-univariate-garch-simulate">arch.univariate.GARCH.simulate<a class="headerlink" href="#arch-univariate-garch-simulate" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.GARCH.simulate">
-<span class="sig-prename descclassname"><span class="pre">GARCH.</span></span><span class="sig-name descname"><span class="pre">simulate</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.GARCH.simulate.parameters" title="arch.univariate.GARCH.simulate.parameters (Python parameter) — Parameters required to simulate the volatility model"><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.GARCH.simulate.nobs" title="arch.univariate.GARCH.simulate.nobs (Python parameter) — Number of data points to simulate"><span class="n"><span class="pre">nobs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.GARCH.simulate.rng" title="arch.univariate.GARCH.simulate.rng (Python parameter) — Callable function that takes a single integer input and returns a vector of random numbers"><span class="n"><span class="pre">rng</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.12)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.GARCH.simulate.burn" title="arch.univariate.GARCH.simulate.burn (Python parameter) — Number of additional observations to generate when initializing the simulation"><span class="n"><span class="pre">burn</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">500</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.GARCH.simulate.initial_value" title="arch.univariate.GARCH.simulate.initial_value (Python parameter) — Scalar or array of initial values to use when initializing the simulation"><span class="n"><span class="pre">initial_value</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#GARCH.simulate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.GARCH.simulate" title="Link to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">GARCH.</span></span><span class="sig-name descname"><span class="pre">simulate</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.GARCH.simulate.parameters" title="arch.univariate.GARCH.simulate.parameters (Python parameter) — Parameters required to simulate the volatility model"><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.GARCH.simulate.nobs" title="arch.univariate.GARCH.simulate.nobs (Python parameter) — Number of data points to simulate"><span class="n"><span class="pre">nobs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.GARCH.simulate.rng" title="arch.univariate.GARCH.simulate.rng (Python parameter) — Callable function that takes a single integer input and returns a vector of random numbers"><span class="n"><span class="pre">rng</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.12)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.GARCH.simulate.burn" title="arch.univariate.GARCH.simulate.burn (Python parameter) — Number of additional observations to generate when initializing the simulation"><span class="n"><span class="pre">burn</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">500</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.GARCH.simulate.initial_value" title="arch.univariate.GARCH.simulate.initial_value (Python parameter) — Scalar or array of initial values to use when initializing the simulation"><span class="n"><span class="pre">initial_value</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#GARCH.simulate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.GARCH.simulate" title="Link to this definition">¶</a></dt>
 <dd><p>Simulate data from the model</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.GARCH.simulate-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.GARCH.simulate-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.GARCH.simulate.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.univariate.GARCH.simulate.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Parameters required to simulate the volatility model</p>
+<dt id="arch.univariate.GARCH.simulate.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a></span></span><a class="headerlink" href="#arch.univariate.GARCH.simulate.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Parameters required to simulate the volatility model</p>
 </dd>
 <dt id="arch.univariate.GARCH.simulate.nobs"><span class="n sig-name">nobs</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a></span></span><a class="headerlink" href="#arch.univariate.GARCH.simulate.nobs" title="Permalink to this definition">¶</a></dt><dd><p>Number of data points to simulate</p>
 </dd>
@@ -1437,7 +1437,7 @@ <h1 id="arch-univariate-garch-simulate">arch.univariate.GARCH.simulate<a class="
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index bb5fe7088d..7788b00bdb 100644
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index 603dd63c31..9e3658e536 100644
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@@ -15,7 +15,7 @@
     
   
       
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@@ -47,7 +47,7 @@
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+    arch v6.2.1 (+23)
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index 68aa88a297..35f7738b55 100644
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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@@ -189,10 +189,10 @@
 
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+    arch v6.2.1 (+23)
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@@ -1354,7 +1354,7 @@ <h1 id="arch-univariate-garch-volatility-updater">arch.univariate.GARCH.volatili
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diff --git a/devel/univariate/generated/generated/arch.univariate.GeneralizedError.bounds.html b/devel/univariate/generated/generated/arch.univariate.GeneralizedError.bounds.html
index a662350c4c..920ebb7d08 100644
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@@ -15,7 +15,7 @@
     
   
       
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@@ -47,7 +47,7 @@
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@@ -92,7 +92,7 @@
 
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@@ -102,7 +102,7 @@
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@@ -189,10 +189,10 @@
 
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-    arch v6.2.1 (+21)
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diff --git a/devel/univariate/generated/generated/arch.univariate.GeneralizedError.cdf.html b/devel/univariate/generated/generated/arch.univariate.GeneralizedError.cdf.html
index a2ee667188..8871934b0c 100644
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+++ b/devel/univariate/generated/generated/arch.univariate.GeneralizedError.cdf.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.GeneralizedError.cdf - arch v6.2.1 (+21)</title>
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@@ -47,7 +47,7 @@
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@@ -92,7 +92,7 @@
 
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@@ -189,10 +189,10 @@
 
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@@ -1115,14 +1115,14 @@
 <h1 id="arch-univariate-generalizederror-cdf">arch.univariate.GeneralizedError.cdf<a class="headerlink" href="#arch-univariate-generalizederror-cdf" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.GeneralizedError.cdf">
-<span class="sig-prename descclassname"><span class="pre">GeneralizedError.</span></span><span class="sig-name descname"><span class="pre">cdf</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.GeneralizedError.cdf.resids" title="arch.univariate.GeneralizedError.cdf.resids (Python parameter) — Values at which to evaluate the cdf"><span class="n"><span class="pre">resids</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.GeneralizedError.cdf.parameters" title="arch.univariate.GeneralizedError.cdf.parameters (Python parameter) — Distribution parameters. Use None for parameterless distributions."><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/distribution.html#GeneralizedError.cdf"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.GeneralizedError.cdf" title="Link to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">GeneralizedError.</span></span><span class="sig-name descname"><span class="pre">cdf</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.GeneralizedError.cdf.resids" title="arch.univariate.GeneralizedError.cdf.resids (Python parameter) — Values at which to evaluate the cdf"><span class="n"><span class="pre">resids</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.GeneralizedError.cdf.parameters" title="arch.univariate.GeneralizedError.cdf.parameters (Python parameter) — Distribution parameters. Use None for parameterless distributions."><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/distribution.html#GeneralizedError.cdf"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.GeneralizedError.cdf" title="Link to this definition">¶</a></dt>
 <dd><p>Cumulative distribution function</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.GeneralizedError.cdf-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.GeneralizedError.cdf-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.GeneralizedError.cdf.resids"><span class="n sig-name">resids</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.univariate.GeneralizedError.cdf.resids" title="Permalink to this definition">¶</a></dt><dd><p>Values at which to evaluate the cdf</p>
+<dt id="arch.univariate.GeneralizedError.cdf.resids"><span class="n sig-name">resids</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a></span></span><a class="headerlink" href="#arch.univariate.GeneralizedError.cdf.resids" title="Permalink to this definition">¶</a></dt><dd><p>Values at which to evaluate the cdf</p>
 </dd>
-<dt id="arch.univariate.GeneralizedError.cdf.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.GeneralizedError.cdf.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Distribution parameters. Use <code class="docutils literal notranslate"><span class="pre">None</span></code> for parameterless
+<dt id="arch.univariate.GeneralizedError.cdf.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.GeneralizedError.cdf.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Distribution parameters. Use <code class="docutils literal notranslate"><span class="pre">None</span></code> for parameterless
 distributions.</p>
 </dd>
 </dl>
@@ -1250,7 +1250,7 @@ <h1 id="arch-univariate-generalizederror-cdf">arch.univariate.GeneralizedError.c
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@@ -15,7 +15,7 @@
     
   
       
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diff --git a/devel/univariate/generated/generated/arch.univariate.GeneralizedError.generator.html b/devel/univariate/generated/generated/arch.univariate.GeneralizedError.generator.html
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+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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@@ -1139,7 +1139,7 @@ <h1 id="arch-univariate-generalizederror-generator">arch.univariate.GeneralizedE
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@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.GeneralizedError.loglikelihood - arch v6.2.1 (+21)</title>
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 <h1 id="arch-univariate-generalizederror-loglikelihood">arch.univariate.GeneralizedError.loglikelihood<a class="headerlink" href="#arch-univariate-generalizederror-loglikelihood" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.GeneralizedError.loglikelihood">
-<span class="sig-prename descclassname"><span class="pre">GeneralizedError.</span></span><span class="sig-name descname"><span class="pre">loglikelihood</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.GeneralizedError.loglikelihood.parameters" title="arch.univariate.GeneralizedError.loglikelihood.parameters (Python parameter) — Shape parameter of the GED distribution"><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.GeneralizedError.loglikelihood.resids" title="arch.univariate.GeneralizedError.loglikelihood.resids (Python parameter) — The residuals to use in the log-likelihood calculation"><span class="n"><span class="pre">resids</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.GeneralizedError.loglikelihood.sigma2" title="arch.univariate.GeneralizedError.loglikelihood.sigma2 (Python parameter) — Conditional variances of resids"><span class="n"><span class="pre">sigma2</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.GeneralizedError.loglikelihood.individual" title="arch.univariate.GeneralizedError.loglikelihood.individual (Python parameter) — Flag indicating whether to return the vector of individual log likelihoods (True) or the sum (False)"><span class="n"><span class="pre">individual</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/distribution.html#GeneralizedError.loglikelihood"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.GeneralizedError.loglikelihood" title="Link to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">GeneralizedError.</span></span><span class="sig-name descname"><span class="pre">loglikelihood</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.GeneralizedError.loglikelihood.parameters" title="arch.univariate.GeneralizedError.loglikelihood.parameters (Python parameter) — Shape parameter of the GED distribution"><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.GeneralizedError.loglikelihood.resids" title="arch.univariate.GeneralizedError.loglikelihood.resids (Python parameter) — The residuals to use in the log-likelihood calculation"><span class="n"><span class="pre">resids</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.GeneralizedError.loglikelihood.sigma2" title="arch.univariate.GeneralizedError.loglikelihood.sigma2 (Python parameter) — Conditional variances of resids"><span class="n"><span class="pre">sigma2</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.GeneralizedError.loglikelihood.individual" title="arch.univariate.GeneralizedError.loglikelihood.individual (Python parameter) — Flag indicating whether to return the vector of individual log likelihoods (True) or the sum (False)"><span class="n"><span class="pre">individual</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/distribution.html#GeneralizedError.loglikelihood"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.GeneralizedError.loglikelihood" title="Link to this definition">¶</a></dt>
 <dd><p>Computes the log-likelihood of assuming residuals are have a
 Generalized Error Distribution, conditional on the variance.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.GeneralizedError.loglikelihood-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.GeneralizedError.loglikelihood-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.GeneralizedError.loglikelihood.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.univariate.GeneralizedError.loglikelihood.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Shape parameter of the GED distribution</p>
+<dt id="arch.univariate.GeneralizedError.loglikelihood.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a></span></span><a class="headerlink" href="#arch.univariate.GeneralizedError.loglikelihood.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Shape parameter of the GED distribution</p>
 </dd>
-<dt id="arch.univariate.GeneralizedError.loglikelihood.resids"><span class="n sig-name">resids</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.univariate.GeneralizedError.loglikelihood.resids" title="Permalink to this definition">¶</a></dt><dd><p>The residuals to use in the log-likelihood calculation</p>
+<dt id="arch.univariate.GeneralizedError.loglikelihood.resids"><span class="n sig-name">resids</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a></span></span><a class="headerlink" href="#arch.univariate.GeneralizedError.loglikelihood.resids" title="Permalink to this definition">¶</a></dt><dd><p>The residuals to use in the log-likelihood calculation</p>
 </dd>
-<dt id="arch.univariate.GeneralizedError.loglikelihood.sigma2"><span class="n sig-name">sigma2</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.univariate.GeneralizedError.loglikelihood.sigma2" title="Permalink to this definition">¶</a></dt><dd><p>Conditional variances of resids</p>
+<dt id="arch.univariate.GeneralizedError.loglikelihood.sigma2"><span class="n sig-name">sigma2</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a></span></span><a class="headerlink" href="#arch.univariate.GeneralizedError.loglikelihood.sigma2" title="Permalink to this definition">¶</a></dt><dd><p>Conditional variances of resids</p>
 </dd>
 <dt id="arch.univariate.GeneralizedError.loglikelihood.individual"><span class="n sig-name">individual</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)">bool</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code><a class="headerlink" href="#arch.univariate.GeneralizedError.loglikelihood.individual" title="Permalink to this definition">¶</a></dt><dd><p>Flag indicating whether to return the vector of individual log
 likelihoods (True) or the sum (False)</p>
@@ -1293,7 +1293,7 @@ <h1 id="arch-univariate-generalizederror-loglikelihood">arch.univariate.Generali
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diff --git a/devel/univariate/generated/generated/arch.univariate.GeneralizedError.moment.html b/devel/univariate/generated/generated/arch.univariate.GeneralizedError.moment.html
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 <h1 id="arch-univariate-generalizederror-moment">arch.univariate.GeneralizedError.moment<a class="headerlink" href="#arch-univariate-generalizederror-moment" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.GeneralizedError.moment">
-<span class="sig-prename descclassname"><span class="pre">GeneralizedError.</span></span><span class="sig-name descname"><span class="pre">moment</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.GeneralizedError.moment.n" title="arch.univariate.GeneralizedError.moment.n (Python parameter) — Order of moment"><span class="n"><span class="pre">n</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.GeneralizedError.moment.parameters" title="arch.univariate.GeneralizedError.moment.parameters (Python parameter) — Distribution parameters. Use None for parameterless distributions."><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/distribution.html#GeneralizedError.moment"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.GeneralizedError.moment" title="Link to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">GeneralizedError.</span></span><span class="sig-name descname"><span class="pre">moment</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.GeneralizedError.moment.n" title="arch.univariate.GeneralizedError.moment.n (Python parameter) — Order of moment"><span class="n"><span class="pre">n</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.GeneralizedError.moment.parameters" title="arch.univariate.GeneralizedError.moment.parameters (Python parameter) — Distribution parameters. Use None for parameterless distributions."><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/distribution.html#GeneralizedError.moment"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.GeneralizedError.moment" title="Link to this definition">¶</a></dt>
 <dd><p>Moment of order n</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.GeneralizedError.moment-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.GeneralizedError.moment-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
 <dt id="arch.univariate.GeneralizedError.moment.n"><span class="n sig-name">n</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a></span></span><a class="headerlink" href="#arch.univariate.GeneralizedError.moment.n" title="Permalink to this definition">¶</a></dt><dd><p>Order of moment</p>
 </dd>
-<dt id="arch.univariate.GeneralizedError.moment.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.GeneralizedError.moment.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Distribution parameters. Use None for parameterless distributions.</p>
+<dt id="arch.univariate.GeneralizedError.moment.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.GeneralizedError.moment.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Distribution parameters. Use None for parameterless distributions.</p>
 </dd>
 </dl>
 </dd>
@@ -1249,7 +1249,7 @@ <h1 id="arch-univariate-generalizederror-moment">arch.univariate.GeneralizedErro
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-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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diff --git a/devel/univariate/generated/generated/arch.univariate.GeneralizedError.parameter_names.html b/devel/univariate/generated/generated/arch.univariate.GeneralizedError.parameter_names.html
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diff --git a/devel/univariate/generated/generated/arch.univariate.GeneralizedError.partial_moment.html b/devel/univariate/generated/generated/arch.univariate.GeneralizedError.partial_moment.html
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 <h1 id="arch-univariate-generalizederror-partial-moment">arch.univariate.GeneralizedError.partial_moment<a class="headerlink" href="#arch-univariate-generalizederror-partial-moment" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.GeneralizedError.partial_moment">
-<span class="sig-prename descclassname"><span class="pre">GeneralizedError.</span></span><span class="sig-name descname"><span class="pre">partial_moment</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.GeneralizedError.partial_moment.n" title="arch.univariate.GeneralizedError.partial_moment.n (Python parameter) — Order of partial moment"><span class="n"><span class="pre">n</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.GeneralizedError.partial_moment.z" title="arch.univariate.GeneralizedError.partial_moment.z (Python parameter) — Upper bound for partial moment integral"><span class="n"><span class="pre">z</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mf">0.0</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.GeneralizedError.partial_moment.parameters" title="arch.univariate.GeneralizedError.partial_moment.parameters (Python parameter) — Distribution parameters.  Use None for parameterless distributions."><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/distribution.html#GeneralizedError.partial_moment"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.GeneralizedError.partial_moment" title="Link to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">GeneralizedError.</span></span><span class="sig-name descname"><span class="pre">partial_moment</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.GeneralizedError.partial_moment.n" title="arch.univariate.GeneralizedError.partial_moment.n (Python parameter) — Order of partial moment"><span class="n"><span class="pre">n</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.GeneralizedError.partial_moment.z" title="arch.univariate.GeneralizedError.partial_moment.z (Python parameter) — Upper bound for partial moment integral"><span class="n"><span class="pre">z</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mf">0.0</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.GeneralizedError.partial_moment.parameters" title="arch.univariate.GeneralizedError.partial_moment.parameters (Python parameter) — Distribution parameters.  Use None for parameterless distributions."><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/distribution.html#GeneralizedError.partial_moment"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.GeneralizedError.partial_moment" title="Link to this definition">¶</a></dt>
 <dd><p>Order n lower partial moment from -inf to z</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.GeneralizedError.partial_moment-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.GeneralizedError.partial_moment-parameters" title="Permalink to this headline">¶</a></dt>
@@ -1138,7 +1138,7 @@ <h1 id="arch-univariate-generalizederror-partial-moment">arch.univariate.General
 </dd>
 <dt id="arch.univariate.GeneralizedError.partial_moment.z"><span class="n sig-name">z</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mf">0.0</span></code><a class="headerlink" href="#arch.univariate.GeneralizedError.partial_moment.z" title="Permalink to this definition">¶</a></dt><dd><p>Upper bound for partial moment integral</p>
 </dd>
-<dt id="arch.univariate.GeneralizedError.partial_moment.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.GeneralizedError.partial_moment.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Distribution parameters.  Use None for parameterless distributions.</p>
+<dt id="arch.univariate.GeneralizedError.partial_moment.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.GeneralizedError.partial_moment.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Distribution parameters.  Use None for parameterless distributions.</p>
 </dd>
 </dl>
 </dd>
@@ -1278,7 +1278,7 @@ <h1 id="arch-univariate-generalizederror-partial-moment">arch.univariate.General
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diff --git a/devel/univariate/generated/generated/arch.univariate.GeneralizedError.ppf.html b/devel/univariate/generated/generated/arch.univariate.GeneralizedError.ppf.html
index 42eb0c3d44..46ac72d16e 100644
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+++ b/devel/univariate/generated/generated/arch.univariate.GeneralizedError.ppf.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.GeneralizedError.ppf - arch v6.2.1 (+21)</title>
+        <title>arch.univariate.GeneralizedError.ppf - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
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@@ -92,7 +92,7 @@
 
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-            arch v6.2.1 (+21)
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-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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 <h1 id="arch-univariate-generalizederror-ppf">arch.univariate.GeneralizedError.ppf<a class="headerlink" href="#arch-univariate-generalizederror-ppf" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.GeneralizedError.ppf">
-<span class="sig-prename descclassname"><span class="pre">GeneralizedError.</span></span><span class="sig-name descname"><span class="pre">ppf</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.GeneralizedError.ppf.pits" title="arch.univariate.GeneralizedError.ppf.pits (Python parameter) — Probability-integral-transformed values in the interval (0, 1)."><span class="n"><span class="pre">pits</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.GeneralizedError.ppf.parameters" title="arch.univariate.GeneralizedError.ppf.parameters (Python parameter) — Distribution parameters. Use None for parameterless distributions."><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/distribution.html#GeneralizedError.ppf"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.GeneralizedError.ppf" title="Link to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">GeneralizedError.</span></span><span class="sig-name descname"><span class="pre">ppf</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.GeneralizedError.ppf.pits" title="arch.univariate.GeneralizedError.ppf.pits (Python parameter) — Probability-integral-transformed values in the interval (0, 1)."><span class="n"><span class="pre">pits</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.GeneralizedError.ppf.parameters" title="arch.univariate.GeneralizedError.ppf.parameters (Python parameter) — Distribution parameters. Use None for parameterless distributions."><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/distribution.html#GeneralizedError.ppf"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.GeneralizedError.ppf" title="Link to this definition">¶</a></dt>
 <dd><p>Inverse cumulative density function (ICDF)</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.GeneralizedError.ppf-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.GeneralizedError.ppf-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.GeneralizedError.ppf.pits"><span class="n sig-name">pits</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.univariate.GeneralizedError.ppf.pits" title="Permalink to this definition">¶</a></dt><dd><p>Probability-integral-transformed values in the interval (0, 1).</p>
+<dt id="arch.univariate.GeneralizedError.ppf.pits"><span class="n sig-name">pits</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a></span></span><a class="headerlink" href="#arch.univariate.GeneralizedError.ppf.pits" title="Permalink to this definition">¶</a></dt><dd><p>Probability-integral-transformed values in the interval (0, 1).</p>
 </dd>
-<dt id="arch.univariate.GeneralizedError.ppf.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.GeneralizedError.ppf.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Distribution parameters. Use <code class="docutils literal notranslate"><span class="pre">None</span></code> for parameterless
+<dt id="arch.univariate.GeneralizedError.ppf.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.GeneralizedError.ppf.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Distribution parameters. Use <code class="docutils literal notranslate"><span class="pre">None</span></code> for parameterless
 distributions.</p>
 </dd>
 </dl>
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@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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@@ -1101,12 +1101,12 @@
 <h1 id="arch-univariate-generalizederror-simulate">arch.univariate.GeneralizedError.simulate<a class="headerlink" href="#arch-univariate-generalizederror-simulate" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.GeneralizedError.simulate">
-<span class="sig-prename descclassname"><span class="pre">GeneralizedError.</span></span><span class="sig-name descname"><span class="pre">simulate</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.GeneralizedError.simulate.parameters" title="arch.univariate.GeneralizedError.simulate.parameters (Python parameter) — Distribution parameters"><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.12)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/distribution.html#GeneralizedError.simulate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.GeneralizedError.simulate" title="Link to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">GeneralizedError.</span></span><span class="sig-name descname"><span class="pre">simulate</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.GeneralizedError.simulate.parameters" title="arch.univariate.GeneralizedError.simulate.parameters (Python parameter) — Distribution parameters"><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a></span></span></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.12)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/distribution.html#GeneralizedError.simulate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.GeneralizedError.simulate" title="Link to this definition">¶</a></dt>
 <dd><p>Simulates i.i.d. draws from the distribution</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.GeneralizedError.simulate-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.GeneralizedError.simulate-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.GeneralizedError.simulate.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.univariate.GeneralizedError.simulate.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Distribution parameters</p>
+<dt id="arch.univariate.GeneralizedError.simulate.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a></span></span><a class="headerlink" href="#arch.univariate.GeneralizedError.simulate.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Distribution parameters</p>
 </dd>
 </dl>
 </dd>
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diff --git a/devel/univariate/generated/generated/arch.univariate.GeneralizedError.starting_values.html b/devel/univariate/generated/generated/arch.univariate.GeneralizedError.starting_values.html
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@@ -15,7 +15,7 @@
     
   
       
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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diff --git a/devel/univariate/generated/generated/arch.univariate.HARCH.backcast.html b/devel/univariate/generated/generated/arch.univariate.HARCH.backcast.html
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+        <title>arch.univariate.HARCH.backcast - arch v6.2.1 (+23)</title>
       
     
 
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diff --git a/devel/univariate/generated/generated/arch.univariate.HARCH.backcast_transform.html b/devel/univariate/generated/generated/arch.univariate.HARCH.backcast_transform.html
index c73346c019..da9348fec6 100644
--- a/devel/univariate/generated/generated/arch.univariate.HARCH.backcast_transform.html
+++ b/devel/univariate/generated/generated/arch.univariate.HARCH.backcast_transform.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.HARCH.backcast_transform - arch v6.2.1 (+21)</title>
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@@ -47,7 +47,7 @@
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@@ -92,7 +92,7 @@
 
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@@ -102,7 +102,7 @@
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+            arch v6.2.1 (+23)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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@@ -1383,7 +1383,7 @@ <h1 id="arch-univariate-harch-backcast-transform">arch.univariate.HARCH.backcast
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-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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diff --git a/devel/univariate/generated/generated/arch.univariate.HARCH.bounds.html b/devel/univariate/generated/generated/arch.univariate.HARCH.bounds.html
index fb0f209c82..7a46dc5930 100644
--- a/devel/univariate/generated/generated/arch.univariate.HARCH.bounds.html
+++ b/devel/univariate/generated/generated/arch.univariate.HARCH.bounds.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.HARCH.bounds - arch v6.2.1 (+21)</title>
+        <title>arch.univariate.HARCH.bounds - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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     <div class="md-nav__source">
@@ -1383,7 +1383,7 @@ <h1 id="arch-univariate-harch-bounds">arch.univariate.HARCH.bounds<a class="head
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-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
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   </body>
diff --git a/devel/univariate/generated/generated/arch.univariate.HARCH.compute_variance.html b/devel/univariate/generated/generated/arch.univariate.HARCH.compute_variance.html
index 096f17335a..8de6edd9c2 100644
--- a/devel/univariate/generated/generated/arch.univariate.HARCH.compute_variance.html
+++ b/devel/univariate/generated/generated/arch.univariate.HARCH.compute_variance.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.HARCH.compute_variance - arch v6.2.1 (+21)</title>
+        <title>arch.univariate.HARCH.compute_variance - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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     </a>
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@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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     <div class="md-nav__source">
@@ -1414,7 +1414,7 @@ <h1 id="arch-univariate-harch-compute-variance">arch.univariate.HARCH.compute_va
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-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
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   </body>
diff --git a/devel/univariate/generated/generated/arch.univariate.HARCH.constraints.html b/devel/univariate/generated/generated/arch.univariate.HARCH.constraints.html
index af7ec14540..8d5e14b3b5 100644
--- a/devel/univariate/generated/generated/arch.univariate.HARCH.constraints.html
+++ b/devel/univariate/generated/generated/arch.univariate.HARCH.constraints.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.HARCH.constraints - arch v6.2.1 (+21)</title>
+        <title>arch.univariate.HARCH.constraints - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
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@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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     <div class="md-nav__source">
@@ -1328,7 +1328,7 @@ <h1 id="arch-univariate-harch-constraints">arch.univariate.HARCH.constraints<a c
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-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
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   </body>
diff --git a/devel/univariate/generated/generated/arch.univariate.HARCH.forecast.html b/devel/univariate/generated/generated/arch.univariate.HARCH.forecast.html
index 698f85b563..9bc305a673 100644
--- a/devel/univariate/generated/generated/arch.univariate.HARCH.forecast.html
+++ b/devel/univariate/generated/generated/arch.univariate.HARCH.forecast.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.HARCH.forecast - arch v6.2.1 (+21)</title>
+        <title>arch.univariate.HARCH.forecast - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
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+            arch v6.2.1 (+23)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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     <div class="md-nav__source">
@@ -1391,12 +1391,12 @@
 <h1 id="arch-univariate-harch-forecast">arch.univariate.HARCH.forecast<a class="headerlink" href="#arch-univariate-harch-forecast" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.HARCH.forecast">
-<span class="sig-prename descclassname"><span class="pre">HARCH.</span></span><span class="sig-name descname"><span class="pre">forecast</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARCH.forecast.parameters" title="arch.univariate.HARCH.forecast.parameters (Python parameter) — Parameters required to forecast the volatility model"><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARCH.forecast.resids" title="arch.univariate.HARCH.forecast.resids (Python parameter) — Residuals to use in the recursion"><span class="n"><span class="pre">resids</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARCH.forecast.backcast" title="arch.univariate.HARCH.forecast.backcast (Python parameter) — Value to use when initializing the recursion"><span class="n"><span class="pre">backcast</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARCH.forecast.var_bounds" title="arch.univariate.HARCH.forecast.var_bounds (Python parameter) — Array containing columns of lower and upper bounds"><span class="n"><span class="pre">var_bounds</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARCH.forecast.start" title="arch.univariate.HARCH.forecast.start (Python parameter) — Index of the first observation to use as the starting point for the forecast.  Default is len(resids)."><span class="n"><span class="pre">start</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARCH.forecast.horizon" title="arch.univariate.HARCH.forecast.horizon (Python parameter) — Forecast horizon.  Must be 1 or larger.  Forecasts are produced for horizons in [1, horizon]."><span class="n"><span class="pre">horizon</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARCH.forecast.method" title="arch.univariate.HARCH.forecast.method (Python parameter) — Method to use when producing the forecast. The default is analytic."><span class="n"><span class="pre">method</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'analytic'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'simulation'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'bootstrap'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;analytic&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARCH.forecast.simulations" title="arch.univariate.HARCH.forecast.simulations (Python parameter) — Number of simulations to run when computing the forecast using either simulation or bootstrap."><span class="n"><span class="pre">simulations</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARCH.forecast.rng" title="arch.univariate.HARCH.forecast.rng (Python parameter) — Callable random number generator required if method is 'simulation'. Must take a single shape input and return random samples numbers with that shape."><span class="n"><span class="pre">rng</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.12)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARCH.forecast.random_state" title="arch.univariate.HARCH.forecast.random_state (Python parameter) — NumPy RandomState instance to use when method is 'bootstrap'"><span class="n"><span class="pre">random_state</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.26)"><span class="pre">RandomState</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><span class="pre">VarianceForecast</span></span></span></span><a class="headerlink" href="#arch.univariate.HARCH.forecast" title="Link to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">HARCH.</span></span><span class="sig-name descname"><span class="pre">forecast</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARCH.forecast.parameters" title="arch.univariate.HARCH.forecast.parameters (Python parameter) — Parameters required to forecast the volatility model"><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARCH.forecast.resids" title="arch.univariate.HARCH.forecast.resids (Python parameter) — Residuals to use in the recursion"><span class="n"><span class="pre">resids</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARCH.forecast.backcast" title="arch.univariate.HARCH.forecast.backcast (Python parameter) — Value to use when initializing the recursion"><span class="n"><span class="pre">backcast</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARCH.forecast.var_bounds" title="arch.univariate.HARCH.forecast.var_bounds (Python parameter) — Array containing columns of lower and upper bounds"><span class="n"><span class="pre">var_bounds</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARCH.forecast.start" title="arch.univariate.HARCH.forecast.start (Python parameter) — Index of the first observation to use as the starting point for the forecast.  Default is len(resids)."><span class="n"><span class="pre">start</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARCH.forecast.horizon" title="arch.univariate.HARCH.forecast.horizon (Python parameter) — Forecast horizon.  Must be 1 or larger.  Forecasts are produced for horizons in [1, horizon]."><span class="n"><span class="pre">horizon</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARCH.forecast.method" title="arch.univariate.HARCH.forecast.method (Python parameter) — Method to use when producing the forecast. The default is analytic."><span class="n"><span class="pre">method</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'analytic'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'simulation'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'bootstrap'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;analytic&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARCH.forecast.simulations" title="arch.univariate.HARCH.forecast.simulations (Python parameter) — Number of simulations to run when computing the forecast using either simulation or bootstrap."><span class="n"><span class="pre">simulations</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARCH.forecast.rng" title="arch.univariate.HARCH.forecast.rng (Python parameter) — Callable random number generator required if method is 'simulation'. Must take a single shape input and return random samples numbers with that shape."><span class="n"><span class="pre">rng</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.12)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARCH.forecast.random_state" title="arch.univariate.HARCH.forecast.random_state (Python parameter) — NumPy RandomState instance to use when method is 'bootstrap'"><span class="n"><span class="pre">random_state</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.26)"><span class="pre">RandomState</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><span class="pre">VarianceForecast</span></span></span></span><a class="headerlink" href="#arch.univariate.HARCH.forecast" title="Link to this definition">¶</a></dt>
 <dd><p>Forecast volatility from the model</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.HARCH.forecast-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.HARCH.forecast-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.HARCH.forecast.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.univariate.HARCH.forecast.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Parameters required to forecast the volatility model</p>
+<dt id="arch.univariate.HARCH.forecast.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a></span></span><a class="headerlink" href="#arch.univariate.HARCH.forecast.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Parameters required to forecast the volatility model</p>
 </dd>
 <dt id="arch.univariate.HARCH.forecast.resids"><span class="n sig-name">resids</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.HARCH.forecast.resids" title="Permalink to this definition">¶</a></dt><dd><p>Residuals to use in the recursion</p>
 </dd>
@@ -1562,7 +1562,7 @@ <h1 id="arch-univariate-harch-forecast">arch.univariate.HARCH.forecast<a class="
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diff --git a/devel/univariate/generated/generated/arch.univariate.HARCH.name.html b/devel/univariate/generated/generated/arch.univariate.HARCH.name.html
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index 3c121e7576..ae5c49e1bc 100644
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+    arch v6.2.1 (+23)
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diff --git a/devel/univariate/generated/generated/arch.univariate.HARCH.parameter_names.html b/devel/univariate/generated/generated/arch.univariate.HARCH.parameter_names.html
index 63898bc01a..c909b597f4 100644
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@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.HARCH.parameter_names - arch v6.2.1 (+21)</title>
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@@ -47,7 +47,7 @@
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+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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diff --git a/devel/univariate/generated/generated/arch.univariate.HARCH.simulate.html b/devel/univariate/generated/generated/arch.univariate.HARCH.simulate.html
index dbe257fca1..4158865516 100644
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@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.HARCH.simulate - arch v6.2.1 (+21)</title>
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@@ -1293,12 +1293,12 @@
 <h1 id="arch-univariate-harch-simulate">arch.univariate.HARCH.simulate<a class="headerlink" href="#arch-univariate-harch-simulate" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.HARCH.simulate">
-<span class="sig-prename descclassname"><span class="pre">HARCH.</span></span><span class="sig-name descname"><span class="pre">simulate</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARCH.simulate.parameters" title="arch.univariate.HARCH.simulate.parameters (Python parameter) — Parameters required to simulate the volatility model"><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARCH.simulate.nobs" title="arch.univariate.HARCH.simulate.nobs (Python parameter) — Number of data points to simulate"><span class="n"><span class="pre">nobs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARCH.simulate.rng" title="arch.univariate.HARCH.simulate.rng (Python parameter) — Callable function that takes a single integer input and returns a vector of random numbers"><span class="n"><span class="pre">rng</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.12)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARCH.simulate.burn" title="arch.univariate.HARCH.simulate.burn (Python parameter) — Number of additional observations to generate when initializing the simulation"><span class="n"><span class="pre">burn</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">500</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARCH.simulate.initial_value" title="arch.univariate.HARCH.simulate.initial_value (Python parameter) — Scalar or array of initial values to use when initializing the simulation"><span class="n"><span class="pre">initial_value</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#HARCH.simulate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.HARCH.simulate" title="Link to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">HARCH.</span></span><span class="sig-name descname"><span class="pre">simulate</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARCH.simulate.parameters" title="arch.univariate.HARCH.simulate.parameters (Python parameter) — Parameters required to simulate the volatility model"><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARCH.simulate.nobs" title="arch.univariate.HARCH.simulate.nobs (Python parameter) — Number of data points to simulate"><span class="n"><span class="pre">nobs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARCH.simulate.rng" title="arch.univariate.HARCH.simulate.rng (Python parameter) — Callable function that takes a single integer input and returns a vector of random numbers"><span class="n"><span class="pre">rng</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.12)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARCH.simulate.burn" title="arch.univariate.HARCH.simulate.burn (Python parameter) — Number of additional observations to generate when initializing the simulation"><span class="n"><span class="pre">burn</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">500</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARCH.simulate.initial_value" title="arch.univariate.HARCH.simulate.initial_value (Python parameter) — Scalar or array of initial values to use when initializing the simulation"><span class="n"><span class="pre">initial_value</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#HARCH.simulate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.HARCH.simulate" title="Link to this definition">¶</a></dt>
 <dd><p>Simulate data from the model</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.HARCH.simulate-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.HARCH.simulate-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.HARCH.simulate.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.univariate.HARCH.simulate.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Parameters required to simulate the volatility model</p>
+<dt id="arch.univariate.HARCH.simulate.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a></span></span><a class="headerlink" href="#arch.univariate.HARCH.simulate.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Parameters required to simulate the volatility model</p>
 </dd>
 <dt id="arch.univariate.HARCH.simulate.nobs"><span class="n sig-name">nobs</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a></span></span><a class="headerlink" href="#arch.univariate.HARCH.simulate.nobs" title="Permalink to this definition">¶</a></dt><dd><p>Number of data points to simulate</p>
 </dd>
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index 345ad6c605..cf98136f3b 100644
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@@ -15,7 +15,7 @@
     
   
       
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index 3d6fe7dc4c..901c6765ff 100644
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@@ -15,7 +15,7 @@
     
   
       
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+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
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index 58f78d85fa..4b51b91752 100644
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@@ -15,7 +15,7 @@
     
   
       
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+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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index deb86dae18..404372f477 100644
--- a/devel/univariate/generated/generated/arch.univariate.HARCH.volatility_updater.html
+++ b/devel/univariate/generated/generated/arch.univariate.HARCH.volatility_updater.html
@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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 <h1 id="arch-univariate-harx-fit">arch.univariate.HARX.fit<a class="headerlink" href="#arch-univariate-harx-fit" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.HARX.fit">
-<span class="sig-prename descclassname"><span class="pre">HARX.</span></span><span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARX.fit.update_freq" title="arch.univariate.HARX.fit.update_freq (Python parameter) — Frequency of iteration updates.  Output is generated every update_freq iterations. Set to 0 to disable iterative output."><span class="n"><span class="pre">update_freq</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARX.fit.disp" title="arch.univariate.HARX.fit.disp (Python parameter) — Either 'final' to print optimization result or 'off' to display nothing. If using a boolean, False is &quot;off&quot; and True is &quot;final&quot;"><span class="n"><span class="pre">disp</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'off'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'final'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;final&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARX.fit.starting_values" title="arch.univariate.HARX.fit.starting_values (Python parameter) — Array of starting values to use.  If not provided, starting values are constructed by the model components."><span class="n"><span class="pre">starting_values</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARX.fit.cov_type" title="arch.univariate.HARX.fit.cov_type (Python parameter) — Estimation method of parameter covariance.  Supported options are 'robust', which does not assume the Information Matrix Equality holds and 'classic' which does.  In the ARCH literature, 'robust' corresponds to Bollerslev-Wooldridge covariance estimator."><span class="n"><span class="pre">cov_type</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'robust'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'classic'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;robust&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARX.fit.show_warning" title="arch.univariate.HARX.fit.show_warning (Python parameter) — Flag indicating whether convergence warnings should be shown."><span class="n"><span class="pre">show_warning</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARX.fit.first_obs" title="arch.univariate.HARX.fit.first_obs (Python parameter) — First observation to use when estimating model"><span class="n"><span class="pre">first_obs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.12)"><span class="pre">datetime</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)"><span class="pre">datetime64</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.1)"><span class="pre">Timestamp</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARX.fit.last_obs" title="arch.univariate.HARX.fit.last_obs (Python parameter) — Last observation to use when estimating model"><span class="n"><span class="pre">last_obs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.12)"><span class="pre">datetime</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)"><span class="pre">datetime64</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.1)"><span class="pre">Timestamp</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARX.fit.tol" title="arch.univariate.HARX.fit.tol (Python parameter) — Tolerance for termination."><span class="n"><span class="pre">tol</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARX.fit.options" title="arch.univariate.HARX.fit.options (Python parameter) — Options to pass to scipy.optimize.minimize.  Valid entries include 'ftol', 'eps', 'disp', and 'maxiter'."><span class="n"><span class="pre">options</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.12)"><span class="pre">Any</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARX.fit.backcast" title="arch.univariate.HARX.fit.backcast (Python parameter) — Value to use as backcast. Should be measure \sigma^2_0 since model-specific non-linear transformations are applied to value before computing the variance recursions."><span class="n"><span class="pre">backcast</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference internal" href="../arch.univariate.base.ARCHModelResult.html#arch.univariate.base.ARCHModelResult" title="arch.univariate.base.ARCHModelResult (Python class) — Results from estimation of an ARCHModel model"><span class="pre">ARCHModelResult</span></a></span></span></span><a class="headerlink" href="#arch.univariate.HARX.fit" title="Link to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">HARX.</span></span><span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARX.fit.update_freq" title="arch.univariate.HARX.fit.update_freq (Python parameter) — Frequency of iteration updates.  Output is generated every update_freq iterations. Set to 0 to disable iterative output."><span class="n"><span class="pre">update_freq</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARX.fit.disp" title="arch.univariate.HARX.fit.disp (Python parameter) — Either 'final' to print optimization result or 'off' to display nothing. If using a boolean, False is &quot;off&quot; and True is &quot;final&quot;"><span class="n"><span class="pre">disp</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'off'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'final'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;final&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARX.fit.starting_values" title="arch.univariate.HARX.fit.starting_values (Python parameter) — Array of starting values to use.  If not provided, starting values are constructed by the model components."><span class="n"><span class="pre">starting_values</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARX.fit.cov_type" title="arch.univariate.HARX.fit.cov_type (Python parameter) — Estimation method of parameter covariance.  Supported options are 'robust', which does not assume the Information Matrix Equality holds and 'classic' which does.  In the ARCH literature, 'robust' corresponds to Bollerslev-Wooldridge covariance estimator."><span class="n"><span class="pre">cov_type</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'robust'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'classic'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;robust&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARX.fit.show_warning" title="arch.univariate.HARX.fit.show_warning (Python parameter) — Flag indicating whether convergence warnings should be shown."><span class="n"><span class="pre">show_warning</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARX.fit.first_obs" title="arch.univariate.HARX.fit.first_obs (Python parameter) — First observation to use when estimating model"><span class="n"><span class="pre">first_obs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.12)"><span class="pre">datetime</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)"><span class="pre">datetime64</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.4)"><span class="pre">Timestamp</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARX.fit.last_obs" title="arch.univariate.HARX.fit.last_obs (Python parameter) — Last observation to use when estimating model"><span class="n"><span class="pre">last_obs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.12)"><span class="pre">datetime</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)"><span class="pre">datetime64</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.4)"><span class="pre">Timestamp</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARX.fit.tol" title="arch.univariate.HARX.fit.tol (Python parameter) — Tolerance for termination."><span class="n"><span class="pre">tol</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARX.fit.options" title="arch.univariate.HARX.fit.options (Python parameter) — Options to pass to scipy.optimize.minimize.  Valid entries include 'ftol', 'eps', 'disp', and 'maxiter'."><span class="n"><span class="pre">options</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.12)"><span class="pre">Any</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARX.fit.backcast" title="arch.univariate.HARX.fit.backcast (Python parameter) — Value to use as backcast. Should be measure \sigma^2_0 since model-specific non-linear transformations are applied to value before computing the variance recursions."><span class="n"><span class="pre">backcast</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference internal" href="../arch.univariate.base.ARCHModelResult.html#arch.univariate.base.ARCHModelResult" title="arch.univariate.base.ARCHModelResult (Python class) — Results from estimation of an ARCHModel model"><span class="pre">ARCHModelResult</span></a></span></span></span><a class="headerlink" href="#arch.univariate.HARX.fit" title="Link to this definition">¶</a></dt>
 <dd><p>Estimate model parameters</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.HARX.fit-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.HARX.fit-parameters" title="Permalink to this headline">¶</a></dt>
@@ -1313,7 +1313,7 @@ <h1 id="arch-univariate-harx-fit">arch.univariate.HARX.fit<a class="headerlink"
 <dt id="arch.univariate.HARX.fit.disp"><span class="n sig-name">disp</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)">bool</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'off'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'final'</span></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;final&#39;</span></code><a class="headerlink" href="#arch.univariate.HARX.fit.disp" title="Permalink to this definition">¶</a></dt><dd><p>Either ‘final’ to print optimization result or ‘off’ to display
 nothing. If using a boolean, False is “off” and True is “final”</p>
 </dd>
-<dt id="arch.univariate.HARX.fit.starting_values"><span class="n sig-name">starting_values</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.HARX.fit.starting_values" title="Permalink to this definition">¶</a></dt><dd><p>Array of starting values to use.  If not provided, starting values
+<dt id="arch.univariate.HARX.fit.starting_values"><span class="n sig-name">starting_values</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.HARX.fit.starting_values" title="Permalink to this definition">¶</a></dt><dd><p>Array of starting values to use.  If not provided, starting values
 are constructed by the model components.</p>
 </dd>
 <dt id="arch.univariate.HARX.fit.cov_type"><span class="n sig-name">cov_type</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><span class="s">'robust'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'classic'</span></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;robust&#39;</span></code><a class="headerlink" href="#arch.univariate.HARX.fit.cov_type" title="Permalink to this definition">¶</a></dt><dd><p>Estimation method of parameter covariance.  Supported options are
@@ -1323,9 +1323,9 @@ <h1 id="arch-univariate-harx-fit">arch.univariate.HARX.fit<a class="headerlink"
 </dd>
 <dt id="arch.univariate.HARX.fit.show_warning"><span class="n sig-name">show_warning</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)">bool</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code><a class="headerlink" href="#arch.univariate.HARX.fit.show_warning" title="Permalink to this definition">¶</a></dt><dd><p>Flag indicating whether convergence warnings should be shown.</p>
 </dd>
-<dt id="arch.univariate.HARX.fit.first_obs"><span class="n sig-name">first_obs</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)">str</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.12)">datetime</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)">datetime64</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.1)">Timestamp</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.HARX.fit.first_obs" title="Permalink to this definition">¶</a></dt><dd><p>First observation to use when estimating model</p>
+<dt id="arch.univariate.HARX.fit.first_obs"><span class="n sig-name">first_obs</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)">str</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.12)">datetime</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)">datetime64</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.4)">Timestamp</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.HARX.fit.first_obs" title="Permalink to this definition">¶</a></dt><dd><p>First observation to use when estimating model</p>
 </dd>
-<dt id="arch.univariate.HARX.fit.last_obs"><span class="n sig-name">last_obs</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)">str</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.12)">datetime</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)">datetime64</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.1)">Timestamp</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.HARX.fit.last_obs" title="Permalink to this definition">¶</a></dt><dd><p>Last observation to use when estimating model</p>
+<dt id="arch.univariate.HARX.fit.last_obs"><span class="n sig-name">last_obs</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)">str</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.12)">datetime</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)">datetime64</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.4)">Timestamp</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.HARX.fit.last_obs" title="Permalink to this definition">¶</a></dt><dd><p>Last observation to use when estimating model</p>
 </dd>
 <dt id="arch.univariate.HARX.fit.tol"><span class="n sig-name">tol</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.HARX.fit.tol" title="Permalink to this definition">¶</a></dt><dd><p>Tolerance for termination.</p>
 </dd>
@@ -1465,7 +1465,7 @@ <h1 id="arch-univariate-harx-fit">arch.univariate.HARX.fit<a class="headerlink"
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index 02d3d844c0..d76b9ddecd 100644
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+++ b/devel/univariate/generated/generated/arch.univariate.HARX.fix.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.HARX.fix - arch v6.2.1 (+21)</title>
+        <title>arch.univariate.HARX.fix - arch v6.2.1 (+23)</title>
       
     
 
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@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
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@@ -1204,18 +1204,18 @@
 <h1 id="arch-univariate-harx-fix">arch.univariate.HARX.fix<a class="headerlink" href="#arch-univariate-harx-fix" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.HARX.fix">
-<span class="sig-prename descclassname"><span class="pre">HARX.</span></span><span class="sig-name descname"><span class="pre">fix</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARX.fix.params" title="arch.univariate.HARX.fix.params (Python parameter) — User specified parameters to use when generating the result. Must have the correct number of parameters for a given choice of mean model, volatility model and distribution."><span class="n"><span class="pre">params</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARX.fix.first_obs" title="arch.univariate.HARX.fix.first_obs (Python parameter) — First observation to use when fixing model"><span class="n"><span class="pre">first_obs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.12)"><span class="pre">datetime</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)"><span class="pre">datetime64</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.1)"><span class="pre">Timestamp</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARX.fix.last_obs" title="arch.univariate.HARX.fix.last_obs (Python parameter) — Last observation to use when fixing model"><span class="n"><span class="pre">last_obs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.12)"><span class="pre">datetime</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)"><span class="pre">datetime64</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.1)"><span class="pre">Timestamp</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference internal" href="../arch.univariate.base.ARCHModelFixedResult.html#arch.univariate.base.ARCHModelFixedResult" title="arch.univariate.base.ARCHModelFixedResult (Python class) — Results for fixed parameters for an ARCHModel model"><span class="pre">ARCHModelFixedResult</span></a></span></span></span><a class="headerlink" href="#arch.univariate.HARX.fix" title="Link to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">HARX.</span></span><span class="sig-name descname"><span class="pre">fix</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARX.fix.params" title="arch.univariate.HARX.fix.params (Python parameter) — User specified parameters to use when generating the result. Must have the correct number of parameters for a given choice of mean model, volatility model and distribution."><span class="n"><span class="pre">params</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARX.fix.first_obs" title="arch.univariate.HARX.fix.first_obs (Python parameter) — First observation to use when fixing model"><span class="n"><span class="pre">first_obs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.12)"><span class="pre">datetime</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)"><span class="pre">datetime64</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.4)"><span class="pre">Timestamp</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARX.fix.last_obs" title="arch.univariate.HARX.fix.last_obs (Python parameter) — Last observation to use when fixing model"><span class="n"><span class="pre">last_obs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.12)"><span class="pre">datetime</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)"><span class="pre">datetime64</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.4)"><span class="pre">Timestamp</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference internal" href="../arch.univariate.base.ARCHModelFixedResult.html#arch.univariate.base.ARCHModelFixedResult" title="arch.univariate.base.ARCHModelFixedResult (Python class) — Results for fixed parameters for an ARCHModel model"><span class="pre">ARCHModelFixedResult</span></a></span></span></span><a class="headerlink" href="#arch.univariate.HARX.fix" title="Link to this definition">¶</a></dt>
 <dd><p>Allows an ARCHModelFixedResult to be constructed from fixed parameters.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.HARX.fix-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.HARX.fix-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.HARX.fix.params"><span class="n sig-name">params</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.univariate.HARX.fix.params" title="Permalink to this definition">¶</a></dt><dd><p>User specified parameters to use when generating the result. Must
+<dt id="arch.univariate.HARX.fix.params"><span class="n sig-name">params</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a></span></span><a class="headerlink" href="#arch.univariate.HARX.fix.params" title="Permalink to this definition">¶</a></dt><dd><p>User specified parameters to use when generating the result. Must
 have the correct number of parameters for a given choice of mean
 model, volatility model and distribution.</p>
 </dd>
-<dt id="arch.univariate.HARX.fix.first_obs"><span class="n sig-name">first_obs</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)">str</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.12)">datetime</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)">datetime64</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.1)">Timestamp</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.HARX.fix.first_obs" title="Permalink to this definition">¶</a></dt><dd><p>First observation to use when fixing model</p>
+<dt id="arch.univariate.HARX.fix.first_obs"><span class="n sig-name">first_obs</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)">str</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.12)">datetime</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)">datetime64</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.4)">Timestamp</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.HARX.fix.first_obs" title="Permalink to this definition">¶</a></dt><dd><p>First observation to use when fixing model</p>
 </dd>
-<dt id="arch.univariate.HARX.fix.last_obs"><span class="n sig-name">last_obs</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)">str</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.12)">datetime</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)">datetime64</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.1)">Timestamp</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.HARX.fix.last_obs" title="Permalink to this definition">¶</a></dt><dd><p>Last observation to use when fixing model</p>
+<dt id="arch.univariate.HARX.fix.last_obs"><span class="n sig-name">last_obs</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)">str</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.12)">datetime</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)">datetime64</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.4)">Timestamp</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.HARX.fix.last_obs" title="Permalink to this definition">¶</a></dt><dd><p>Last observation to use when fixing model</p>
 </dd>
 </dl>
 </dd>
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 <h1 id="arch-univariate-harx-forecast">arch.univariate.HARX.forecast<a class="headerlink" href="#arch-univariate-harx-forecast" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.HARX.forecast">
-<span class="sig-prename descclassname"><span class="pre">HARX.</span></span><span class="sig-name descname"><span class="pre">forecast</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARX.forecast.params" title="arch.univariate.HARX.forecast.params (Python parameter) — Parameters required to forecast. Must be identical in shape to the parameters computed by fitting the model."><span class="n"><span class="pre">params</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARX.forecast.horizon" title="arch.univariate.HARX.forecast.horizon (Python parameter) — Number of steps to forecast"><span class="n"><span class="pre">horizon</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARX.forecast.start" title="arch.univariate.HARX.forecast.start (Python parameter) — An integer, datetime or str indicating the first observation to produce the forecast for.  Datetimes can only be used with pandas inputs that have a datetime index. Strings must be convertible to a date time, such as in '1945-01-01'."><span class="n"><span class="pre">start</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.12)"><span class="pre">datetime</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)"><span class="pre">datetime64</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.1)"><span class="pre">Timestamp</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARX.forecast.align" title="arch.univariate.HARX.forecast.align (Python parameter) — Either 'origin' or 'target'.  When set of 'origin', the t-th row of forecasts contains the forecasts for t+1, t+2, ..., t+h. When set to 'target', the t-th row contains the 1-step ahead forecast from time t-1, the 2 step from time t-2, ..., and the h-step from time t-h.  'target' simplified computing forecast errors since the realization and h-step forecast are aligned."><span class="n"><span class="pre">align</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'origin'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'target'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;origin&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARX.forecast.method" title="arch.univariate.HARX.forecast.method (Python parameter) — Method to use when producing the forecast. The default is analytic. The method only affects the variance forecast generation.  Not all volatility models support all methods. In particular, volatility models that do not evolve in squares such as EGARCH or TARCH do not support the 'analytic' method for horizons &gt; 1."><span class="n"><span class="pre">method</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'analytic'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'simulation'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'bootstrap'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;analytic&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARX.forecast.simulations" title="arch.univariate.HARX.forecast.simulations (Python parameter) — Number of simulations to run when computing the forecast using either simulation or bootstrap."><span class="n"><span class="pre">simulations</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARX.forecast.rng" title="arch.univariate.HARX.forecast.rng (Python parameter) — Custom random number generator to use in simulation-based forecasts. Must produce random samples using the syntax rng(size) where size the 2-element tuple (simulations, horizon)."><span class="n"><span class="pre">rng</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.12)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARX.forecast.random_state" title="arch.univariate.HARX.forecast.random_state (Python parameter) — NumPy RandomState instance to use when method is 'bootstrap'"><span class="n"><span class="pre">random_state</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.26)"><span class="pre">RandomState</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARX.forecast.reindex" title="arch.univariate.HARX.forecast.reindex (Python parameter) — Whether to reindex the forecasts to have the same dimension as the series being forecast. Prior to 4.18 this was the default. As of 4.19 this is now optional. If not provided, a warning is raised about the future change in the default which will occur after September 2021."><span class="n"><span class="pre">reindex</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARX.forecast.x" title="arch.univariate.HARX.forecast.x (Python parameter) — Values to use for exogenous regressors if any are included in the model. Three formats are accepted:"><span class="n"><span class="pre">x</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Hashable" title="(in Python v3.12)"><span class="pre">Hashable</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference internal" href="../arch.univariate.base.ARCHModelForecast.html#arch.univariate.base.ARCHModelForecast" title="arch.univariate.base.ARCHModelForecast (Python class) — Container for forecasts from an ARCH Model"><span class="pre">ARCHModelForecast</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/mean.html#HARX.forecast"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.HARX.forecast" title="Link to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">HARX.</span></span><span class="sig-name descname"><span class="pre">forecast</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARX.forecast.params" title="arch.univariate.HARX.forecast.params (Python parameter) — Parameters required to forecast. Must be identical in shape to the parameters computed by fitting the model."><span class="n"><span class="pre">params</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARX.forecast.horizon" title="arch.univariate.HARX.forecast.horizon (Python parameter) — Number of steps to forecast"><span class="n"><span class="pre">horizon</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARX.forecast.start" title="arch.univariate.HARX.forecast.start (Python parameter) — An integer, datetime or str indicating the first observation to produce the forecast for.  Datetimes can only be used with pandas inputs that have a datetime index. Strings must be convertible to a date time, such as in '1945-01-01'."><span class="n"><span class="pre">start</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.12)"><span class="pre">datetime</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)"><span class="pre">datetime64</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.4)"><span class="pre">Timestamp</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARX.forecast.align" title="arch.univariate.HARX.forecast.align (Python parameter) — Either 'origin' or 'target'.  When set of 'origin', the t-th row of forecasts contains the forecasts for t+1, t+2, ..., t+h. When set to 'target', the t-th row contains the 1-step ahead forecast from time t-1, the 2 step from time t-2, ..., and the h-step from time t-h.  'target' simplified computing forecast errors since the realization and h-step forecast are aligned."><span class="n"><span class="pre">align</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'origin'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'target'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;origin&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARX.forecast.method" title="arch.univariate.HARX.forecast.method (Python parameter) — Method to use when producing the forecast. The default is analytic. The method only affects the variance forecast generation.  Not all volatility models support all methods. In particular, volatility models that do not evolve in squares such as EGARCH or TARCH do not support the 'analytic' method for horizons &gt; 1."><span class="n"><span class="pre">method</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'analytic'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'simulation'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'bootstrap'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;analytic&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARX.forecast.simulations" title="arch.univariate.HARX.forecast.simulations (Python parameter) — Number of simulations to run when computing the forecast using either simulation or bootstrap."><span class="n"><span class="pre">simulations</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARX.forecast.rng" title="arch.univariate.HARX.forecast.rng (Python parameter) — Custom random number generator to use in simulation-based forecasts. Must produce random samples using the syntax rng(size) where size the 2-element tuple (simulations, horizon)."><span class="n"><span class="pre">rng</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.12)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARX.forecast.random_state" title="arch.univariate.HARX.forecast.random_state (Python parameter) — NumPy RandomState instance to use when method is 'bootstrap'"><span class="n"><span class="pre">random_state</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.26)"><span class="pre">RandomState</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARX.forecast.reindex" title="arch.univariate.HARX.forecast.reindex (Python parameter) — Whether to reindex the forecasts to have the same dimension as the series being forecast. Prior to 4.18 this was the default. As of 4.19 this is now optional. If not provided, a warning is raised about the future change in the default which will occur after September 2021."><span class="n"><span class="pre">reindex</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARX.forecast.x" title="arch.univariate.HARX.forecast.x (Python parameter) — Values to use for exogenous regressors if any are included in the model. Three formats are accepted:"><span class="n"><span class="pre">x</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Hashable" title="(in Python v3.12)"><span class="pre">Hashable</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference internal" href="../arch.univariate.base.ARCHModelForecast.html#arch.univariate.base.ARCHModelForecast" title="arch.univariate.base.ARCHModelForecast (Python class) — Container for forecasts from an ARCH Model"><span class="pre">ARCHModelForecast</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/mean.html#HARX.forecast"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.HARX.forecast" title="Link to this definition">¶</a></dt>
 <dd><p>Construct forecasts from estimated model</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.HARX.forecast-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.HARX.forecast-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.HARX.forecast.params"><span class="n sig-name">params</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.univariate.HARX.forecast.params" title="Permalink to this definition">¶</a></dt><dd><p>Parameters required to forecast. Must be identical in
+<dt id="arch.univariate.HARX.forecast.params"><span class="n sig-name">params</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a></span></span><a class="headerlink" href="#arch.univariate.HARX.forecast.params" title="Permalink to this definition">¶</a></dt><dd><p>Parameters required to forecast. Must be identical in
 shape to the parameters computed by fitting the model.</p>
 </dd>
 <dt id="arch.univariate.HARX.forecast.horizon"><span class="n sig-name">horizon</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code><a class="headerlink" href="#arch.univariate.HARX.forecast.horizon" title="Permalink to this definition">¶</a></dt><dd><p>Number of steps to forecast</p>
 </dd>
-<dt id="arch.univariate.HARX.forecast.start"><span class="n sig-name">start</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)">str</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.12)">datetime</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)">datetime64</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.1)">Timestamp</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.HARX.forecast.start" title="Permalink to this definition">¶</a></dt><dd><p>An integer, datetime or str indicating the first observation to
+<dt id="arch.univariate.HARX.forecast.start"><span class="n sig-name">start</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)">str</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.12)">datetime</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)">datetime64</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.4)">Timestamp</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.HARX.forecast.start" title="Permalink to this definition">¶</a></dt><dd><p>An integer, datetime or str indicating the first observation to
 produce the forecast for.  Datetimes can only be used with pandas
 inputs that have a datetime index. Strings must be convertible
 to a date time, such as in ‘1945-01-01’.</p>
@@ -1348,7 +1348,7 @@ <h1 id="arch-univariate-harx-forecast">arch.univariate.HARX.forecast<a class="he
 </div>
 </p>
 </dd>
-<dt id="arch.univariate.HARX.forecast.x"><span class="n sig-name">x</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)">dict</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Hashable" title="(in Python v3.12)">Hashable</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.HARX.forecast.x" title="Permalink to this definition">¶</a></dt><dd><p><p>Values to use for exogenous regressors if any are included in the
+<dt id="arch.univariate.HARX.forecast.x"><span class="n sig-name">x</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)">dict</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Hashable" title="(in Python v3.12)">Hashable</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.HARX.forecast.x" title="Permalink to this definition">¶</a></dt><dd><p><p>Values to use for exogenous regressors if any are included in the
 model. Three formats are accepted:</p>
 <ul class="simple">
 <li><p>2-d array-like: This format can be used when there is a single
@@ -1530,7 +1530,7 @@ <h1 id="arch-univariate-harx-forecast">arch.univariate.HARX.forecast<a class="he
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diff --git a/devel/univariate/generated/generated/arch.univariate.HARX.name.html b/devel/univariate/generated/generated/arch.univariate.HARX.name.html
index 0ad4cbf44e..d4344f903d 100644
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@@ -15,7 +15,7 @@
     
   
       
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+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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diff --git a/devel/univariate/generated/generated/arch.univariate.HARX.num_params.html b/devel/univariate/generated/generated/arch.univariate.HARX.num_params.html
index f714095493..67a7dc99a6 100644
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+++ b/devel/univariate/generated/generated/arch.univariate.HARX.num_params.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.HARX.num_params - arch v6.2.1 (+21)</title>
+        <title>arch.univariate.HARX.num_params - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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diff --git a/devel/univariate/generated/generated/arch.univariate.HARX.parameter_names.html b/devel/univariate/generated/generated/arch.univariate.HARX.parameter_names.html
index 99bd982412..2a36faece6 100644
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+++ b/devel/univariate/generated/generated/arch.univariate.HARX.parameter_names.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.HARX.parameter_names - arch v6.2.1 (+21)</title>
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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+            arch v6.2.1 (+23)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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diff --git a/devel/univariate/generated/generated/arch.univariate.HARX.resids.html b/devel/univariate/generated/generated/arch.univariate.HARX.resids.html
index b8ad955a99..806a17b3d6 100644
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+++ b/devel/univariate/generated/generated/arch.univariate.HARX.resids.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.HARX.resids - arch v6.2.1 (+21)</title>
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@@ -47,7 +47,7 @@
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@@ -92,7 +92,7 @@
 
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 <h1 id="arch-univariate-harx-resids">arch.univariate.HARX.resids<a class="headerlink" href="#arch-univariate-harx-resids" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.HARX.resids">
-<span class="sig-prename descclassname"><span class="pre">HARX.</span></span><span class="sig-name descname"><span class="pre">resids</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARX.resids.params" title="arch.univariate.HARX.resids.params (Python parameter) — Model parameters"><span class="n"><span class="pre">params</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARX.resids.y" title="arch.univariate.HARX.resids.y (Python parameter) — Alternative values to use when computing model residuals"><span class="n"><span class="pre">y</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARX.resids.regressors" title="arch.univariate.HARX.resids.regressors (Python parameter) — Alternative regressor values to use when computing model residuals"><span class="n"><span class="pre">regressors</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/mean.html#HARX.resids"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.HARX.resids" title="Link to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">HARX.</span></span><span class="sig-name descname"><span class="pre">resids</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARX.resids.params" title="arch.univariate.HARX.resids.params (Python parameter) — Model parameters"><span class="n"><span class="pre">params</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARX.resids.y" title="arch.univariate.HARX.resids.y (Python parameter) — Alternative values to use when computing model residuals"><span class="n"><span class="pre">y</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARX.resids.regressors" title="arch.univariate.HARX.resids.regressors (Python parameter) — Alternative regressor values to use when computing model residuals"><span class="n"><span class="pre">regressors</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/mean.html#HARX.resids"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.HARX.resids" title="Link to this definition">¶</a></dt>
 <dd><p>Compute model residuals</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.HARX.resids-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.HARX.resids-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
 <dt id="arch.univariate.HARX.resids.params"><span class="n sig-name">params</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.HARX.resids.params" title="Permalink to this definition">¶</a></dt><dd><p>Model parameters</p>
 </dd>
-<dt id="arch.univariate.HARX.resids.y"><span class="n sig-name">y</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.HARX.resids.y" title="Permalink to this definition">¶</a></dt><dd><p>Alternative values to use when computing model residuals</p>
+<dt id="arch.univariate.HARX.resids.y"><span class="n sig-name">y</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.HARX.resids.y" title="Permalink to this definition">¶</a></dt><dd><p>Alternative values to use when computing model residuals</p>
 </dd>
-<dt id="arch.univariate.HARX.resids.regressors"><span class="n sig-name">regressors</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.HARX.resids.regressors" title="Permalink to this definition">¶</a></dt><dd><p>Alternative regressor values to use when computing model residuals</p>
+<dt id="arch.univariate.HARX.resids.regressors"><span class="n sig-name">regressors</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.HARX.resids.regressors" title="Permalink to this definition">¶</a></dt><dd><p>Alternative regressor values to use when computing model residuals</p>
 </dd>
 </dl>
 </dd>
@@ -1340,7 +1340,7 @@ <h1 id="arch-univariate-harx-resids">arch.univariate.HARX.resids<a class="header
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diff --git a/devel/univariate/generated/generated/arch.univariate.HARX.simulate.html b/devel/univariate/generated/generated/arch.univariate.HARX.simulate.html
index 60e0b9a0d8..1b10ad0f13 100644
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-            arch v6.2.1 (+21)
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-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
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@@ -1246,12 +1246,12 @@
 <h1 id="arch-univariate-harx-simulate">arch.univariate.HARX.simulate<a class="headerlink" href="#arch-univariate-harx-simulate" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.HARX.simulate">
-<span class="sig-prename descclassname"><span class="pre">HARX.</span></span><span class="sig-name descname"><span class="pre">simulate</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARX.simulate.params" title="arch.univariate.HARX.simulate.params (Python parameter) — Parameters to use when simulating the model.  Parameter order is [mean volatility distribution] where the parameters of the mean model are ordered [constant lag[0] lag[1] ... lag[p] ex[0] ... ex[k-1]] where lag[j] indicates the coefficient on the jth lag in the model and ex[j] is the coefficient on the jth exogenous variable."><span class="n"><span class="pre">params</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARX.simulate.nobs" title="arch.univariate.HARX.simulate.nobs (Python parameter) — Length of series to simulate"><span class="n"><span class="pre">nobs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARX.simulate.burn" title="arch.univariate.HARX.simulate.burn (Python parameter) — Number of values to simulate to initialize the model and remove dependence on initial values."><span class="n"><span class="pre">burn</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">500</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARX.simulate.initial_value" title="arch.univariate.HARX.simulate.initial_value (Python parameter) — Either a scalar value or max(lags) array set of initial values to use when initializing the model.  If omitted, 0.0 is used."><span class="n"><span class="pre">initial_value</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARX.simulate.x" title="arch.univariate.HARX.simulate.x (Python parameter) — nobs + burn by k array of exogenous variables to include in the simulation."><span class="n"><span class="pre">x</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARX.simulate.initial_value_vol" title="arch.univariate.HARX.simulate.initial_value_vol (Python parameter) — An array or scalar to use when initializing the volatility process."><span class="n"><span class="pre">initial_value_vol</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/mean.html#HARX.simulate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.HARX.simulate" title="Link to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">HARX.</span></span><span class="sig-name descname"><span class="pre">simulate</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARX.simulate.params" title="arch.univariate.HARX.simulate.params (Python parameter) — Parameters to use when simulating the model.  Parameter order is [mean volatility distribution] where the parameters of the mean model are ordered [constant lag[0] lag[1] ... lag[p] ex[0] ... ex[k-1]] where lag[j] indicates the coefficient on the jth lag in the model and ex[j] is the coefficient on the jth exogenous variable."><span class="n"><span class="pre">params</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARX.simulate.nobs" title="arch.univariate.HARX.simulate.nobs (Python parameter) — Length of series to simulate"><span class="n"><span class="pre">nobs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARX.simulate.burn" title="arch.univariate.HARX.simulate.burn (Python parameter) — Number of values to simulate to initialize the model and remove dependence on initial values."><span class="n"><span class="pre">burn</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">500</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARX.simulate.initial_value" title="arch.univariate.HARX.simulate.initial_value (Python parameter) — Either a scalar value or max(lags) array set of initial values to use when initializing the model.  If omitted, 0.0 is used."><span class="n"><span class="pre">initial_value</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARX.simulate.x" title="arch.univariate.HARX.simulate.x (Python parameter) — nobs + burn by k array of exogenous variables to include in the simulation."><span class="n"><span class="pre">x</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.HARX.simulate.initial_value_vol" title="arch.univariate.HARX.simulate.initial_value_vol (Python parameter) — An array or scalar to use when initializing the volatility process."><span class="n"><span class="pre">initial_value_vol</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><span class="pre">DataFrame</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/mean.html#HARX.simulate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.HARX.simulate" title="Link to this definition">¶</a></dt>
 <dd><p>Simulates data from a linear regression, AR or HAR models</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.HARX.simulate-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.HARX.simulate-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.HARX.simulate.params"><span class="n sig-name">params</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="p">]</span></span></span><a class="headerlink" href="#arch.univariate.HARX.simulate.params" title="Permalink to this definition">¶</a></dt><dd><p>Parameters to use when simulating the model.  Parameter order is
+<dt id="arch.univariate.HARX.simulate.params"><span class="n sig-name">params</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="p">]</span></span></span><a class="headerlink" href="#arch.univariate.HARX.simulate.params" title="Permalink to this definition">¶</a></dt><dd><p>Parameters to use when simulating the model.  Parameter order is
 [mean volatility distribution] where the parameters of the mean
 model are ordered [constant lag[0] lag[1] … lag[p] ex[0] …
 ex[k-1]] where lag[j] indicates the coefficient on the jth lag in
@@ -1266,7 +1266,7 @@ <h1 id="arch-univariate-harx-simulate">arch.univariate.HARX.simulate<a class="he
 <dt id="arch.univariate.HARX.simulate.initial_value"><span class="n sig-name">initial_value</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.HARX.simulate.initial_value" title="Permalink to this definition">¶</a></dt><dd><p>Either a scalar value or <cite>max(lags)</cite> array set of initial values to
 use when initializing the model.  If omitted, 0.0 is used.</p>
 </dd>
-<dt id="arch.univariate.HARX.simulate.x"><span class="n sig-name">x</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.HARX.simulate.x" title="Permalink to this definition">¶</a></dt><dd><p>nobs + burn by k array of exogenous variables to include in the
+<dt id="arch.univariate.HARX.simulate.x"><span class="n sig-name">x</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.HARX.simulate.x" title="Permalink to this definition">¶</a></dt><dd><p>nobs + burn by k array of exogenous variables to include in the
 simulation.</p>
 </dd>
 <dt id="arch.univariate.HARX.simulate.initial_value_vol"><span class="n sig-name">initial_value_vol</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.HARX.simulate.initial_value_vol" title="Permalink to this definition">¶</a></dt><dd><p>An array or scalar to use when initializing the volatility process.</p>
@@ -1279,7 +1279,7 @@ <h1 id="arch-univariate-harx-simulate">arch.univariate.HARX.simulate<a class="he
 containing the errors used in the simulation</p>
 </dd>
 <dt class="field-odd" id="arch.univariate.HARX.simulate-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.HARX.simulate-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><code class="xref py py-class docutils literal notranslate"><span class="pre">pandas.DataFrame</span></code></a></p>
+<dd class="field-odd"><p><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><code class="xref py py-class docutils literal notranslate"><span class="pre">pandas.DataFrame</span></code></a></p>
 </dd>
 </dl>
 <p class="example rubric">Examples</p>
@@ -1419,7 +1419,7 @@ <h1 id="arch-univariate-harx-simulate">arch.univariate.HARX.simulate<a class="he
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/devel/univariate/generated/generated/arch.univariate.HARX.starting_values.html b/devel/univariate/generated/generated/arch.univariate.HARX.starting_values.html
index 973c15ce98..55344a478a 100644
--- a/devel/univariate/generated/generated/arch.univariate.HARX.starting_values.html
+++ b/devel/univariate/generated/generated/arch.univariate.HARX.starting_values.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.HARX.starting_values - arch v6.2.1 (+21)</title>
+        <title>arch.univariate.HARX.starting_values - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
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-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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     <div class="md-nav__source">
@@ -1263,7 +1263,7 @@ <h1 id="arch-univariate-harx-starting-values">arch.univariate.HARX.starting_valu
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
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diff --git a/devel/univariate/generated/generated/arch.univariate.HARX.volatility.html b/devel/univariate/generated/generated/arch.univariate.HARX.volatility.html
index 3140b3571e..ebca138263 100644
--- a/devel/univariate/generated/generated/arch.univariate.HARX.volatility.html
+++ b/devel/univariate/generated/generated/arch.univariate.HARX.volatility.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.HARX.volatility - arch v6.2.1 (+21)</title>
+        <title>arch.univariate.HARX.volatility - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
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-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -1215,7 +1215,7 @@ <h1 id="arch-univariate-harx-volatility">arch.univariate.HARX.volatility<a class
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/devel/univariate/generated/generated/arch.univariate.HARX.x.html b/devel/univariate/generated/generated/arch.univariate.HARX.x.html
index 7958a17f16..2fd5652058 100644
--- a/devel/univariate/generated/generated/arch.univariate.HARX.x.html
+++ b/devel/univariate/generated/generated/arch.univariate.HARX.x.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.HARX.x - arch v6.2.1 (+21)</title>
+        <title>arch.univariate.HARX.x - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
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-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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@@ -1096,7 +1096,7 @@
 <h1 id="arch-univariate-harx-x">arch.univariate.HARX.x<a class="headerlink" href="#arch-univariate-harx-x" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.HARX.x">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">HARX.</span></span><span class="sig-name descname"><span class="pre">x</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span><a class="headerlink" href="#arch.univariate.HARX.x" title="Link to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">HARX.</span></span><span class="sig-name descname"><span class="pre">x</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span><a class="headerlink" href="#arch.univariate.HARX.x" title="Link to this definition">¶</a></dt>
 <dd><p>Gets the value of the exogenous regressors in the model</p>
 </dd></dl>
 
@@ -1214,7 +1214,7 @@ <h1 id="arch-univariate-harx-x">arch.univariate.HARX.x<a class="headerlink" href
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
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diff --git a/devel/univariate/generated/generated/arch.univariate.HARX.y.html b/devel/univariate/generated/generated/arch.univariate.HARX.y.html
index 6ee37e205e..37a4c9db9e 100644
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+++ b/devel/univariate/generated/generated/arch.univariate.HARX.y.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.HARX.y - arch v6.2.1 (+21)</title>
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
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-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -1096,7 +1096,7 @@
 <h1 id="arch-univariate-harx-y">arch.univariate.HARX.y<a class="headerlink" href="#arch-univariate-harx-y" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.HARX.y">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">HARX.</span></span><span class="sig-name descname"><span class="pre">y</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span><a class="headerlink" href="#arch.univariate.HARX.y" title="Link to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">HARX.</span></span><span class="sig-name descname"><span class="pre">y</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span><a class="headerlink" href="#arch.univariate.HARX.y" title="Link to this definition">¶</a></dt>
 <dd><p>Returns the dependent variable</p>
 </dd></dl>
 
@@ -1214,7 +1214,7 @@ <h1 id="arch-univariate-harx-y">arch.univariate.HARX.y<a class="headerlink" href
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-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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diff --git a/devel/univariate/generated/generated/arch.univariate.LS.bounds.html b/devel/univariate/generated/generated/arch.univariate.LS.bounds.html
index a0416ef211..ec7c8c1320 100644
--- a/devel/univariate/generated/generated/arch.univariate.LS.bounds.html
+++ b/devel/univariate/generated/generated/arch.univariate.LS.bounds.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.LS.bounds - arch v6.2.1 (+21)</title>
+        <title>arch.univariate.LS.bounds - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
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@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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@@ -102,7 +102,7 @@
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           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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@@ -1262,7 +1262,7 @@ <h1 id="arch-univariate-ls-bounds">arch.univariate.LS.bounds<a class="headerlink
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-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
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diff --git a/devel/univariate/generated/generated/arch.univariate.LS.compute_param_cov.html b/devel/univariate/generated/generated/arch.univariate.LS.compute_param_cov.html
index 2b151e56d1..44cc4a4546 100644
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+++ b/devel/univariate/generated/generated/arch.univariate.LS.compute_param_cov.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.LS.compute_param_cov - arch v6.2.1 (+21)</title>
+        <title>arch.univariate.LS.compute_param_cov - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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     <div class="md-nav__source">
@@ -1307,7 +1307,7 @@ <h1 id="arch-univariate-ls-compute-param-cov">arch.univariate.LS.compute_param_c
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-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
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   </body>
diff --git a/devel/univariate/generated/generated/arch.univariate.LS.constraints.html b/devel/univariate/generated/generated/arch.univariate.LS.constraints.html
index 34ccd4a859..6291b05f0f 100644
--- a/devel/univariate/generated/generated/arch.univariate.LS.constraints.html
+++ b/devel/univariate/generated/generated/arch.univariate.LS.constraints.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.LS.constraints - arch v6.2.1 (+21)</title>
+        <title>arch.univariate.LS.constraints - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
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@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
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-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
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   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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     <div class="md-nav__source">
@@ -1251,7 +1251,7 @@ <h1 id="arch-univariate-ls-constraints">arch.univariate.LS.constraints<a class="
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-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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diff --git a/devel/univariate/generated/generated/arch.univariate.LS.distribution.html b/devel/univariate/generated/generated/arch.univariate.LS.distribution.html
index f803bb8787..69b3e08acb 100644
--- a/devel/univariate/generated/generated/arch.univariate.LS.distribution.html
+++ b/devel/univariate/generated/generated/arch.univariate.LS.distribution.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.LS.distribution - arch v6.2.1 (+21)</title>
+        <title>arch.univariate.LS.distribution - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
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-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
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   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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@@ -1215,7 +1215,7 @@ <h1 id="arch-univariate-ls-distribution">arch.univariate.LS.distribution<a class
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 <h1 id="arch-univariate-ls-fit">arch.univariate.LS.fit<a class="headerlink" href="#arch-univariate-ls-fit" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.LS.fit">
-<span class="sig-prename descclassname"><span class="pre">LS.</span></span><span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.LS.fit.update_freq" title="arch.univariate.LS.fit.update_freq (Python parameter) — Frequency of iteration updates.  Output is generated every update_freq iterations. Set to 0 to disable iterative output."><span class="n"><span class="pre">update_freq</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.LS.fit.disp" title="arch.univariate.LS.fit.disp (Python parameter) — Either 'final' to print optimization result or 'off' to display nothing. If using a boolean, False is &quot;off&quot; and True is &quot;final&quot;"><span class="n"><span class="pre">disp</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'off'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'final'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;final&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.LS.fit.starting_values" title="arch.univariate.LS.fit.starting_values (Python parameter) — Array of starting values to use.  If not provided, starting values are constructed by the model components."><span class="n"><span class="pre">starting_values</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.LS.fit.cov_type" title="arch.univariate.LS.fit.cov_type (Python parameter) — Estimation method of parameter covariance.  Supported options are 'robust', which does not assume the Information Matrix Equality holds and 'classic' which does.  In the ARCH literature, 'robust' corresponds to Bollerslev-Wooldridge covariance estimator."><span class="n"><span class="pre">cov_type</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'robust'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'classic'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;robust&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.LS.fit.show_warning" title="arch.univariate.LS.fit.show_warning (Python parameter) — Flag indicating whether convergence warnings should be shown."><span class="n"><span class="pre">show_warning</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.LS.fit.first_obs" title="arch.univariate.LS.fit.first_obs (Python parameter) — First observation to use when estimating model"><span class="n"><span class="pre">first_obs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.12)"><span class="pre">datetime</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)"><span class="pre">datetime64</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.1)"><span class="pre">Timestamp</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.LS.fit.last_obs" title="arch.univariate.LS.fit.last_obs (Python parameter) — Last observation to use when estimating model"><span class="n"><span class="pre">last_obs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.12)"><span class="pre">datetime</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)"><span class="pre">datetime64</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.1)"><span class="pre">Timestamp</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.LS.fit.tol" title="arch.univariate.LS.fit.tol (Python parameter) — Tolerance for termination."><span class="n"><span class="pre">tol</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.LS.fit.options" title="arch.univariate.LS.fit.options (Python parameter) — Options to pass to scipy.optimize.minimize.  Valid entries include 'ftol', 'eps', 'disp', and 'maxiter'."><span class="n"><span class="pre">options</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.12)"><span class="pre">Any</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.LS.fit.backcast" title="arch.univariate.LS.fit.backcast (Python parameter) — Value to use as backcast. Should be measure \sigma^2_0 since model-specific non-linear transformations are applied to value before computing the variance recursions."><span class="n"><span class="pre">backcast</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference internal" href="../arch.univariate.base.ARCHModelResult.html#arch.univariate.base.ARCHModelResult" title="arch.univariate.base.ARCHModelResult (Python class) — Results from estimation of an ARCHModel model"><span class="pre">ARCHModelResult</span></a></span></span></span><a class="headerlink" href="#arch.univariate.LS.fit" title="Link to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">LS.</span></span><span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.LS.fit.update_freq" title="arch.univariate.LS.fit.update_freq (Python parameter) — Frequency of iteration updates.  Output is generated every update_freq iterations. Set to 0 to disable iterative output."><span class="n"><span class="pre">update_freq</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.LS.fit.disp" title="arch.univariate.LS.fit.disp (Python parameter) — Either 'final' to print optimization result or 'off' to display nothing. If using a boolean, False is &quot;off&quot; and True is &quot;final&quot;"><span class="n"><span class="pre">disp</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'off'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'final'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;final&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.LS.fit.starting_values" title="arch.univariate.LS.fit.starting_values (Python parameter) — Array of starting values to use.  If not provided, starting values are constructed by the model components."><span class="n"><span class="pre">starting_values</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.LS.fit.cov_type" title="arch.univariate.LS.fit.cov_type (Python parameter) — Estimation method of parameter covariance.  Supported options are 'robust', which does not assume the Information Matrix Equality holds and 'classic' which does.  In the ARCH literature, 'robust' corresponds to Bollerslev-Wooldridge covariance estimator."><span class="n"><span class="pre">cov_type</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'robust'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'classic'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;robust&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.LS.fit.show_warning" title="arch.univariate.LS.fit.show_warning (Python parameter) — Flag indicating whether convergence warnings should be shown."><span class="n"><span class="pre">show_warning</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.LS.fit.first_obs" title="arch.univariate.LS.fit.first_obs (Python parameter) — First observation to use when estimating model"><span class="n"><span class="pre">first_obs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.12)"><span class="pre">datetime</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)"><span class="pre">datetime64</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.4)"><span class="pre">Timestamp</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.LS.fit.last_obs" title="arch.univariate.LS.fit.last_obs (Python parameter) — Last observation to use when estimating model"><span class="n"><span class="pre">last_obs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.12)"><span class="pre">datetime</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)"><span class="pre">datetime64</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.4)"><span class="pre">Timestamp</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.LS.fit.tol" title="arch.univariate.LS.fit.tol (Python parameter) — Tolerance for termination."><span class="n"><span class="pre">tol</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.LS.fit.options" title="arch.univariate.LS.fit.options (Python parameter) — Options to pass to scipy.optimize.minimize.  Valid entries include 'ftol', 'eps', 'disp', and 'maxiter'."><span class="n"><span class="pre">options</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.12)"><span class="pre">Any</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.LS.fit.backcast" title="arch.univariate.LS.fit.backcast (Python parameter) — Value to use as backcast. Should be measure \sigma^2_0 since model-specific non-linear transformations are applied to value before computing the variance recursions."><span class="n"><span class="pre">backcast</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference internal" href="../arch.univariate.base.ARCHModelResult.html#arch.univariate.base.ARCHModelResult" title="arch.univariate.base.ARCHModelResult (Python class) — Results from estimation of an ARCHModel model"><span class="pre">ARCHModelResult</span></a></span></span></span><a class="headerlink" href="#arch.univariate.LS.fit" title="Link to this definition">¶</a></dt>
 <dd><p>Estimate model parameters</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.LS.fit-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.LS.fit-parameters" title="Permalink to this headline">¶</a></dt>
@@ -1313,7 +1313,7 @@ <h1 id="arch-univariate-ls-fit">arch.univariate.LS.fit<a class="headerlink" href
 <dt id="arch.univariate.LS.fit.disp"><span class="n sig-name">disp</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)">bool</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'off'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'final'</span></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;final&#39;</span></code><a class="headerlink" href="#arch.univariate.LS.fit.disp" title="Permalink to this definition">¶</a></dt><dd><p>Either ‘final’ to print optimization result or ‘off’ to display
 nothing. If using a boolean, False is “off” and True is “final”</p>
 </dd>
-<dt id="arch.univariate.LS.fit.starting_values"><span class="n sig-name">starting_values</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.LS.fit.starting_values" title="Permalink to this definition">¶</a></dt><dd><p>Array of starting values to use.  If not provided, starting values
+<dt id="arch.univariate.LS.fit.starting_values"><span class="n sig-name">starting_values</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.LS.fit.starting_values" title="Permalink to this definition">¶</a></dt><dd><p>Array of starting values to use.  If not provided, starting values
 are constructed by the model components.</p>
 </dd>
 <dt id="arch.univariate.LS.fit.cov_type"><span class="n sig-name">cov_type</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><span class="s">'robust'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'classic'</span></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;robust&#39;</span></code><a class="headerlink" href="#arch.univariate.LS.fit.cov_type" title="Permalink to this definition">¶</a></dt><dd><p>Estimation method of parameter covariance.  Supported options are
@@ -1323,9 +1323,9 @@ <h1 id="arch-univariate-ls-fit">arch.univariate.LS.fit<a class="headerlink" href
 </dd>
 <dt id="arch.univariate.LS.fit.show_warning"><span class="n sig-name">show_warning</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)">bool</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code><a class="headerlink" href="#arch.univariate.LS.fit.show_warning" title="Permalink to this definition">¶</a></dt><dd><p>Flag indicating whether convergence warnings should be shown.</p>
 </dd>
-<dt id="arch.univariate.LS.fit.first_obs"><span class="n sig-name">first_obs</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)">str</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.12)">datetime</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)">datetime64</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.1)">Timestamp</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.LS.fit.first_obs" title="Permalink to this definition">¶</a></dt><dd><p>First observation to use when estimating model</p>
+<dt id="arch.univariate.LS.fit.first_obs"><span class="n sig-name">first_obs</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)">str</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.12)">datetime</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)">datetime64</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.4)">Timestamp</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.LS.fit.first_obs" title="Permalink to this definition">¶</a></dt><dd><p>First observation to use when estimating model</p>
 </dd>
-<dt id="arch.univariate.LS.fit.last_obs"><span class="n sig-name">last_obs</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)">str</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.12)">datetime</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)">datetime64</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.1)">Timestamp</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.LS.fit.last_obs" title="Permalink to this definition">¶</a></dt><dd><p>Last observation to use when estimating model</p>
+<dt id="arch.univariate.LS.fit.last_obs"><span class="n sig-name">last_obs</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)">str</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.12)">datetime</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)">datetime64</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.4)">Timestamp</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.LS.fit.last_obs" title="Permalink to this definition">¶</a></dt><dd><p>Last observation to use when estimating model</p>
 </dd>
 <dt id="arch.univariate.LS.fit.tol"><span class="n sig-name">tol</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.LS.fit.tol" title="Permalink to this definition">¶</a></dt><dd><p>Tolerance for termination.</p>
 </dd>
@@ -1465,7 +1465,7 @@ <h1 id="arch-univariate-ls-fit">arch.univariate.LS.fit<a class="headerlink" href
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diff --git a/devel/univariate/generated/generated/arch.univariate.LS.fix.html b/devel/univariate/generated/generated/arch.univariate.LS.fix.html
index 58137c977d..9b82565f8e 100644
--- a/devel/univariate/generated/generated/arch.univariate.LS.fix.html
+++ b/devel/univariate/generated/generated/arch.univariate.LS.fix.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.LS.fix - arch v6.2.1 (+21)</title>
+        <title>arch.univariate.LS.fix - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
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 <h1 id="arch-univariate-ls-fix">arch.univariate.LS.fix<a class="headerlink" href="#arch-univariate-ls-fix" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.LS.fix">
-<span class="sig-prename descclassname"><span class="pre">LS.</span></span><span class="sig-name descname"><span class="pre">fix</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.LS.fix.params" title="arch.univariate.LS.fix.params (Python parameter) — User specified parameters to use when generating the result. Must have the correct number of parameters for a given choice of mean model, volatility model and distribution."><span class="n"><span class="pre">params</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.LS.fix.first_obs" title="arch.univariate.LS.fix.first_obs (Python parameter) — First observation to use when fixing model"><span class="n"><span class="pre">first_obs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.12)"><span class="pre">datetime</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)"><span class="pre">datetime64</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.1)"><span class="pre">Timestamp</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.LS.fix.last_obs" title="arch.univariate.LS.fix.last_obs (Python parameter) — Last observation to use when fixing model"><span class="n"><span class="pre">last_obs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.12)"><span class="pre">datetime</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)"><span class="pre">datetime64</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.1)"><span class="pre">Timestamp</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference internal" href="../arch.univariate.base.ARCHModelFixedResult.html#arch.univariate.base.ARCHModelFixedResult" title="arch.univariate.base.ARCHModelFixedResult (Python class) — Results for fixed parameters for an ARCHModel model"><span class="pre">ARCHModelFixedResult</span></a></span></span></span><a class="headerlink" href="#arch.univariate.LS.fix" title="Link to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">LS.</span></span><span class="sig-name descname"><span class="pre">fix</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.LS.fix.params" title="arch.univariate.LS.fix.params (Python parameter) — User specified parameters to use when generating the result. Must have the correct number of parameters for a given choice of mean model, volatility model and distribution."><span class="n"><span class="pre">params</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.LS.fix.first_obs" title="arch.univariate.LS.fix.first_obs (Python parameter) — First observation to use when fixing model"><span class="n"><span class="pre">first_obs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.12)"><span class="pre">datetime</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)"><span class="pre">datetime64</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.4)"><span class="pre">Timestamp</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.LS.fix.last_obs" title="arch.univariate.LS.fix.last_obs (Python parameter) — Last observation to use when fixing model"><span class="n"><span class="pre">last_obs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.12)"><span class="pre">datetime</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)"><span class="pre">datetime64</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.4)"><span class="pre">Timestamp</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference internal" href="../arch.univariate.base.ARCHModelFixedResult.html#arch.univariate.base.ARCHModelFixedResult" title="arch.univariate.base.ARCHModelFixedResult (Python class) — Results for fixed parameters for an ARCHModel model"><span class="pre">ARCHModelFixedResult</span></a></span></span></span><a class="headerlink" href="#arch.univariate.LS.fix" title="Link to this definition">¶</a></dt>
 <dd><p>Allows an ARCHModelFixedResult to be constructed from fixed parameters.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.LS.fix-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.LS.fix-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.LS.fix.params"><span class="n sig-name">params</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.univariate.LS.fix.params" title="Permalink to this definition">¶</a></dt><dd><p>User specified parameters to use when generating the result. Must
+<dt id="arch.univariate.LS.fix.params"><span class="n sig-name">params</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a></span></span><a class="headerlink" href="#arch.univariate.LS.fix.params" title="Permalink to this definition">¶</a></dt><dd><p>User specified parameters to use when generating the result. Must
 have the correct number of parameters for a given choice of mean
 model, volatility model and distribution.</p>
 </dd>
-<dt id="arch.univariate.LS.fix.first_obs"><span class="n sig-name">first_obs</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)">str</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.12)">datetime</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)">datetime64</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.1)">Timestamp</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.LS.fix.first_obs" title="Permalink to this definition">¶</a></dt><dd><p>First observation to use when fixing model</p>
+<dt id="arch.univariate.LS.fix.first_obs"><span class="n sig-name">first_obs</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)">str</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.12)">datetime</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)">datetime64</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.4)">Timestamp</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.LS.fix.first_obs" title="Permalink to this definition">¶</a></dt><dd><p>First observation to use when fixing model</p>
 </dd>
-<dt id="arch.univariate.LS.fix.last_obs"><span class="n sig-name">last_obs</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)">str</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.12)">datetime</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)">datetime64</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.1)">Timestamp</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.LS.fix.last_obs" title="Permalink to this definition">¶</a></dt><dd><p>Last observation to use when fixing model</p>
+<dt id="arch.univariate.LS.fix.last_obs"><span class="n sig-name">last_obs</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)">str</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.12)">datetime</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)">datetime64</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.4)">Timestamp</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.LS.fix.last_obs" title="Permalink to this definition">¶</a></dt><dd><p>Last observation to use when fixing model</p>
 </dd>
 </dl>
 </dd>
@@ -1344,7 +1344,7 @@ <h1 id="arch-univariate-ls-fix">arch.univariate.LS.fix<a class="headerlink" href
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index 5d1d023cbe..c6a74d5d7e 100644
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-        <title>arch.univariate.LS.forecast - arch v6.2.1 (+21)</title>
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@@ -102,7 +102,7 @@
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-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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@@ -189,10 +189,10 @@
 
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@@ -1302,17 +1302,17 @@
 <h1 id="arch-univariate-ls-forecast">arch.univariate.LS.forecast<a class="headerlink" href="#arch-univariate-ls-forecast" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.LS.forecast">
-<span class="sig-prename descclassname"><span class="pre">LS.</span></span><span class="sig-name descname"><span class="pre">forecast</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.LS.forecast.params" title="arch.univariate.LS.forecast.params (Python parameter) — Parameters required to forecast. Must be identical in shape to the parameters computed by fitting the model."><span class="n"><span class="pre">params</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.LS.forecast.horizon" title="arch.univariate.LS.forecast.horizon (Python parameter) — Number of steps to forecast"><span class="n"><span class="pre">horizon</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.LS.forecast.start" title="arch.univariate.LS.forecast.start (Python parameter) — An integer, datetime or str indicating the first observation to produce the forecast for.  Datetimes can only be used with pandas inputs that have a datetime index. Strings must be convertible to a date time, such as in '1945-01-01'."><span class="n"><span class="pre">start</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.12)"><span class="pre">datetime</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)"><span class="pre">datetime64</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.1)"><span class="pre">Timestamp</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.LS.forecast.align" title="arch.univariate.LS.forecast.align (Python parameter) — Either 'origin' or 'target'.  When set of 'origin', the t-th row of forecasts contains the forecasts for t+1, t+2, ..., t+h. When set to 'target', the t-th row contains the 1-step ahead forecast from time t-1, the 2 step from time t-2, ..., and the h-step from time t-h.  'target' simplified computing forecast errors since the realization and h-step forecast are aligned."><span class="n"><span class="pre">align</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'origin'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'target'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;origin&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.LS.forecast.method" title="arch.univariate.LS.forecast.method (Python parameter) — Method to use when producing the forecast. The default is analytic. The method only affects the variance forecast generation.  Not all volatility models support all methods. In particular, volatility models that do not evolve in squares such as EGARCH or TARCH do not support the 'analytic' method for horizons &gt; 1."><span class="n"><span class="pre">method</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'analytic'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'simulation'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'bootstrap'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;analytic&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.LS.forecast.simulations" title="arch.univariate.LS.forecast.simulations (Python parameter) — Number of simulations to run when computing the forecast using either simulation or bootstrap."><span class="n"><span class="pre">simulations</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.LS.forecast.rng" title="arch.univariate.LS.forecast.rng (Python parameter) — Custom random number generator to use in simulation-based forecasts. Must produce random samples using the syntax rng(size) where size the 2-element tuple (simulations, horizon)."><span class="n"><span class="pre">rng</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.12)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.LS.forecast.random_state" title="arch.univariate.LS.forecast.random_state (Python parameter) — NumPy RandomState instance to use when method is 'bootstrap'"><span class="n"><span class="pre">random_state</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.26)"><span class="pre">RandomState</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.LS.forecast.reindex" title="arch.univariate.LS.forecast.reindex (Python parameter) — Whether to reindex the forecasts to have the same dimension as the series being forecast. Prior to 4.18 this was the default. As of 4.19 this is now optional. If not provided, a warning is raised about the future change in the default which will occur after September 2021."><span class="n"><span class="pre">reindex</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.LS.forecast.x" title="arch.univariate.LS.forecast.x (Python parameter) — Values to use for exogenous regressors if any are included in the model. Three formats are accepted:"><span class="n"><span class="pre">x</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Hashable" title="(in Python v3.12)"><span class="pre">Hashable</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference internal" href="../arch.univariate.base.ARCHModelForecast.html#arch.univariate.base.ARCHModelForecast" title="arch.univariate.base.ARCHModelForecast (Python class) — Container for forecasts from an ARCH Model"><span class="pre">ARCHModelForecast</span></a></span></span></span><a class="headerlink" href="#arch.univariate.LS.forecast" title="Link to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">LS.</span></span><span class="sig-name descname"><span class="pre">forecast</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.LS.forecast.params" title="arch.univariate.LS.forecast.params (Python parameter) — Parameters required to forecast. Must be identical in shape to the parameters computed by fitting the model."><span class="n"><span class="pre">params</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.LS.forecast.horizon" title="arch.univariate.LS.forecast.horizon (Python parameter) — Number of steps to forecast"><span class="n"><span class="pre">horizon</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.LS.forecast.start" title="arch.univariate.LS.forecast.start (Python parameter) — An integer, datetime or str indicating the first observation to produce the forecast for.  Datetimes can only be used with pandas inputs that have a datetime index. Strings must be convertible to a date time, such as in '1945-01-01'."><span class="n"><span class="pre">start</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.12)"><span class="pre">datetime</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)"><span class="pre">datetime64</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.4)"><span class="pre">Timestamp</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.LS.forecast.align" title="arch.univariate.LS.forecast.align (Python parameter) — Either 'origin' or 'target'.  When set of 'origin', the t-th row of forecasts contains the forecasts for t+1, t+2, ..., t+h. When set to 'target', the t-th row contains the 1-step ahead forecast from time t-1, the 2 step from time t-2, ..., and the h-step from time t-h.  'target' simplified computing forecast errors since the realization and h-step forecast are aligned."><span class="n"><span class="pre">align</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'origin'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'target'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;origin&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.LS.forecast.method" title="arch.univariate.LS.forecast.method (Python parameter) — Method to use when producing the forecast. The default is analytic. The method only affects the variance forecast generation.  Not all volatility models support all methods. In particular, volatility models that do not evolve in squares such as EGARCH or TARCH do not support the 'analytic' method for horizons &gt; 1."><span class="n"><span class="pre">method</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'analytic'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'simulation'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'bootstrap'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;analytic&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.LS.forecast.simulations" title="arch.univariate.LS.forecast.simulations (Python parameter) — Number of simulations to run when computing the forecast using either simulation or bootstrap."><span class="n"><span class="pre">simulations</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.LS.forecast.rng" title="arch.univariate.LS.forecast.rng (Python parameter) — Custom random number generator to use in simulation-based forecasts. Must produce random samples using the syntax rng(size) where size the 2-element tuple (simulations, horizon)."><span class="n"><span class="pre">rng</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.12)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.LS.forecast.random_state" title="arch.univariate.LS.forecast.random_state (Python parameter) — NumPy RandomState instance to use when method is 'bootstrap'"><span class="n"><span class="pre">random_state</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.26)"><span class="pre">RandomState</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.LS.forecast.reindex" title="arch.univariate.LS.forecast.reindex (Python parameter) — Whether to reindex the forecasts to have the same dimension as the series being forecast. Prior to 4.18 this was the default. As of 4.19 this is now optional. If not provided, a warning is raised about the future change in the default which will occur after September 2021."><span class="n"><span class="pre">reindex</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.LS.forecast.x" title="arch.univariate.LS.forecast.x (Python parameter) — Values to use for exogenous regressors if any are included in the model. Three formats are accepted:"><span class="n"><span class="pre">x</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Hashable" title="(in Python v3.12)"><span class="pre">Hashable</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference internal" href="../arch.univariate.base.ARCHModelForecast.html#arch.univariate.base.ARCHModelForecast" title="arch.univariate.base.ARCHModelForecast (Python class) — Container for forecasts from an ARCH Model"><span class="pre">ARCHModelForecast</span></a></span></span></span><a class="headerlink" href="#arch.univariate.LS.forecast" title="Link to this definition">¶</a></dt>
 <dd><p>Construct forecasts from estimated model</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.LS.forecast-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.LS.forecast-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.LS.forecast.params"><span class="n sig-name">params</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.univariate.LS.forecast.params" title="Permalink to this definition">¶</a></dt><dd><p>Parameters required to forecast. Must be identical in
+<dt id="arch.univariate.LS.forecast.params"><span class="n sig-name">params</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a></span></span><a class="headerlink" href="#arch.univariate.LS.forecast.params" title="Permalink to this definition">¶</a></dt><dd><p>Parameters required to forecast. Must be identical in
 shape to the parameters computed by fitting the model.</p>
 </dd>
 <dt id="arch.univariate.LS.forecast.horizon"><span class="n sig-name">horizon</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code><a class="headerlink" href="#arch.univariate.LS.forecast.horizon" title="Permalink to this definition">¶</a></dt><dd><p>Number of steps to forecast</p>
 </dd>
-<dt id="arch.univariate.LS.forecast.start"><span class="n sig-name">start</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)">str</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.12)">datetime</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)">datetime64</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.1)">Timestamp</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.LS.forecast.start" title="Permalink to this definition">¶</a></dt><dd><p>An integer, datetime or str indicating the first observation to
+<dt id="arch.univariate.LS.forecast.start"><span class="n sig-name">start</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)">str</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.12)">datetime</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)">datetime64</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.4)">Timestamp</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.LS.forecast.start" title="Permalink to this definition">¶</a></dt><dd><p>An integer, datetime or str indicating the first observation to
 produce the forecast for.  Datetimes can only be used with pandas
 inputs that have a datetime index. Strings must be convertible
 to a date time, such as in ‘1945-01-01’.</p>
@@ -1348,7 +1348,7 @@ <h1 id="arch-univariate-ls-forecast">arch.univariate.LS.forecast<a class="header
 </div>
 </p>
 </dd>
-<dt id="arch.univariate.LS.forecast.x"><span class="n sig-name">x</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)">dict</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Hashable" title="(in Python v3.12)">Hashable</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.LS.forecast.x" title="Permalink to this definition">¶</a></dt><dd><p><p>Values to use for exogenous regressors if any are included in the
+<dt id="arch.univariate.LS.forecast.x"><span class="n sig-name">x</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)">dict</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Hashable" title="(in Python v3.12)">Hashable</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.LS.forecast.x" title="Permalink to this definition">¶</a></dt><dd><p><p>Values to use for exogenous regressors if any are included in the
 model. Three formats are accepted:</p>
 <ul class="simple">
 <li><p>2-d array-like: This format can be used when there is a single
@@ -1530,7 +1530,7 @@ <h1 id="arch-univariate-ls-forecast">arch.univariate.LS.forecast<a class="header
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diff --git a/devel/univariate/generated/generated/arch.univariate.LS.name.html b/devel/univariate/generated/generated/arch.univariate.LS.name.html
index 83cdf4dc92..a61c102dbd 100644
--- a/devel/univariate/generated/generated/arch.univariate.LS.name.html
+++ b/devel/univariate/generated/generated/arch.univariate.LS.name.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.LS.name - arch v6.2.1 (+21)</title>
+        <title>arch.univariate.LS.name - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
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+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
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@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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@@ -1214,7 +1214,7 @@ <h1 id="arch-univariate-ls-name">arch.univariate.LS.name<a class="headerlink" hr
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+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
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diff --git a/devel/univariate/generated/generated/arch.univariate.LS.num_params.html b/devel/univariate/generated/generated/arch.univariate.LS.num_params.html
index 1dad3f0ce2..68472f7ada 100644
--- a/devel/univariate/generated/generated/arch.univariate.LS.num_params.html
+++ b/devel/univariate/generated/generated/arch.univariate.LS.num_params.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.LS.num_params - arch v6.2.1 (+21)</title>
+        <title>arch.univariate.LS.num_params - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
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   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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     <div class="md-nav__source">
@@ -1214,7 +1214,7 @@ <h1 id="arch-univariate-ls-num-params">arch.univariate.LS.num_params<a class="he
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diff --git a/devel/univariate/generated/generated/arch.univariate.LS.parameter_names.html b/devel/univariate/generated/generated/arch.univariate.LS.parameter_names.html
index 6f570ecd46..7d95e8060b 100644
--- a/devel/univariate/generated/generated/arch.univariate.LS.parameter_names.html
+++ b/devel/univariate/generated/generated/arch.univariate.LS.parameter_names.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.LS.parameter_names - arch v6.2.1 (+21)</title>
+        <title>arch.univariate.LS.parameter_names - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
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@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
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-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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@@ -1262,7 +1262,7 @@ <h1 id="arch-univariate-ls-parameter-names">arch.univariate.LS.parameter_names<a
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diff --git a/devel/univariate/generated/generated/arch.univariate.LS.resids.html b/devel/univariate/generated/generated/arch.univariate.LS.resids.html
index b1c5894b60..c2a390d6fc 100644
--- a/devel/univariate/generated/generated/arch.univariate.LS.resids.html
+++ b/devel/univariate/generated/generated/arch.univariate.LS.resids.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.LS.resids - arch v6.2.1 (+21)</title>
+        <title>arch.univariate.LS.resids - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
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@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
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@@ -1204,16 +1204,16 @@
 <h1 id="arch-univariate-ls-resids">arch.univariate.LS.resids<a class="headerlink" href="#arch-univariate-ls-resids" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.LS.resids">
-<span class="sig-prename descclassname"><span class="pre">LS.</span></span><span class="sig-name descname"><span class="pre">resids</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.LS.resids.params" title="arch.univariate.LS.resids.params (Python parameter) — Model parameters"><span class="n"><span class="pre">params</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.LS.resids.y" title="arch.univariate.LS.resids.y (Python parameter) — Alternative values to use when computing model residuals"><span class="n"><span class="pre">y</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.LS.resids.regressors" title="arch.univariate.LS.resids.regressors (Python parameter) — Alternative regressor values to use when computing model residuals"><span class="n"><span class="pre">regressors</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></span><a class="headerlink" href="#arch.univariate.LS.resids" title="Link to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">LS.</span></span><span class="sig-name descname"><span class="pre">resids</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.LS.resids.params" title="arch.univariate.LS.resids.params (Python parameter) — Model parameters"><span class="n"><span class="pre">params</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.LS.resids.y" title="arch.univariate.LS.resids.y (Python parameter) — Alternative values to use when computing model residuals"><span class="n"><span class="pre">y</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.LS.resids.regressors" title="arch.univariate.LS.resids.regressors (Python parameter) — Alternative regressor values to use when computing model residuals"><span class="n"><span class="pre">regressors</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a></span></span></span><a class="headerlink" href="#arch.univariate.LS.resids" title="Link to this definition">¶</a></dt>
 <dd><p>Compute model residuals</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.LS.resids-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.LS.resids-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
 <dt id="arch.univariate.LS.resids.params"><span class="n sig-name">params</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.LS.resids.params" title="Permalink to this definition">¶</a></dt><dd><p>Model parameters</p>
 </dd>
-<dt id="arch.univariate.LS.resids.y"><span class="n sig-name">y</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.LS.resids.y" title="Permalink to this definition">¶</a></dt><dd><p>Alternative values to use when computing model residuals</p>
+<dt id="arch.univariate.LS.resids.y"><span class="n sig-name">y</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.LS.resids.y" title="Permalink to this definition">¶</a></dt><dd><p>Alternative values to use when computing model residuals</p>
 </dd>
-<dt id="arch.univariate.LS.resids.regressors"><span class="n sig-name">regressors</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.LS.resids.regressors" title="Permalink to this definition">¶</a></dt><dd><p>Alternative regressor values to use when computing model residuals</p>
+<dt id="arch.univariate.LS.resids.regressors"><span class="n sig-name">regressors</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.LS.resids.regressors" title="Permalink to this definition">¶</a></dt><dd><p>Alternative regressor values to use when computing model residuals</p>
 </dd>
 </dl>
 </dd>
@@ -1340,7 +1340,7 @@ <h1 id="arch-univariate-ls-resids">arch.univariate.LS.resids<a class="headerlink
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diff --git a/devel/univariate/generated/generated/arch.univariate.LS.simulate.html b/devel/univariate/generated/generated/arch.univariate.LS.simulate.html
index 0256c7ea7c..b273f07cf6 100644
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@@ -1246,12 +1246,12 @@
 <h1 id="arch-univariate-ls-simulate">arch.univariate.LS.simulate<a class="headerlink" href="#arch-univariate-ls-simulate" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.LS.simulate">
-<span class="sig-prename descclassname"><span class="pre">LS.</span></span><span class="sig-name descname"><span class="pre">simulate</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.LS.simulate.params" title="arch.univariate.LS.simulate.params (Python parameter) — Parameters to use when simulating the model.  Parameter order is [mean volatility distribution] where the parameters of the mean model are ordered [constant lag[0] lag[1] ... lag[p] ex[0] ... ex[k-1]] where lag[j] indicates the coefficient on the jth lag in the model and ex[j] is the coefficient on the jth exogenous variable."><span class="n"><span class="pre">params</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.LS.simulate.nobs" title="arch.univariate.LS.simulate.nobs (Python parameter) — Length of series to simulate"><span class="n"><span class="pre">nobs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.LS.simulate.burn" title="arch.univariate.LS.simulate.burn (Python parameter) — Number of values to simulate to initialize the model and remove dependence on initial values."><span class="n"><span class="pre">burn</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">500</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.LS.simulate.initial_value" title="arch.univariate.LS.simulate.initial_value (Python parameter) — Either a scalar value or max(lags) array set of initial values to use when initializing the model.  If omitted, 0.0 is used."><span class="n"><span class="pre">initial_value</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.LS.simulate.x" title="arch.univariate.LS.simulate.x (Python parameter) — nobs + burn by k array of exogenous variables to include in the simulation."><span class="n"><span class="pre">x</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.LS.simulate.initial_value_vol" title="arch.univariate.LS.simulate.initial_value_vol (Python parameter) — An array or scalar to use when initializing the volatility process."><span class="n"><span class="pre">initial_value_vol</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a></span></span></span><a class="headerlink" href="#arch.univariate.LS.simulate" title="Link to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">LS.</span></span><span class="sig-name descname"><span class="pre">simulate</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.LS.simulate.params" title="arch.univariate.LS.simulate.params (Python parameter) — Parameters to use when simulating the model.  Parameter order is [mean volatility distribution] where the parameters of the mean model are ordered [constant lag[0] lag[1] ... lag[p] ex[0] ... ex[k-1]] where lag[j] indicates the coefficient on the jth lag in the model and ex[j] is the coefficient on the jth exogenous variable."><span class="n"><span class="pre">params</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.LS.simulate.nobs" title="arch.univariate.LS.simulate.nobs (Python parameter) — Length of series to simulate"><span class="n"><span class="pre">nobs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.LS.simulate.burn" title="arch.univariate.LS.simulate.burn (Python parameter) — Number of values to simulate to initialize the model and remove dependence on initial values."><span class="n"><span class="pre">burn</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">500</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.LS.simulate.initial_value" title="arch.univariate.LS.simulate.initial_value (Python parameter) — Either a scalar value or max(lags) array set of initial values to use when initializing the model.  If omitted, 0.0 is used."><span class="n"><span class="pre">initial_value</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.LS.simulate.x" title="arch.univariate.LS.simulate.x (Python parameter) — nobs + burn by k array of exogenous variables to include in the simulation."><span class="n"><span class="pre">x</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.LS.simulate.initial_value_vol" title="arch.univariate.LS.simulate.initial_value_vol (Python parameter) — An array or scalar to use when initializing the volatility process."><span class="n"><span class="pre">initial_value_vol</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><span class="pre">DataFrame</span></a></span></span></span><a class="headerlink" href="#arch.univariate.LS.simulate" title="Link to this definition">¶</a></dt>
 <dd><p>Simulates data from a linear regression, AR or HAR models</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.LS.simulate-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.LS.simulate-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.LS.simulate.params"><span class="n sig-name">params</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="p">]</span></span></span><a class="headerlink" href="#arch.univariate.LS.simulate.params" title="Permalink to this definition">¶</a></dt><dd><p>Parameters to use when simulating the model.  Parameter order is
+<dt id="arch.univariate.LS.simulate.params"><span class="n sig-name">params</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="p">]</span></span></span><a class="headerlink" href="#arch.univariate.LS.simulate.params" title="Permalink to this definition">¶</a></dt><dd><p>Parameters to use when simulating the model.  Parameter order is
 [mean volatility distribution] where the parameters of the mean
 model are ordered [constant lag[0] lag[1] … lag[p] ex[0] …
 ex[k-1]] where lag[j] indicates the coefficient on the jth lag in
@@ -1266,7 +1266,7 @@ <h1 id="arch-univariate-ls-simulate">arch.univariate.LS.simulate<a class="header
 <dt id="arch.univariate.LS.simulate.initial_value"><span class="n sig-name">initial_value</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.LS.simulate.initial_value" title="Permalink to this definition">¶</a></dt><dd><p>Either a scalar value or <cite>max(lags)</cite> array set of initial values to
 use when initializing the model.  If omitted, 0.0 is used.</p>
 </dd>
-<dt id="arch.univariate.LS.simulate.x"><span class="n sig-name">x</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.LS.simulate.x" title="Permalink to this definition">¶</a></dt><dd><p>nobs + burn by k array of exogenous variables to include in the
+<dt id="arch.univariate.LS.simulate.x"><span class="n sig-name">x</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.LS.simulate.x" title="Permalink to this definition">¶</a></dt><dd><p>nobs + burn by k array of exogenous variables to include in the
 simulation.</p>
 </dd>
 <dt id="arch.univariate.LS.simulate.initial_value_vol"><span class="n sig-name">initial_value_vol</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.LS.simulate.initial_value_vol" title="Permalink to this definition">¶</a></dt><dd><p>An array or scalar to use when initializing the volatility process.</p>
@@ -1279,7 +1279,7 @@ <h1 id="arch-univariate-ls-simulate">arch.univariate.LS.simulate<a class="header
 containing the errors used in the simulation</p>
 </dd>
 <dt class="field-odd" id="arch.univariate.LS.simulate-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.LS.simulate-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><code class="xref py py-class docutils literal notranslate"><span class="pre">pandas.DataFrame</span></code></a></p>
+<dd class="field-odd"><p><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><code class="xref py py-class docutils literal notranslate"><span class="pre">pandas.DataFrame</span></code></a></p>
 </dd>
 </dl>
 <p class="example rubric">Examples</p>
@@ -1419,7 +1419,7 @@ <h1 id="arch-univariate-ls-simulate">arch.univariate.LS.simulate<a class="header
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/devel/univariate/generated/generated/arch.univariate.LS.starting_values.html b/devel/univariate/generated/generated/arch.univariate.LS.starting_values.html
index d631adabc7..bb840b3684 100644
--- a/devel/univariate/generated/generated/arch.univariate.LS.starting_values.html
+++ b/devel/univariate/generated/generated/arch.univariate.LS.starting_values.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.LS.starting_values - arch v6.2.1 (+21)</title>
+        <title>arch.univariate.LS.starting_values - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -1263,7 +1263,7 @@ <h1 id="arch-univariate-ls-starting-values">arch.univariate.LS.starting_values<a
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/devel/univariate/generated/generated/arch.univariate.LS.volatility.html b/devel/univariate/generated/generated/arch.univariate.LS.volatility.html
index 4dbfb4b9c7..4fa68887ac 100644
--- a/devel/univariate/generated/generated/arch.univariate.LS.volatility.html
+++ b/devel/univariate/generated/generated/arch.univariate.LS.volatility.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.LS.volatility - arch v6.2.1 (+21)</title>
+        <title>arch.univariate.LS.volatility - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -1215,7 +1215,7 @@ <h1 id="arch-univariate-ls-volatility">arch.univariate.LS.volatility<a class="he
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/devel/univariate/generated/generated/arch.univariate.LS.x.html b/devel/univariate/generated/generated/arch.univariate.LS.x.html
index 5706d78ae3..dacec320d3 100644
--- a/devel/univariate/generated/generated/arch.univariate.LS.x.html
+++ b/devel/univariate/generated/generated/arch.univariate.LS.x.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.LS.x - arch v6.2.1 (+21)</title>
+        <title>arch.univariate.LS.x - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -1096,7 +1096,7 @@
 <h1 id="arch-univariate-ls-x">arch.univariate.LS.x<a class="headerlink" href="#arch-univariate-ls-x" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.LS.x">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">LS.</span></span><span class="sig-name descname"><span class="pre">x</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span><a class="headerlink" href="#arch.univariate.LS.x" title="Link to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">LS.</span></span><span class="sig-name descname"><span class="pre">x</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span><a class="headerlink" href="#arch.univariate.LS.x" title="Link to this definition">¶</a></dt>
 <dd><p>Gets the value of the exogenous regressors in the model</p>
 </dd></dl>
 
@@ -1214,7 +1214,7 @@ <h1 id="arch-univariate-ls-x">arch.univariate.LS.x<a class="headerlink" href="#a
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/devel/univariate/generated/generated/arch.univariate.LS.y.html b/devel/univariate/generated/generated/arch.univariate.LS.y.html
index c66eccf7e3..05c76f2967 100644
--- a/devel/univariate/generated/generated/arch.univariate.LS.y.html
+++ b/devel/univariate/generated/generated/arch.univariate.LS.y.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.LS.y - arch v6.2.1 (+21)</title>
+        <title>arch.univariate.LS.y - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -1096,7 +1096,7 @@
 <h1 id="arch-univariate-ls-y">arch.univariate.LS.y<a class="headerlink" href="#arch-univariate-ls-y" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.LS.y">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">LS.</span></span><span class="sig-name descname"><span class="pre">y</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span><a class="headerlink" href="#arch.univariate.LS.y" title="Link to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">LS.</span></span><span class="sig-name descname"><span class="pre">y</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span><a class="headerlink" href="#arch.univariate.LS.y" title="Link to this definition">¶</a></dt>
 <dd><p>Returns the dependent variable</p>
 </dd></dl>
 
@@ -1214,7 +1214,7 @@ <h1 id="arch-univariate-ls-y">arch.univariate.LS.y<a class="headerlink" href="#a
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
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diff --git a/devel/univariate/generated/generated/arch.univariate.MIDASHyperbolic.backcast.html b/devel/univariate/generated/generated/arch.univariate.MIDASHyperbolic.backcast.html
index db2cd0bb50..bb9ecac302 100644
--- a/devel/univariate/generated/generated/arch.univariate.MIDASHyperbolic.backcast.html
+++ b/devel/univariate/generated/generated/arch.univariate.MIDASHyperbolic.backcast.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.MIDASHyperbolic.backcast - arch v6.2.1 (+21)</title>
+        <title>arch.univariate.MIDASHyperbolic.backcast - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -1383,7 +1383,7 @@ <h1 id="arch-univariate-midashyperbolic-backcast">arch.univariate.MIDASHyperboli
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/devel/univariate/generated/generated/arch.univariate.MIDASHyperbolic.backcast_transform.html b/devel/univariate/generated/generated/arch.univariate.MIDASHyperbolic.backcast_transform.html
index 23081933d0..e9e7fab13a 100644
--- a/devel/univariate/generated/generated/arch.univariate.MIDASHyperbolic.backcast_transform.html
+++ b/devel/univariate/generated/generated/arch.univariate.MIDASHyperbolic.backcast_transform.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.MIDASHyperbolic.backcast_transform - arch v6.2.1 (+21)</title>
+        <title>arch.univariate.MIDASHyperbolic.backcast_transform - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -1383,7 +1383,7 @@ <h1 id="arch-univariate-midashyperbolic-backcast-transform">arch.univariate.MIDA
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/devel/univariate/generated/generated/arch.univariate.MIDASHyperbolic.bounds.html b/devel/univariate/generated/generated/arch.univariate.MIDASHyperbolic.bounds.html
index 78b57aeace..b8a2b6d199 100644
--- a/devel/univariate/generated/generated/arch.univariate.MIDASHyperbolic.bounds.html
+++ b/devel/univariate/generated/generated/arch.univariate.MIDASHyperbolic.bounds.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.MIDASHyperbolic.bounds - arch v6.2.1 (+21)</title>
+        <title>arch.univariate.MIDASHyperbolic.bounds - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -1383,7 +1383,7 @@ <h1 id="arch-univariate-midashyperbolic-bounds">arch.univariate.MIDASHyperbolic.
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
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diff --git a/devel/univariate/generated/generated/arch.univariate.MIDASHyperbolic.compute_variance.html b/devel/univariate/generated/generated/arch.univariate.MIDASHyperbolic.compute_variance.html
index e4a90b7dc1..1ee035a5ed 100644
--- a/devel/univariate/generated/generated/arch.univariate.MIDASHyperbolic.compute_variance.html
+++ b/devel/univariate/generated/generated/arch.univariate.MIDASHyperbolic.compute_variance.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.MIDASHyperbolic.compute_variance - arch v6.2.1 (+21)</title>
+        <title>arch.univariate.MIDASHyperbolic.compute_variance - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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@@ -1414,7 +1414,7 @@ <h1 id="arch-univariate-midashyperbolic-compute-variance">arch.univariate.MIDASH
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diff --git a/devel/univariate/generated/generated/arch.univariate.MIDASHyperbolic.constraints.html b/devel/univariate/generated/generated/arch.univariate.MIDASHyperbolic.constraints.html
index 222c4b76af..599b938ee9 100644
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+++ b/devel/univariate/generated/generated/arch.univariate.MIDASHyperbolic.constraints.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.MIDASHyperbolic.constraints - arch v6.2.1 (+21)</title>
+        <title>arch.univariate.MIDASHyperbolic.constraints - arch v6.2.1 (+23)</title>
       
     
 
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+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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@@ -1299,7 +1299,7 @@ <h1 id="arch-univariate-midashyperbolic-constraints">arch.univariate.MIDASHyperb
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diff --git a/devel/univariate/generated/generated/arch.univariate.MIDASHyperbolic.forecast.html b/devel/univariate/generated/generated/arch.univariate.MIDASHyperbolic.forecast.html
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-        <title>arch.univariate.MIDASHyperbolic.forecast - arch v6.2.1 (+21)</title>
+        <title>arch.univariate.MIDASHyperbolic.forecast - arch v6.2.1 (+23)</title>
       
     
 
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 <h1 id="arch-univariate-midashyperbolic-forecast">arch.univariate.MIDASHyperbolic.forecast<a class="headerlink" href="#arch-univariate-midashyperbolic-forecast" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.MIDASHyperbolic.forecast">
-<span class="sig-prename descclassname"><span class="pre">MIDASHyperbolic.</span></span><span class="sig-name descname"><span class="pre">forecast</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.MIDASHyperbolic.forecast.parameters" title="arch.univariate.MIDASHyperbolic.forecast.parameters (Python parameter) — Parameters required to forecast the volatility model"><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.MIDASHyperbolic.forecast.resids" title="arch.univariate.MIDASHyperbolic.forecast.resids (Python parameter) — Residuals to use in the recursion"><span class="n"><span class="pre">resids</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.MIDASHyperbolic.forecast.backcast" title="arch.univariate.MIDASHyperbolic.forecast.backcast (Python parameter) — Value to use when initializing the recursion"><span class="n"><span class="pre">backcast</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.MIDASHyperbolic.forecast.var_bounds" title="arch.univariate.MIDASHyperbolic.forecast.var_bounds (Python parameter) — Array containing columns of lower and upper bounds"><span class="n"><span class="pre">var_bounds</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.MIDASHyperbolic.forecast.start" title="arch.univariate.MIDASHyperbolic.forecast.start (Python parameter) — Index of the first observation to use as the starting point for the forecast.  Default is len(resids)."><span class="n"><span class="pre">start</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.MIDASHyperbolic.forecast.horizon" title="arch.univariate.MIDASHyperbolic.forecast.horizon (Python parameter) — Forecast horizon.  Must be 1 or larger.  Forecasts are produced for horizons in [1, horizon]."><span class="n"><span class="pre">horizon</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.MIDASHyperbolic.forecast.method" title="arch.univariate.MIDASHyperbolic.forecast.method (Python parameter) — Method to use when producing the forecast. The default is analytic."><span class="n"><span class="pre">method</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'analytic'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'simulation'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'bootstrap'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;analytic&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.MIDASHyperbolic.forecast.simulations" title="arch.univariate.MIDASHyperbolic.forecast.simulations (Python parameter) — Number of simulations to run when computing the forecast using either simulation or bootstrap."><span class="n"><span class="pre">simulations</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.MIDASHyperbolic.forecast.rng" title="arch.univariate.MIDASHyperbolic.forecast.rng (Python parameter) — Callable random number generator required if method is 'simulation'. Must take a single shape input and return random samples numbers with that shape."><span class="n"><span class="pre">rng</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.12)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.MIDASHyperbolic.forecast.random_state" title="arch.univariate.MIDASHyperbolic.forecast.random_state (Python parameter) — NumPy RandomState instance to use when method is 'bootstrap'"><span class="n"><span class="pre">random_state</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.26)"><span class="pre">RandomState</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><span class="pre">VarianceForecast</span></span></span></span><a class="headerlink" href="#arch.univariate.MIDASHyperbolic.forecast" title="Link to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">MIDASHyperbolic.</span></span><span class="sig-name descname"><span class="pre">forecast</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.MIDASHyperbolic.forecast.parameters" title="arch.univariate.MIDASHyperbolic.forecast.parameters (Python parameter) — Parameters required to forecast the volatility model"><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.MIDASHyperbolic.forecast.resids" title="arch.univariate.MIDASHyperbolic.forecast.resids (Python parameter) — Residuals to use in the recursion"><span class="n"><span class="pre">resids</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.MIDASHyperbolic.forecast.backcast" title="arch.univariate.MIDASHyperbolic.forecast.backcast (Python parameter) — Value to use when initializing the recursion"><span class="n"><span class="pre">backcast</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.MIDASHyperbolic.forecast.var_bounds" title="arch.univariate.MIDASHyperbolic.forecast.var_bounds (Python parameter) — Array containing columns of lower and upper bounds"><span class="n"><span class="pre">var_bounds</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.MIDASHyperbolic.forecast.start" title="arch.univariate.MIDASHyperbolic.forecast.start (Python parameter) — Index of the first observation to use as the starting point for the forecast.  Default is len(resids)."><span class="n"><span class="pre">start</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.MIDASHyperbolic.forecast.horizon" title="arch.univariate.MIDASHyperbolic.forecast.horizon (Python parameter) — Forecast horizon.  Must be 1 or larger.  Forecasts are produced for horizons in [1, horizon]."><span class="n"><span class="pre">horizon</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.MIDASHyperbolic.forecast.method" title="arch.univariate.MIDASHyperbolic.forecast.method (Python parameter) — Method to use when producing the forecast. The default is analytic."><span class="n"><span class="pre">method</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'analytic'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'simulation'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'bootstrap'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;analytic&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.MIDASHyperbolic.forecast.simulations" title="arch.univariate.MIDASHyperbolic.forecast.simulations (Python parameter) — Number of simulations to run when computing the forecast using either simulation or bootstrap."><span class="n"><span class="pre">simulations</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.MIDASHyperbolic.forecast.rng" title="arch.univariate.MIDASHyperbolic.forecast.rng (Python parameter) — Callable random number generator required if method is 'simulation'. Must take a single shape input and return random samples numbers with that shape."><span class="n"><span class="pre">rng</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.12)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.MIDASHyperbolic.forecast.random_state" title="arch.univariate.MIDASHyperbolic.forecast.random_state (Python parameter) — NumPy RandomState instance to use when method is 'bootstrap'"><span class="n"><span class="pre">random_state</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.26)"><span class="pre">RandomState</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><span class="pre">VarianceForecast</span></span></span></span><a class="headerlink" href="#arch.univariate.MIDASHyperbolic.forecast" title="Link to this definition">¶</a></dt>
 <dd><p>Forecast volatility from the model</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.MIDASHyperbolic.forecast-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.MIDASHyperbolic.forecast-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.MIDASHyperbolic.forecast.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.univariate.MIDASHyperbolic.forecast.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Parameters required to forecast the volatility model</p>
+<dt id="arch.univariate.MIDASHyperbolic.forecast.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a></span></span><a class="headerlink" href="#arch.univariate.MIDASHyperbolic.forecast.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Parameters required to forecast the volatility model</p>
 </dd>
 <dt id="arch.univariate.MIDASHyperbolic.forecast.resids"><span class="n sig-name">resids</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.MIDASHyperbolic.forecast.resids" title="Permalink to this definition">¶</a></dt><dd><p>Residuals to use in the recursion</p>
 </dd>
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+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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index c572851301..ec8dad96c0 100644
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+++ b/devel/univariate/generated/generated/arch.univariate.MIDASHyperbolic.num_params.html
@@ -15,7 +15,7 @@
     
   
       
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@@ -102,7 +102,7 @@
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           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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@@ -189,10 +189,10 @@
 
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+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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@@ -1289,7 +1289,7 @@ <h1 id="arch-univariate-midashyperbolic-num-params">arch.univariate.MIDASHyperbo
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diff --git a/devel/univariate/generated/generated/arch.univariate.MIDASHyperbolic.parameter_names.html b/devel/univariate/generated/generated/arch.univariate.MIDASHyperbolic.parameter_names.html
index 9e236b3f3a..13c67bb8e7 100644
--- a/devel/univariate/generated/generated/arch.univariate.MIDASHyperbolic.parameter_names.html
+++ b/devel/univariate/generated/generated/arch.univariate.MIDASHyperbolic.parameter_names.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.MIDASHyperbolic.parameter_names - arch v6.2.1 (+21)</title>
+        <title>arch.univariate.MIDASHyperbolic.parameter_names - arch v6.2.1 (+23)</title>
       
     
 
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
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@@ -102,7 +102,7 @@
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+            arch v6.2.1 (+23)
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@@ -189,10 +189,10 @@
 
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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@@ -1337,7 +1337,7 @@ <h1 id="arch-univariate-midashyperbolic-parameter-names">arch.univariate.MIDASHy
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diff --git a/devel/univariate/generated/generated/arch.univariate.MIDASHyperbolic.simulate.html b/devel/univariate/generated/generated/arch.univariate.MIDASHyperbolic.simulate.html
index b6257b78c3..219b5345fa 100644
--- a/devel/univariate/generated/generated/arch.univariate.MIDASHyperbolic.simulate.html
+++ b/devel/univariate/generated/generated/arch.univariate.MIDASHyperbolic.simulate.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.MIDASHyperbolic.simulate - arch v6.2.1 (+21)</title>
+        <title>arch.univariate.MIDASHyperbolic.simulate - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
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@@ -92,7 +92,7 @@
 
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-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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@@ -189,10 +189,10 @@
 
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+    arch v6.2.1 (+23)
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@@ -1293,12 +1293,12 @@
 <h1 id="arch-univariate-midashyperbolic-simulate">arch.univariate.MIDASHyperbolic.simulate<a class="headerlink" href="#arch-univariate-midashyperbolic-simulate" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.MIDASHyperbolic.simulate">
-<span class="sig-prename descclassname"><span class="pre">MIDASHyperbolic.</span></span><span class="sig-name descname"><span class="pre">simulate</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.MIDASHyperbolic.simulate.parameters" title="arch.univariate.MIDASHyperbolic.simulate.parameters (Python parameter) — Parameters required to simulate the volatility model"><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.MIDASHyperbolic.simulate.nobs" title="arch.univariate.MIDASHyperbolic.simulate.nobs (Python parameter) — Number of data points to simulate"><span class="n"><span class="pre">nobs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.MIDASHyperbolic.simulate.rng" title="arch.univariate.MIDASHyperbolic.simulate.rng (Python parameter) — Callable function that takes a single integer input and returns a vector of random numbers"><span class="n"><span class="pre">rng</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.12)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.MIDASHyperbolic.simulate.burn" title="arch.univariate.MIDASHyperbolic.simulate.burn (Python parameter) — Number of additional observations to generate when initializing the simulation"><span class="n"><span class="pre">burn</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">500</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.MIDASHyperbolic.simulate.initial_value" title="arch.univariate.MIDASHyperbolic.simulate.initial_value (Python parameter) — Scalar or array of initial values to use when initializing the simulation"><span class="n"><span class="pre">initial_value</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#MIDASHyperbolic.simulate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.MIDASHyperbolic.simulate" title="Link to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">MIDASHyperbolic.</span></span><span class="sig-name descname"><span class="pre">simulate</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.MIDASHyperbolic.simulate.parameters" title="arch.univariate.MIDASHyperbolic.simulate.parameters (Python parameter) — Parameters required to simulate the volatility model"><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.MIDASHyperbolic.simulate.nobs" title="arch.univariate.MIDASHyperbolic.simulate.nobs (Python parameter) — Number of data points to simulate"><span class="n"><span class="pre">nobs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.MIDASHyperbolic.simulate.rng" title="arch.univariate.MIDASHyperbolic.simulate.rng (Python parameter) — Callable function that takes a single integer input and returns a vector of random numbers"><span class="n"><span class="pre">rng</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.12)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.MIDASHyperbolic.simulate.burn" title="arch.univariate.MIDASHyperbolic.simulate.burn (Python parameter) — Number of additional observations to generate when initializing the simulation"><span class="n"><span class="pre">burn</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">500</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.MIDASHyperbolic.simulate.initial_value" title="arch.univariate.MIDASHyperbolic.simulate.initial_value (Python parameter) — Scalar or array of initial values to use when initializing the simulation"><span class="n"><span class="pre">initial_value</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#MIDASHyperbolic.simulate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.MIDASHyperbolic.simulate" title="Link to this definition">¶</a></dt>
 <dd><p>Simulate data from the model</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.MIDASHyperbolic.simulate-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.MIDASHyperbolic.simulate-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.MIDASHyperbolic.simulate.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.univariate.MIDASHyperbolic.simulate.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Parameters required to simulate the volatility model</p>
+<dt id="arch.univariate.MIDASHyperbolic.simulate.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a></span></span><a class="headerlink" href="#arch.univariate.MIDASHyperbolic.simulate.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Parameters required to simulate the volatility model</p>
 </dd>
 <dt id="arch.univariate.MIDASHyperbolic.simulate.nobs"><span class="n sig-name">nobs</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a></span></span><a class="headerlink" href="#arch.univariate.MIDASHyperbolic.simulate.nobs" title="Permalink to this definition">¶</a></dt><dd><p>Number of data points to simulate</p>
 </dd>
@@ -1437,7 +1437,7 @@ <h1 id="arch-univariate-midashyperbolic-simulate">arch.univariate.MIDASHyperboli
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diff --git a/devel/univariate/generated/generated/arch.univariate.MIDASHyperbolic.start.html b/devel/univariate/generated/generated/arch.univariate.MIDASHyperbolic.start.html
index 0d6ab0f78b..1d4ec8e0d1 100644
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+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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diff --git a/devel/univariate/generated/generated/arch.univariate.MIDASHyperbolic.starting_values.html b/devel/univariate/generated/generated/arch.univariate.MIDASHyperbolic.starting_values.html
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+++ b/devel/univariate/generated/generated/arch.univariate.MIDASHyperbolic.starting_values.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.MIDASHyperbolic.starting_values - arch v6.2.1 (+21)</title>
+        <title>arch.univariate.MIDASHyperbolic.starting_values - arch v6.2.1 (+23)</title>
       
     
 
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+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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diff --git a/devel/univariate/generated/generated/arch.univariate.MIDASHyperbolic.stop.html b/devel/univariate/generated/generated/arch.univariate.MIDASHyperbolic.stop.html
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+++ b/devel/univariate/generated/generated/arch.univariate.MIDASHyperbolic.stop.html
@@ -15,7 +15,7 @@
     
   
       
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+        <title>arch.univariate.MIDASHyperbolic.stop - arch v6.2.1 (+23)</title>
       
     
 
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
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index 21110a8230..f7cfb20f7c 100644
--- a/devel/univariate/generated/generated/arch.univariate.MIDASHyperbolic.update.html
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@@ -15,7 +15,7 @@
     
   
       
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-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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diff --git a/devel/univariate/generated/generated/arch.univariate.MIDASHyperbolic.updateable.html b/devel/univariate/generated/generated/arch.univariate.MIDASHyperbolic.updateable.html
index 095e614ef0..f31e3e2baf 100644
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@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.MIDASHyperbolic.updateable - arch v6.2.1 (+21)</title>
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-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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+    arch v6.2.1 (+23)
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@@ -1289,7 +1289,7 @@ <h1 id="arch-univariate-midashyperbolic-updateable">arch.univariate.MIDASHyperbo
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-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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diff --git a/devel/univariate/generated/generated/arch.univariate.MIDASHyperbolic.variance_bounds.html b/devel/univariate/generated/generated/arch.univariate.MIDASHyperbolic.variance_bounds.html
index f13a085955..51ce62884c 100644
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+++ b/devel/univariate/generated/generated/arch.univariate.MIDASHyperbolic.variance_bounds.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.MIDASHyperbolic.variance_bounds - arch v6.2.1 (+21)</title>
+        <title>arch.univariate.MIDASHyperbolic.variance_bounds - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
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+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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+            arch v6.2.1 (+23)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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+    arch v6.2.1 (+23)
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-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
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diff --git a/devel/univariate/generated/generated/arch.univariate.MIDASHyperbolic.volatility_updater.html b/devel/univariate/generated/generated/arch.univariate.MIDASHyperbolic.volatility_updater.html
index c690301554..b7327aa508 100644
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+++ b/devel/univariate/generated/generated/arch.univariate.MIDASHyperbolic.volatility_updater.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.MIDASHyperbolic.volatility_updater - arch v6.2.1 (+21)</title>
+        <title>arch.univariate.MIDASHyperbolic.volatility_updater - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
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@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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@@ -1354,7 +1354,7 @@ <h1 id="arch-univariate-midashyperbolic-volatility-updater">arch.univariate.MIDA
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-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
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index 9288dae58e..85f1357224 100644
--- a/devel/univariate/generated/generated/arch.univariate.Normal.bounds.html
+++ b/devel/univariate/generated/generated/arch.univariate.Normal.bounds.html
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-        <title>arch.univariate.Normal.bounds - arch v6.2.1 (+21)</title>
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@@ -47,7 +47,7 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
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@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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+            arch v6.2.1 (+23)
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diff --git a/devel/univariate/generated/generated/arch.univariate.Normal.cdf.html b/devel/univariate/generated/generated/arch.univariate.Normal.cdf.html
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-    arch v6.2.1 (+21)
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@@ -1115,14 +1115,14 @@
 <h1 id="arch-univariate-normal-cdf">arch.univariate.Normal.cdf<a class="headerlink" href="#arch-univariate-normal-cdf" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.Normal.cdf">
-<span class="sig-prename descclassname"><span class="pre">Normal.</span></span><span class="sig-name descname"><span class="pre">cdf</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.Normal.cdf.resids" title="arch.univariate.Normal.cdf.resids (Python parameter) — Values at which to evaluate the cdf"><span class="n"><span class="pre">resids</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.Normal.cdf.parameters" title="arch.univariate.Normal.cdf.parameters (Python parameter) — Distribution parameters. Use None for parameterless distributions."><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/distribution.html#Normal.cdf"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.Normal.cdf" title="Link to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">Normal.</span></span><span class="sig-name descname"><span class="pre">cdf</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.Normal.cdf.resids" title="arch.univariate.Normal.cdf.resids (Python parameter) — Values at which to evaluate the cdf"><span class="n"><span class="pre">resids</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.Normal.cdf.parameters" title="arch.univariate.Normal.cdf.parameters (Python parameter) — Distribution parameters. Use None for parameterless distributions."><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/distribution.html#Normal.cdf"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.Normal.cdf" title="Link to this definition">¶</a></dt>
 <dd><p>Cumulative distribution function</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.Normal.cdf-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.Normal.cdf-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.Normal.cdf.resids"><span class="n sig-name">resids</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.univariate.Normal.cdf.resids" title="Permalink to this definition">¶</a></dt><dd><p>Values at which to evaluate the cdf</p>
+<dt id="arch.univariate.Normal.cdf.resids"><span class="n sig-name">resids</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a></span></span><a class="headerlink" href="#arch.univariate.Normal.cdf.resids" title="Permalink to this definition">¶</a></dt><dd><p>Values at which to evaluate the cdf</p>
 </dd>
-<dt id="arch.univariate.Normal.cdf.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.Normal.cdf.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Distribution parameters. Use <code class="docutils literal notranslate"><span class="pre">None</span></code> for parameterless
+<dt id="arch.univariate.Normal.cdf.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.Normal.cdf.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Distribution parameters. Use <code class="docutils literal notranslate"><span class="pre">None</span></code> for parameterless
 distributions.</p>
 </dd>
 </dl>
@@ -1250,7 +1250,7 @@ <h1 id="arch-univariate-normal-cdf">arch.univariate.Normal.cdf<a class="headerli
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diff --git a/devel/univariate/generated/generated/arch.univariate.Normal.constraints.html b/devel/univariate/generated/generated/arch.univariate.Normal.constraints.html
index f8969d32d6..d786f9d481 100644
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+        <title>arch.univariate.Normal.constraints - arch v6.2.1 (+23)</title>
       
     
 
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@@ -92,7 +92,7 @@
 
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@@ -102,7 +102,7 @@
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-            arch v6.2.1 (+21)
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@@ -189,10 +189,10 @@
 
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+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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@@ -1176,7 +1176,7 @@ <h1 id="arch-univariate-normal-constraints">arch.univariate.Normal.constraints<a
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diff --git a/devel/univariate/generated/generated/arch.univariate.Normal.generator.html b/devel/univariate/generated/generated/arch.univariate.Normal.generator.html
index 90974c5bf6..355abf6309 100644
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+++ b/devel/univariate/generated/generated/arch.univariate.Normal.generator.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.Normal.generator - arch v6.2.1 (+21)</title>
+        <title>arch.univariate.Normal.generator - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
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 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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diff --git a/devel/univariate/generated/generated/arch.univariate.Normal.loglikelihood.html b/devel/univariate/generated/generated/arch.univariate.Normal.loglikelihood.html
index bbfe41d869..16a9c27532 100644
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@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.Normal.loglikelihood - arch v6.2.1 (+21)</title>
+        <title>arch.univariate.Normal.loglikelihood - arch v6.2.1 (+23)</title>
       
     
 
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@@ -92,7 +92,7 @@
 
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-            arch v6.2.1 (+21)
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 <h1 id="arch-univariate-normal-loglikelihood">arch.univariate.Normal.loglikelihood<a class="headerlink" href="#arch-univariate-normal-loglikelihood" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.Normal.loglikelihood">
-<span class="sig-prename descclassname"><span class="pre">Normal.</span></span><span class="sig-name descname"><span class="pre">loglikelihood</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.Normal.loglikelihood.parameters" title="arch.univariate.Normal.loglikelihood.parameters (Python parameter) — The normal likelihood has no shape parameters. Empty since the standard normal has no shape parameters."><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.Normal.loglikelihood.resids" title="arch.univariate.Normal.loglikelihood.resids (Python parameter) — The residuals to use in the log-likelihood calculation"><span class="n"><span class="pre">resids</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.Normal.loglikelihood.sigma2" title="arch.univariate.Normal.loglikelihood.sigma2 (Python parameter) — Conditional variances of resids"><span class="n"><span class="pre">sigma2</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.Normal.loglikelihood.individual" title="arch.univariate.Normal.loglikelihood.individual (Python parameter) — Flag indicating whether to return the vector of individual log likelihoods (True) or the sum (False)"><span class="n"><span class="pre">individual</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/distribution.html#Normal.loglikelihood"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.Normal.loglikelihood" title="Link to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">Normal.</span></span><span class="sig-name descname"><span class="pre">loglikelihood</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.Normal.loglikelihood.parameters" title="arch.univariate.Normal.loglikelihood.parameters (Python parameter) — The normal likelihood has no shape parameters. Empty since the standard normal has no shape parameters."><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.Normal.loglikelihood.resids" title="arch.univariate.Normal.loglikelihood.resids (Python parameter) — The residuals to use in the log-likelihood calculation"><span class="n"><span class="pre">resids</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.Normal.loglikelihood.sigma2" title="arch.univariate.Normal.loglikelihood.sigma2 (Python parameter) — Conditional variances of resids"><span class="n"><span class="pre">sigma2</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.Normal.loglikelihood.individual" title="arch.univariate.Normal.loglikelihood.individual (Python parameter) — Flag indicating whether to return the vector of individual log likelihoods (True) or the sum (False)"><span class="n"><span class="pre">individual</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/distribution.html#Normal.loglikelihood"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.Normal.loglikelihood" title="Link to this definition">¶</a></dt>
 <dd><p>Computes the log-likelihood of assuming residuals are normally
 distributed, conditional on the variance</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.Normal.loglikelihood-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.Normal.loglikelihood-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.Normal.loglikelihood.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.univariate.Normal.loglikelihood.parameters" title="Permalink to this definition">¶</a></dt><dd><p>The normal likelihood has no shape parameters. Empty since the
+<dt id="arch.univariate.Normal.loglikelihood.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a></span></span><a class="headerlink" href="#arch.univariate.Normal.loglikelihood.parameters" title="Permalink to this definition">¶</a></dt><dd><p>The normal likelihood has no shape parameters. Empty since the
 standard normal has no shape parameters.</p>
 </dd>
-<dt id="arch.univariate.Normal.loglikelihood.resids"><span class="n sig-name">resids</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.univariate.Normal.loglikelihood.resids" title="Permalink to this definition">¶</a></dt><dd><p>The residuals to use in the log-likelihood calculation</p>
+<dt id="arch.univariate.Normal.loglikelihood.resids"><span class="n sig-name">resids</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a></span></span><a class="headerlink" href="#arch.univariate.Normal.loglikelihood.resids" title="Permalink to this definition">¶</a></dt><dd><p>The residuals to use in the log-likelihood calculation</p>
 </dd>
-<dt id="arch.univariate.Normal.loglikelihood.sigma2"><span class="n sig-name">sigma2</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.univariate.Normal.loglikelihood.sigma2" title="Permalink to this definition">¶</a></dt><dd><p>Conditional variances of resids</p>
+<dt id="arch.univariate.Normal.loglikelihood.sigma2"><span class="n sig-name">sigma2</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a></span></span><a class="headerlink" href="#arch.univariate.Normal.loglikelihood.sigma2" title="Permalink to this definition">¶</a></dt><dd><p>Conditional variances of resids</p>
 </dd>
 <dt id="arch.univariate.Normal.loglikelihood.individual"><span class="n sig-name">individual</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)">bool</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code><a class="headerlink" href="#arch.univariate.Normal.loglikelihood.individual" title="Permalink to this definition">¶</a></dt><dd><p>Flag indicating whether to return the vector of individual log
 likelihoods (True) or the sum (False)</p>
@@ -1289,7 +1289,7 @@ <h1 id="arch-univariate-normal-loglikelihood">arch.univariate.Normal.loglikeliho
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index 6a78686f98..2a8e35fd96 100644
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+++ b/devel/univariate/generated/generated/arch.univariate.Normal.moment.html
@@ -15,7 +15,7 @@
     
   
       
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@@ -1115,14 +1115,14 @@
 <h1 id="arch-univariate-normal-moment">arch.univariate.Normal.moment<a class="headerlink" href="#arch-univariate-normal-moment" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.Normal.moment">
-<span class="sig-prename descclassname"><span class="pre">Normal.</span></span><span class="sig-name descname"><span class="pre">moment</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.Normal.moment.n" title="arch.univariate.Normal.moment.n (Python parameter) — Order of moment"><span class="n"><span class="pre">n</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.Normal.moment.parameters" title="arch.univariate.Normal.moment.parameters (Python parameter) — Distribution parameters. Use None for parameterless distributions."><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/distribution.html#Normal.moment"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.Normal.moment" title="Link to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">Normal.</span></span><span class="sig-name descname"><span class="pre">moment</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.Normal.moment.n" title="arch.univariate.Normal.moment.n (Python parameter) — Order of moment"><span class="n"><span class="pre">n</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.Normal.moment.parameters" title="arch.univariate.Normal.moment.parameters (Python parameter) — Distribution parameters. Use None for parameterless distributions."><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/distribution.html#Normal.moment"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.Normal.moment" title="Link to this definition">¶</a></dt>
 <dd><p>Moment of order n</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.Normal.moment-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.Normal.moment-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
 <dt id="arch.univariate.Normal.moment.n"><span class="n sig-name">n</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a></span></span><a class="headerlink" href="#arch.univariate.Normal.moment.n" title="Permalink to this definition">¶</a></dt><dd><p>Order of moment</p>
 </dd>
-<dt id="arch.univariate.Normal.moment.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.Normal.moment.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Distribution parameters. Use None for parameterless distributions.</p>
+<dt id="arch.univariate.Normal.moment.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.Normal.moment.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Distribution parameters. Use None for parameterless distributions.</p>
 </dd>
 </dl>
 </dd>
@@ -1249,7 +1249,7 @@ <h1 id="arch-univariate-normal-moment">arch.univariate.Normal.moment<a class="he
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@@ -15,7 +15,7 @@
     
   
       
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diff --git a/devel/univariate/generated/generated/arch.univariate.Normal.parameter_names.html b/devel/univariate/generated/generated/arch.univariate.Normal.parameter_names.html
index fadb734963..613152442a 100644
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+++ b/devel/univariate/generated/generated/arch.univariate.Normal.parameter_names.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.Normal.parameter_names - arch v6.2.1 (+21)</title>
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@@ -47,7 +47,7 @@
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@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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+            arch v6.2.1 (+23)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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@@ -1187,7 +1187,7 @@ <h1 id="arch-univariate-normal-parameter-names">arch.univariate.Normal.parameter
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diff --git a/devel/univariate/generated/generated/arch.univariate.Normal.partial_moment.html b/devel/univariate/generated/generated/arch.univariate.Normal.partial_moment.html
index fb1912a314..07f2cb6f70 100644
--- a/devel/univariate/generated/generated/arch.univariate.Normal.partial_moment.html
+++ b/devel/univariate/generated/generated/arch.univariate.Normal.partial_moment.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.Normal.partial_moment - arch v6.2.1 (+21)</title>
+        <title>arch.univariate.Normal.partial_moment - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
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@@ -92,7 +92,7 @@
 
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 <h1 id="arch-univariate-normal-partial-moment">arch.univariate.Normal.partial_moment<a class="headerlink" href="#arch-univariate-normal-partial-moment" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.Normal.partial_moment">
-<span class="sig-prename descclassname"><span class="pre">Normal.</span></span><span class="sig-name descname"><span class="pre">partial_moment</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.Normal.partial_moment.n" title="arch.univariate.Normal.partial_moment.n (Python parameter) — Order of partial moment"><span class="n"><span class="pre">n</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.Normal.partial_moment.z" title="arch.univariate.Normal.partial_moment.z (Python parameter) — Upper bound for partial moment integral"><span class="n"><span class="pre">z</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mf">0.0</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.Normal.partial_moment.parameters" title="arch.univariate.Normal.partial_moment.parameters (Python parameter) — Distribution parameters.  Use None for parameterless distributions."><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/distribution.html#Normal.partial_moment"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.Normal.partial_moment" title="Link to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">Normal.</span></span><span class="sig-name descname"><span class="pre">partial_moment</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.Normal.partial_moment.n" title="arch.univariate.Normal.partial_moment.n (Python parameter) — Order of partial moment"><span class="n"><span class="pre">n</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.Normal.partial_moment.z" title="arch.univariate.Normal.partial_moment.z (Python parameter) — Upper bound for partial moment integral"><span class="n"><span class="pre">z</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mf">0.0</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.Normal.partial_moment.parameters" title="arch.univariate.Normal.partial_moment.parameters (Python parameter) — Distribution parameters.  Use None for parameterless distributions."><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/distribution.html#Normal.partial_moment"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.Normal.partial_moment" title="Link to this definition">¶</a></dt>
 <dd><p>Order n lower partial moment from -inf to z</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.Normal.partial_moment-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.Normal.partial_moment-parameters" title="Permalink to this headline">¶</a></dt>
@@ -1138,7 +1138,7 @@ <h1 id="arch-univariate-normal-partial-moment">arch.univariate.Normal.partial_mo
 </dd>
 <dt id="arch.univariate.Normal.partial_moment.z"><span class="n sig-name">z</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mf">0.0</span></code><a class="headerlink" href="#arch.univariate.Normal.partial_moment.z" title="Permalink to this definition">¶</a></dt><dd><p>Upper bound for partial moment integral</p>
 </dd>
-<dt id="arch.univariate.Normal.partial_moment.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.Normal.partial_moment.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Distribution parameters.  Use None for parameterless distributions.</p>
+<dt id="arch.univariate.Normal.partial_moment.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.Normal.partial_moment.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Distribution parameters.  Use None for parameterless distributions.</p>
 </dd>
 </dl>
 </dd>
@@ -1278,7 +1278,7 @@ <h1 id="arch-univariate-normal-partial-moment">arch.univariate.Normal.partial_mo
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index 6d7a450996..4a639e3f4d 100644
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+++ b/devel/univariate/generated/generated/arch.univariate.Normal.ppf.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.Normal.ppf - arch v6.2.1 (+21)</title>
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@@ -102,7 +102,7 @@
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-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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@@ -189,10 +189,10 @@
 
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+    arch v6.2.1 (+23)
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@@ -1115,14 +1115,14 @@
 <h1 id="arch-univariate-normal-ppf">arch.univariate.Normal.ppf<a class="headerlink" href="#arch-univariate-normal-ppf" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.Normal.ppf">
-<span class="sig-prename descclassname"><span class="pre">Normal.</span></span><span class="sig-name descname"><span class="pre">ppf</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.Normal.ppf.pits" title="arch.univariate.Normal.ppf.pits (Python parameter) — Probability-integral-transformed values in the interval (0, 1)."><span class="n"><span class="pre">pits</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.Normal.ppf.parameters" title="arch.univariate.Normal.ppf.parameters (Python parameter) — Distribution parameters. Use None for parameterless distributions."><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/distribution.html#Normal.ppf"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.Normal.ppf" title="Link to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">Normal.</span></span><span class="sig-name descname"><span class="pre">ppf</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.Normal.ppf.pits" title="arch.univariate.Normal.ppf.pits (Python parameter) — Probability-integral-transformed values in the interval (0, 1)."><span class="n"><span class="pre">pits</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.Normal.ppf.parameters" title="arch.univariate.Normal.ppf.parameters (Python parameter) — Distribution parameters. Use None for parameterless distributions."><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/distribution.html#Normal.ppf"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.Normal.ppf" title="Link to this definition">¶</a></dt>
 <dd><p>Inverse cumulative density function (ICDF)</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.Normal.ppf-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.Normal.ppf-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.Normal.ppf.pits"><span class="n sig-name">pits</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.univariate.Normal.ppf.pits" title="Permalink to this definition">¶</a></dt><dd><p>Probability-integral-transformed values in the interval (0, 1).</p>
+<dt id="arch.univariate.Normal.ppf.pits"><span class="n sig-name">pits</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a></span></span><a class="headerlink" href="#arch.univariate.Normal.ppf.pits" title="Permalink to this definition">¶</a></dt><dd><p>Probability-integral-transformed values in the interval (0, 1).</p>
 </dd>
-<dt id="arch.univariate.Normal.ppf.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.Normal.ppf.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Distribution parameters. Use <code class="docutils literal notranslate"><span class="pre">None</span></code> for parameterless
+<dt id="arch.univariate.Normal.ppf.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.Normal.ppf.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Distribution parameters. Use <code class="docutils literal notranslate"><span class="pre">None</span></code> for parameterless
 distributions.</p>
 </dd>
 </dl>
@@ -1250,7 +1250,7 @@ <h1 id="arch-univariate-normal-ppf">arch.univariate.Normal.ppf<a class="headerli
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diff --git a/devel/univariate/generated/generated/arch.univariate.Normal.simulate.html b/devel/univariate/generated/generated/arch.univariate.Normal.simulate.html
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 <h1 id="arch-univariate-normal-simulate">arch.univariate.Normal.simulate<a class="headerlink" href="#arch-univariate-normal-simulate" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.Normal.simulate">
-<span class="sig-prename descclassname"><span class="pre">Normal.</span></span><span class="sig-name descname"><span class="pre">simulate</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.Normal.simulate.parameters" title="arch.univariate.Normal.simulate.parameters (Python parameter) — Distribution parameters"><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.12)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/distribution.html#Normal.simulate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.Normal.simulate" title="Link to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">Normal.</span></span><span class="sig-name descname"><span class="pre">simulate</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.Normal.simulate.parameters" title="arch.univariate.Normal.simulate.parameters (Python parameter) — Distribution parameters"><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a></span></span></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.12)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/distribution.html#Normal.simulate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.Normal.simulate" title="Link to this definition">¶</a></dt>
 <dd><p>Simulates i.i.d. draws from the distribution</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.Normal.simulate-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.Normal.simulate-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.Normal.simulate.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.univariate.Normal.simulate.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Distribution parameters</p>
+<dt id="arch.univariate.Normal.simulate.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a></span></span><a class="headerlink" href="#arch.univariate.Normal.simulate.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Distribution parameters</p>
 </dd>
 </dl>
 </dd>
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diff --git a/devel/univariate/generated/generated/arch.univariate.Normal.starting_values.html b/devel/univariate/generated/generated/arch.univariate.Normal.starting_values.html
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+++ b/devel/univariate/generated/generated/arch.univariate.Normal.starting_values.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.Normal.starting_values - arch v6.2.1 (+21)</title>
+        <title>arch.univariate.Normal.starting_values - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
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@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.RiskMetrics2006.backcast - arch v6.2.1 (+21)</title>
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-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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diff --git a/devel/univariate/generated/generated/arch.univariate.RiskMetrics2006.backcast_transform.html b/devel/univariate/generated/generated/arch.univariate.RiskMetrics2006.backcast_transform.html
index f5a43ed1fe..24728c06bd 100644
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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index c00510e626..d55674a0ea 100644
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-        <title>arch.univariate.RiskMetrics2006.bounds - arch v6.2.1 (+21)</title>
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+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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+    arch v6.2.1 (+23)
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-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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index 4084c136fa..8cdcfdc47c 100644
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+++ b/devel/univariate/generated/generated/arch.univariate.RiskMetrics2006.compute_variance.html
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-        <title>arch.univariate.RiskMetrics2006.compute_variance - arch v6.2.1 (+21)</title>
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@@ -92,7 +92,7 @@
 
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-    arch v6.2.1 (+21)
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@@ -1383,7 +1383,7 @@ <h1 id="arch-univariate-riskmetrics2006-constraints">arch.univariate.RiskMetrics
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 <h1 id="arch-univariate-riskmetrics2006-forecast">arch.univariate.RiskMetrics2006.forecast<a class="headerlink" href="#arch-univariate-riskmetrics2006-forecast" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.RiskMetrics2006.forecast">
-<span class="sig-prename descclassname"><span class="pre">RiskMetrics2006.</span></span><span class="sig-name descname"><span class="pre">forecast</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.RiskMetrics2006.forecast.parameters" title="arch.univariate.RiskMetrics2006.forecast.parameters (Python parameter) — Parameters required to forecast the volatility model"><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.RiskMetrics2006.forecast.resids" title="arch.univariate.RiskMetrics2006.forecast.resids (Python parameter) — Residuals to use in the recursion"><span class="n"><span class="pre">resids</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.RiskMetrics2006.forecast.backcast" title="arch.univariate.RiskMetrics2006.forecast.backcast (Python parameter) — Value to use when initializing the recursion"><span class="n"><span class="pre">backcast</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.RiskMetrics2006.forecast.var_bounds" title="arch.univariate.RiskMetrics2006.forecast.var_bounds (Python parameter) — Array containing columns of lower and upper bounds"><span class="n"><span class="pre">var_bounds</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.RiskMetrics2006.forecast.start" title="arch.univariate.RiskMetrics2006.forecast.start (Python parameter) — Index of the first observation to use as the starting point for the forecast.  Default is len(resids)."><span class="n"><span class="pre">start</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.RiskMetrics2006.forecast.horizon" title="arch.univariate.RiskMetrics2006.forecast.horizon (Python parameter) — Forecast horizon.  Must be 1 or larger.  Forecasts are produced for horizons in [1, horizon]."><span class="n"><span class="pre">horizon</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.RiskMetrics2006.forecast.method" title="arch.univariate.RiskMetrics2006.forecast.method (Python parameter) — Method to use when producing the forecast. The default is analytic."><span class="n"><span class="pre">method</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'analytic'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'simulation'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'bootstrap'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;analytic&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.RiskMetrics2006.forecast.simulations" title="arch.univariate.RiskMetrics2006.forecast.simulations (Python parameter) — Number of simulations to run when computing the forecast using either simulation or bootstrap."><span class="n"><span class="pre">simulations</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.RiskMetrics2006.forecast.rng" title="arch.univariate.RiskMetrics2006.forecast.rng (Python parameter) — Callable random number generator required if method is 'simulation'. Must take a single shape input and return random samples numbers with that shape."><span class="n"><span class="pre">rng</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.12)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.RiskMetrics2006.forecast.random_state" title="arch.univariate.RiskMetrics2006.forecast.random_state (Python parameter) — NumPy RandomState instance to use when method is 'bootstrap'"><span class="n"><span class="pre">random_state</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.26)"><span class="pre">RandomState</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><span class="pre">VarianceForecast</span></span></span></span><a class="headerlink" href="#arch.univariate.RiskMetrics2006.forecast" title="Link to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">RiskMetrics2006.</span></span><span class="sig-name descname"><span class="pre">forecast</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.RiskMetrics2006.forecast.parameters" title="arch.univariate.RiskMetrics2006.forecast.parameters (Python parameter) — Parameters required to forecast the volatility model"><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.RiskMetrics2006.forecast.resids" title="arch.univariate.RiskMetrics2006.forecast.resids (Python parameter) — Residuals to use in the recursion"><span class="n"><span class="pre">resids</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.RiskMetrics2006.forecast.backcast" title="arch.univariate.RiskMetrics2006.forecast.backcast (Python parameter) — Value to use when initializing the recursion"><span class="n"><span class="pre">backcast</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.RiskMetrics2006.forecast.var_bounds" title="arch.univariate.RiskMetrics2006.forecast.var_bounds (Python parameter) — Array containing columns of lower and upper bounds"><span class="n"><span class="pre">var_bounds</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.RiskMetrics2006.forecast.start" title="arch.univariate.RiskMetrics2006.forecast.start (Python parameter) — Index of the first observation to use as the starting point for the forecast.  Default is len(resids)."><span class="n"><span class="pre">start</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.RiskMetrics2006.forecast.horizon" title="arch.univariate.RiskMetrics2006.forecast.horizon (Python parameter) — Forecast horizon.  Must be 1 or larger.  Forecasts are produced for horizons in [1, horizon]."><span class="n"><span class="pre">horizon</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.RiskMetrics2006.forecast.method" title="arch.univariate.RiskMetrics2006.forecast.method (Python parameter) — Method to use when producing the forecast. The default is analytic."><span class="n"><span class="pre">method</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'analytic'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'simulation'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'bootstrap'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;analytic&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.RiskMetrics2006.forecast.simulations" title="arch.univariate.RiskMetrics2006.forecast.simulations (Python parameter) — Number of simulations to run when computing the forecast using either simulation or bootstrap."><span class="n"><span class="pre">simulations</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.RiskMetrics2006.forecast.rng" title="arch.univariate.RiskMetrics2006.forecast.rng (Python parameter) — Callable random number generator required if method is 'simulation'. Must take a single shape input and return random samples numbers with that shape."><span class="n"><span class="pre">rng</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.12)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.RiskMetrics2006.forecast.random_state" title="arch.univariate.RiskMetrics2006.forecast.random_state (Python parameter) — NumPy RandomState instance to use when method is 'bootstrap'"><span class="n"><span class="pre">random_state</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.26)"><span class="pre">RandomState</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><span class="pre">VarianceForecast</span></span></span></span><a class="headerlink" href="#arch.univariate.RiskMetrics2006.forecast" title="Link to this definition">¶</a></dt>
 <dd><p>Forecast volatility from the model</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.RiskMetrics2006.forecast-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.RiskMetrics2006.forecast-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.RiskMetrics2006.forecast.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.univariate.RiskMetrics2006.forecast.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Parameters required to forecast the volatility model</p>
+<dt id="arch.univariate.RiskMetrics2006.forecast.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a></span></span><a class="headerlink" href="#arch.univariate.RiskMetrics2006.forecast.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Parameters required to forecast the volatility model</p>
 </dd>
 <dt id="arch.univariate.RiskMetrics2006.forecast.resids"><span class="n sig-name">resids</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.RiskMetrics2006.forecast.resids" title="Permalink to this definition">¶</a></dt><dd><p>Residuals to use in the recursion</p>
 </dd>
@@ -1617,7 +1617,7 @@ <h1 id="arch-univariate-riskmetrics2006-forecast">arch.univariate.RiskMetrics200
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diff --git a/devel/univariate/generated/generated/arch.univariate.RiskMetrics2006.name.html b/devel/univariate/generated/generated/arch.univariate.RiskMetrics2006.name.html
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@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.RiskMetrics2006.name - arch v6.2.1 (+21)</title>
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+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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-            arch v6.2.1 (+21)
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+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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diff --git a/devel/univariate/generated/generated/arch.univariate.RiskMetrics2006.num_params.html b/devel/univariate/generated/generated/arch.univariate.RiskMetrics2006.num_params.html
index 7b1f25dc8a..9fc484fa60 100644
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+++ b/devel/univariate/generated/generated/arch.univariate.RiskMetrics2006.num_params.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.RiskMetrics2006.num_params - arch v6.2.1 (+21)</title>
+        <title>arch.univariate.RiskMetrics2006.num_params - arch v6.2.1 (+23)</title>
       
     
 
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+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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-            arch v6.2.1 (+21)
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-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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-    arch v6.2.1 (+21)
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diff --git a/devel/univariate/generated/generated/arch.univariate.RiskMetrics2006.parameter_names.html b/devel/univariate/generated/generated/arch.univariate.RiskMetrics2006.parameter_names.html
index a334f3a899..466c7def53 100644
--- a/devel/univariate/generated/generated/arch.univariate.RiskMetrics2006.parameter_names.html
+++ b/devel/univariate/generated/generated/arch.univariate.RiskMetrics2006.parameter_names.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.RiskMetrics2006.parameter_names - arch v6.2.1 (+21)</title>
+        <title>arch.univariate.RiskMetrics2006.parameter_names - arch v6.2.1 (+23)</title>
       
     
 
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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-            arch v6.2.1 (+21)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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-    arch v6.2.1 (+21)
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@@ -1392,7 +1392,7 @@ <h1 id="arch-univariate-riskmetrics2006-parameter-names">arch.univariate.RiskMet
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@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.RiskMetrics2006.simulate - arch v6.2.1 (+21)</title>
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 <h1 id="arch-univariate-riskmetrics2006-simulate">arch.univariate.RiskMetrics2006.simulate<a class="headerlink" href="#arch-univariate-riskmetrics2006-simulate" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.RiskMetrics2006.simulate">
-<span class="sig-prename descclassname"><span class="pre">RiskMetrics2006.</span></span><span class="sig-name descname"><span class="pre">simulate</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.RiskMetrics2006.simulate.parameters" title="arch.univariate.RiskMetrics2006.simulate.parameters (Python parameter) — Parameters required to simulate the volatility model"><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.RiskMetrics2006.simulate.nobs" title="arch.univariate.RiskMetrics2006.simulate.nobs (Python parameter) — Number of data points to simulate"><span class="n"><span class="pre">nobs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.RiskMetrics2006.simulate.rng" title="arch.univariate.RiskMetrics2006.simulate.rng (Python parameter) — Callable function that takes a single integer input and returns a vector of random numbers"><span class="n"><span class="pre">rng</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.12)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.RiskMetrics2006.simulate.burn" title="arch.univariate.RiskMetrics2006.simulate.burn (Python parameter) — Number of additional observations to generate when initializing the simulation"><span class="n"><span class="pre">burn</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">500</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.RiskMetrics2006.simulate.initial_value" title="arch.univariate.RiskMetrics2006.simulate.initial_value (Python parameter) — Scalar or array of initial values to use when initializing the simulation"><span class="n"><span class="pre">initial_value</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#RiskMetrics2006.simulate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.RiskMetrics2006.simulate" title="Link to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">RiskMetrics2006.</span></span><span class="sig-name descname"><span class="pre">simulate</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.RiskMetrics2006.simulate.parameters" title="arch.univariate.RiskMetrics2006.simulate.parameters (Python parameter) — Parameters required to simulate the volatility model"><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.RiskMetrics2006.simulate.nobs" title="arch.univariate.RiskMetrics2006.simulate.nobs (Python parameter) — Number of data points to simulate"><span class="n"><span class="pre">nobs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.RiskMetrics2006.simulate.rng" title="arch.univariate.RiskMetrics2006.simulate.rng (Python parameter) — Callable function that takes a single integer input and returns a vector of random numbers"><span class="n"><span class="pre">rng</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.12)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.RiskMetrics2006.simulate.burn" title="arch.univariate.RiskMetrics2006.simulate.burn (Python parameter) — Number of additional observations to generate when initializing the simulation"><span class="n"><span class="pre">burn</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">500</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.RiskMetrics2006.simulate.initial_value" title="arch.univariate.RiskMetrics2006.simulate.initial_value (Python parameter) — Scalar or array of initial values to use when initializing the simulation"><span class="n"><span class="pre">initial_value</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#RiskMetrics2006.simulate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.RiskMetrics2006.simulate" title="Link to this definition">¶</a></dt>
 <dd><p>Simulate data from the model</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.RiskMetrics2006.simulate-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.RiskMetrics2006.simulate-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.RiskMetrics2006.simulate.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.univariate.RiskMetrics2006.simulate.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Parameters required to simulate the volatility model</p>
+<dt id="arch.univariate.RiskMetrics2006.simulate.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a></span></span><a class="headerlink" href="#arch.univariate.RiskMetrics2006.simulate.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Parameters required to simulate the volatility model</p>
 </dd>
 <dt id="arch.univariate.RiskMetrics2006.simulate.nobs"><span class="n sig-name">nobs</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a></span></span><a class="headerlink" href="#arch.univariate.RiskMetrics2006.simulate.nobs" title="Permalink to this definition">¶</a></dt><dd><p>Number of data points to simulate</p>
 </dd>
@@ -1492,7 +1492,7 @@ <h1 id="arch-univariate-riskmetrics2006-simulate">arch.univariate.RiskMetrics200
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@@ -92,7 +92,7 @@
 
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@@ -1115,14 +1115,14 @@
 <h1 id="arch-univariate-skewstudent-cdf">arch.univariate.SkewStudent.cdf<a class="headerlink" href="#arch-univariate-skewstudent-cdf" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.SkewStudent.cdf">
-<span class="sig-prename descclassname"><span class="pre">SkewStudent.</span></span><span class="sig-name descname"><span class="pre">cdf</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.SkewStudent.cdf.resids" title="arch.univariate.SkewStudent.cdf.resids (Python parameter) — Values at which to evaluate the cdf"><span class="n"><span class="pre">resids</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.SkewStudent.cdf.parameters" title="arch.univariate.SkewStudent.cdf.parameters (Python parameter) — Distribution parameters. Use None for parameterless distributions."><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/distribution.html#SkewStudent.cdf"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.SkewStudent.cdf" title="Link to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">SkewStudent.</span></span><span class="sig-name descname"><span class="pre">cdf</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.SkewStudent.cdf.resids" title="arch.univariate.SkewStudent.cdf.resids (Python parameter) — Values at which to evaluate the cdf"><span class="n"><span class="pre">resids</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.SkewStudent.cdf.parameters" title="arch.univariate.SkewStudent.cdf.parameters (Python parameter) — Distribution parameters. Use None for parameterless distributions."><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/distribution.html#SkewStudent.cdf"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.SkewStudent.cdf" title="Link to this definition">¶</a></dt>
 <dd><p>Cumulative distribution function</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.SkewStudent.cdf-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.SkewStudent.cdf-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.SkewStudent.cdf.resids"><span class="n sig-name">resids</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.univariate.SkewStudent.cdf.resids" title="Permalink to this definition">¶</a></dt><dd><p>Values at which to evaluate the cdf</p>
+<dt id="arch.univariate.SkewStudent.cdf.resids"><span class="n sig-name">resids</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a></span></span><a class="headerlink" href="#arch.univariate.SkewStudent.cdf.resids" title="Permalink to this definition">¶</a></dt><dd><p>Values at which to evaluate the cdf</p>
 </dd>
-<dt id="arch.univariate.SkewStudent.cdf.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.SkewStudent.cdf.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Distribution parameters. Use <code class="docutils literal notranslate"><span class="pre">None</span></code> for parameterless
+<dt id="arch.univariate.SkewStudent.cdf.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.SkewStudent.cdf.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Distribution parameters. Use <code class="docutils literal notranslate"><span class="pre">None</span></code> for parameterless
 distributions.</p>
 </dd>
 </dl>
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+++ b/devel/univariate/generated/generated/arch.univariate.SkewStudent.constraints.html
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+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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+    arch v6.2.1 (+23)
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+++ b/devel/univariate/generated/generated/arch.univariate.SkewStudent.generator.html
@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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@@ -1139,7 +1139,7 @@ <h1 id="arch-univariate-skewstudent-generator">arch.univariate.SkewStudent.gener
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 <h1 id="arch-univariate-skewstudent-loglikelihood">arch.univariate.SkewStudent.loglikelihood<a class="headerlink" href="#arch-univariate-skewstudent-loglikelihood" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.SkewStudent.loglikelihood">
-<span class="sig-prename descclassname"><span class="pre">SkewStudent.</span></span><span class="sig-name descname"><span class="pre">loglikelihood</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.SkewStudent.loglikelihood.parameters" title="arch.univariate.SkewStudent.loglikelihood.parameters (Python parameter) — Shape parameter of the skew-t distribution"><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.SkewStudent.loglikelihood.resids" title="arch.univariate.SkewStudent.loglikelihood.resids (Python parameter) — The residuals to use in the log-likelihood calculation"><span class="n"><span class="pre">resids</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.SkewStudent.loglikelihood.sigma2" title="arch.univariate.SkewStudent.loglikelihood.sigma2 (Python parameter) — Conditional variances of resids"><span class="n"><span class="pre">sigma2</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.SkewStudent.loglikelihood.individual" title="arch.univariate.SkewStudent.loglikelihood.individual (Python parameter) — Flag indicating whether to return the vector of individual log likelihoods (True) or the sum (False)"><span class="n"><span class="pre">individual</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/distribution.html#SkewStudent.loglikelihood"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.SkewStudent.loglikelihood" title="Link to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">SkewStudent.</span></span><span class="sig-name descname"><span class="pre">loglikelihood</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.SkewStudent.loglikelihood.parameters" title="arch.univariate.SkewStudent.loglikelihood.parameters (Python parameter) — Shape parameter of the skew-t distribution"><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.SkewStudent.loglikelihood.resids" title="arch.univariate.SkewStudent.loglikelihood.resids (Python parameter) — The residuals to use in the log-likelihood calculation"><span class="n"><span class="pre">resids</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.SkewStudent.loglikelihood.sigma2" title="arch.univariate.SkewStudent.loglikelihood.sigma2 (Python parameter) — Conditional variances of resids"><span class="n"><span class="pre">sigma2</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.SkewStudent.loglikelihood.individual" title="arch.univariate.SkewStudent.loglikelihood.individual (Python parameter) — Flag indicating whether to return the vector of individual log likelihoods (True) or the sum (False)"><span class="n"><span class="pre">individual</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/distribution.html#SkewStudent.loglikelihood"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.SkewStudent.loglikelihood" title="Link to this definition">¶</a></dt>
 <dd><p>Computes the log-likelihood of assuming residuals are have a
 standardized (to have unit variance) Skew Student’s t distribution,
 conditional on the variance.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.SkewStudent.loglikelihood-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.SkewStudent.loglikelihood-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.SkewStudent.loglikelihood.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.univariate.SkewStudent.loglikelihood.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Shape parameter of the skew-t distribution</p>
+<dt id="arch.univariate.SkewStudent.loglikelihood.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a></span></span><a class="headerlink" href="#arch.univariate.SkewStudent.loglikelihood.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Shape parameter of the skew-t distribution</p>
 </dd>
-<dt id="arch.univariate.SkewStudent.loglikelihood.resids"><span class="n sig-name">resids</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.univariate.SkewStudent.loglikelihood.resids" title="Permalink to this definition">¶</a></dt><dd><p>The residuals to use in the log-likelihood calculation</p>
+<dt id="arch.univariate.SkewStudent.loglikelihood.resids"><span class="n sig-name">resids</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a></span></span><a class="headerlink" href="#arch.univariate.SkewStudent.loglikelihood.resids" title="Permalink to this definition">¶</a></dt><dd><p>The residuals to use in the log-likelihood calculation</p>
 </dd>
-<dt id="arch.univariate.SkewStudent.loglikelihood.sigma2"><span class="n sig-name">sigma2</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.univariate.SkewStudent.loglikelihood.sigma2" title="Permalink to this definition">¶</a></dt><dd><p>Conditional variances of resids</p>
+<dt id="arch.univariate.SkewStudent.loglikelihood.sigma2"><span class="n sig-name">sigma2</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a></span></span><a class="headerlink" href="#arch.univariate.SkewStudent.loglikelihood.sigma2" title="Permalink to this definition">¶</a></dt><dd><p>Conditional variances of resids</p>
 </dd>
 <dt id="arch.univariate.SkewStudent.loglikelihood.individual"><span class="n sig-name">individual</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)">bool</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code><a class="headerlink" href="#arch.univariate.SkewStudent.loglikelihood.individual" title="Permalink to this definition">¶</a></dt><dd><p>Flag indicating whether to return the vector of individual log
 likelihoods (True) or the sum (False)</p>
@@ -1300,7 +1300,7 @@ <h1 id="arch-univariate-skewstudent-loglikelihood">arch.univariate.SkewStudent.l
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diff --git a/devel/univariate/generated/generated/arch.univariate.SkewStudent.moment.html b/devel/univariate/generated/generated/arch.univariate.SkewStudent.moment.html
index f541b1ca86..f09bf6bfee 100644
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+++ b/devel/univariate/generated/generated/arch.univariate.SkewStudent.moment.html
@@ -15,7 +15,7 @@
     
   
       
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 <h1 id="arch-univariate-skewstudent-moment">arch.univariate.SkewStudent.moment<a class="headerlink" href="#arch-univariate-skewstudent-moment" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.SkewStudent.moment">
-<span class="sig-prename descclassname"><span class="pre">SkewStudent.</span></span><span class="sig-name descname"><span class="pre">moment</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.SkewStudent.moment.n" title="arch.univariate.SkewStudent.moment.n (Python parameter) — Order of moment"><span class="n"><span class="pre">n</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.SkewStudent.moment.parameters" title="arch.univariate.SkewStudent.moment.parameters (Python parameter) — Distribution parameters. Use None for parameterless distributions."><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/distribution.html#SkewStudent.moment"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.SkewStudent.moment" title="Link to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">SkewStudent.</span></span><span class="sig-name descname"><span class="pre">moment</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.SkewStudent.moment.n" title="arch.univariate.SkewStudent.moment.n (Python parameter) — Order of moment"><span class="n"><span class="pre">n</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.SkewStudent.moment.parameters" title="arch.univariate.SkewStudent.moment.parameters (Python parameter) — Distribution parameters. Use None for parameterless distributions."><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/distribution.html#SkewStudent.moment"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.SkewStudent.moment" title="Link to this definition">¶</a></dt>
 <dd><p>Moment of order n</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.SkewStudent.moment-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.SkewStudent.moment-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
 <dt id="arch.univariate.SkewStudent.moment.n"><span class="n sig-name">n</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a></span></span><a class="headerlink" href="#arch.univariate.SkewStudent.moment.n" title="Permalink to this definition">¶</a></dt><dd><p>Order of moment</p>
 </dd>
-<dt id="arch.univariate.SkewStudent.moment.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.SkewStudent.moment.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Distribution parameters. Use None for parameterless distributions.</p>
+<dt id="arch.univariate.SkewStudent.moment.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.SkewStudent.moment.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Distribution parameters. Use None for parameterless distributions.</p>
 </dd>
 </dl>
 </dd>
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-            arch v6.2.1 (+21)
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@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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diff --git a/devel/univariate/generated/generated/arch.univariate.SkewStudent.partial_moment.html b/devel/univariate/generated/generated/arch.univariate.SkewStudent.partial_moment.html
index 9e8b738f5e..483afd81cb 100644
--- a/devel/univariate/generated/generated/arch.univariate.SkewStudent.partial_moment.html
+++ b/devel/univariate/generated/generated/arch.univariate.SkewStudent.partial_moment.html
@@ -15,7 +15,7 @@
     
   
       
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+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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-            arch v6.2.1 (+21)
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@@ -1129,7 +1129,7 @@
 <h1 id="arch-univariate-skewstudent-partial-moment">arch.univariate.SkewStudent.partial_moment<a class="headerlink" href="#arch-univariate-skewstudent-partial-moment" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.SkewStudent.partial_moment">
-<span class="sig-prename descclassname"><span class="pre">SkewStudent.</span></span><span class="sig-name descname"><span class="pre">partial_moment</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.SkewStudent.partial_moment.n" title="arch.univariate.SkewStudent.partial_moment.n (Python parameter) — Order of partial moment"><span class="n"><span class="pre">n</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.SkewStudent.partial_moment.z" title="arch.univariate.SkewStudent.partial_moment.z (Python parameter) — Upper bound for partial moment integral"><span class="n"><span class="pre">z</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mf">0.0</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.SkewStudent.partial_moment.parameters" title="arch.univariate.SkewStudent.partial_moment.parameters (Python parameter) — Distribution parameters.  Use None for parameterless distributions."><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/distribution.html#SkewStudent.partial_moment"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.SkewStudent.partial_moment" title="Link to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">SkewStudent.</span></span><span class="sig-name descname"><span class="pre">partial_moment</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.SkewStudent.partial_moment.n" title="arch.univariate.SkewStudent.partial_moment.n (Python parameter) — Order of partial moment"><span class="n"><span class="pre">n</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.SkewStudent.partial_moment.z" title="arch.univariate.SkewStudent.partial_moment.z (Python parameter) — Upper bound for partial moment integral"><span class="n"><span class="pre">z</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mf">0.0</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.SkewStudent.partial_moment.parameters" title="arch.univariate.SkewStudent.partial_moment.parameters (Python parameter) — Distribution parameters.  Use None for parameterless distributions."><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/distribution.html#SkewStudent.partial_moment"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.SkewStudent.partial_moment" title="Link to this definition">¶</a></dt>
 <dd><p>Order n lower partial moment from -inf to z</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.SkewStudent.partial_moment-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.SkewStudent.partial_moment-parameters" title="Permalink to this headline">¶</a></dt>
@@ -1138,7 +1138,7 @@ <h1 id="arch-univariate-skewstudent-partial-moment">arch.univariate.SkewStudent.
 </dd>
 <dt id="arch.univariate.SkewStudent.partial_moment.z"><span class="n sig-name">z</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mf">0.0</span></code><a class="headerlink" href="#arch.univariate.SkewStudent.partial_moment.z" title="Permalink to this definition">¶</a></dt><dd><p>Upper bound for partial moment integral</p>
 </dd>
-<dt id="arch.univariate.SkewStudent.partial_moment.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.SkewStudent.partial_moment.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Distribution parameters.  Use None for parameterless distributions.</p>
+<dt id="arch.univariate.SkewStudent.partial_moment.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.SkewStudent.partial_moment.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Distribution parameters.  Use None for parameterless distributions.</p>
 </dd>
 </dl>
 </dd>
@@ -1278,7 +1278,7 @@ <h1 id="arch-univariate-skewstudent-partial-moment">arch.univariate.SkewStudent.
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diff --git a/devel/univariate/generated/generated/arch.univariate.SkewStudent.ppf.html b/devel/univariate/generated/generated/arch.univariate.SkewStudent.ppf.html
index c24d6412c2..f559940e13 100644
--- a/devel/univariate/generated/generated/arch.univariate.SkewStudent.ppf.html
+++ b/devel/univariate/generated/generated/arch.univariate.SkewStudent.ppf.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.SkewStudent.ppf - arch v6.2.1 (+21)</title>
+        <title>arch.univariate.SkewStudent.ppf - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
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@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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-    arch v6.2.1 (+21)
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@@ -1115,14 +1115,14 @@
 <h1 id="arch-univariate-skewstudent-ppf">arch.univariate.SkewStudent.ppf<a class="headerlink" href="#arch-univariate-skewstudent-ppf" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.SkewStudent.ppf">
-<span class="sig-prename descclassname"><span class="pre">SkewStudent.</span></span><span class="sig-name descname"><span class="pre">ppf</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.SkewStudent.ppf.pits" title="arch.univariate.SkewStudent.ppf.pits (Python parameter) — Probability-integral-transformed values in the interval (0, 1)."><span class="n"><span class="pre">pits</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.SkewStudent.ppf.parameters" title="arch.univariate.SkewStudent.ppf.parameters (Python parameter) — Distribution parameters. Use None for parameterless distributions."><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/distribution.html#SkewStudent.ppf"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.SkewStudent.ppf" title="Link to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">SkewStudent.</span></span><span class="sig-name descname"><span class="pre">ppf</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.SkewStudent.ppf.pits" title="arch.univariate.SkewStudent.ppf.pits (Python parameter) — Probability-integral-transformed values in the interval (0, 1)."><span class="n"><span class="pre">pits</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.SkewStudent.ppf.parameters" title="arch.univariate.SkewStudent.ppf.parameters (Python parameter) — Distribution parameters. Use None for parameterless distributions."><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/distribution.html#SkewStudent.ppf"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.SkewStudent.ppf" title="Link to this definition">¶</a></dt>
 <dd><p>Inverse cumulative density function (ICDF)</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.SkewStudent.ppf-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.SkewStudent.ppf-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.SkewStudent.ppf.pits"><span class="n sig-name">pits</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.univariate.SkewStudent.ppf.pits" title="Permalink to this definition">¶</a></dt><dd><p>Probability-integral-transformed values in the interval (0, 1).</p>
+<dt id="arch.univariate.SkewStudent.ppf.pits"><span class="n sig-name">pits</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a></span></span><a class="headerlink" href="#arch.univariate.SkewStudent.ppf.pits" title="Permalink to this definition">¶</a></dt><dd><p>Probability-integral-transformed values in the interval (0, 1).</p>
 </dd>
-<dt id="arch.univariate.SkewStudent.ppf.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.SkewStudent.ppf.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Distribution parameters. Use <code class="docutils literal notranslate"><span class="pre">None</span></code> for parameterless
+<dt id="arch.univariate.SkewStudent.ppf.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.SkewStudent.ppf.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Distribution parameters. Use <code class="docutils literal notranslate"><span class="pre">None</span></code> for parameterless
 distributions.</p>
 </dd>
 </dl>
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-    arch v6.2.1 (+21)
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@@ -15,7 +15,7 @@
     
   
       
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+        <title>arch.univariate.SkewStudent.simulate - arch v6.2.1 (+23)</title>
       
     
 
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@@ -92,7 +92,7 @@
 
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@@ -102,7 +102,7 @@
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-            arch v6.2.1 (+21)
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@@ -189,10 +189,10 @@
 
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@@ -1101,12 +1101,12 @@
 <h1 id="arch-univariate-skewstudent-simulate">arch.univariate.SkewStudent.simulate<a class="headerlink" href="#arch-univariate-skewstudent-simulate" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.SkewStudent.simulate">
-<span class="sig-prename descclassname"><span class="pre">SkewStudent.</span></span><span class="sig-name descname"><span class="pre">simulate</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.SkewStudent.simulate.parameters" title="arch.univariate.SkewStudent.simulate.parameters (Python parameter) — Distribution parameters"><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.12)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/distribution.html#SkewStudent.simulate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.SkewStudent.simulate" title="Link to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">SkewStudent.</span></span><span class="sig-name descname"><span class="pre">simulate</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.SkewStudent.simulate.parameters" title="arch.univariate.SkewStudent.simulate.parameters (Python parameter) — Distribution parameters"><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a></span></span></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.12)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/distribution.html#SkewStudent.simulate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.SkewStudent.simulate" title="Link to this definition">¶</a></dt>
 <dd><p>Simulates i.i.d. draws from the distribution</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.SkewStudent.simulate-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.SkewStudent.simulate-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.SkewStudent.simulate.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.univariate.SkewStudent.simulate.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Distribution parameters</p>
+<dt id="arch.univariate.SkewStudent.simulate.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a></span></span><a class="headerlink" href="#arch.univariate.SkewStudent.simulate.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Distribution parameters</p>
 </dd>
 </dl>
 </dd>
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diff --git a/devel/univariate/generated/generated/arch.univariate.SkewStudent.starting_values.html b/devel/univariate/generated/generated/arch.univariate.SkewStudent.starting_values.html
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@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.SkewStudent.starting_values - arch v6.2.1 (+21)</title>
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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diff --git a/devel/univariate/generated/generated/arch.univariate.StudentsT.bounds.html b/devel/univariate/generated/generated/arch.univariate.StudentsT.bounds.html
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@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.StudentsT.bounds - arch v6.2.1 (+21)</title>
+        <title>arch.univariate.StudentsT.bounds - arch v6.2.1 (+23)</title>
       
     
 
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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@@ -1233,7 +1233,7 @@ <h1 id="arch-univariate-studentst-bounds">arch.univariate.StudentsT.bounds<a cla
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diff --git a/devel/univariate/generated/generated/arch.univariate.StudentsT.cdf.html b/devel/univariate/generated/generated/arch.univariate.StudentsT.cdf.html
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@@ -1115,14 +1115,14 @@
 <h1 id="arch-univariate-studentst-cdf">arch.univariate.StudentsT.cdf<a class="headerlink" href="#arch-univariate-studentst-cdf" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.StudentsT.cdf">
-<span class="sig-prename descclassname"><span class="pre">StudentsT.</span></span><span class="sig-name descname"><span class="pre">cdf</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.StudentsT.cdf.resids" title="arch.univariate.StudentsT.cdf.resids (Python parameter) — Values at which to evaluate the cdf"><span class="n"><span class="pre">resids</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.StudentsT.cdf.parameters" title="arch.univariate.StudentsT.cdf.parameters (Python parameter) — Distribution parameters. Use None for parameterless distributions."><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/distribution.html#StudentsT.cdf"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.StudentsT.cdf" title="Link to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">StudentsT.</span></span><span class="sig-name descname"><span class="pre">cdf</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.StudentsT.cdf.resids" title="arch.univariate.StudentsT.cdf.resids (Python parameter) — Values at which to evaluate the cdf"><span class="n"><span class="pre">resids</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.StudentsT.cdf.parameters" title="arch.univariate.StudentsT.cdf.parameters (Python parameter) — Distribution parameters. Use None for parameterless distributions."><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/distribution.html#StudentsT.cdf"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.StudentsT.cdf" title="Link to this definition">¶</a></dt>
 <dd><p>Cumulative distribution function</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.StudentsT.cdf-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.StudentsT.cdf-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.StudentsT.cdf.resids"><span class="n sig-name">resids</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.univariate.StudentsT.cdf.resids" title="Permalink to this definition">¶</a></dt><dd><p>Values at which to evaluate the cdf</p>
+<dt id="arch.univariate.StudentsT.cdf.resids"><span class="n sig-name">resids</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a></span></span><a class="headerlink" href="#arch.univariate.StudentsT.cdf.resids" title="Permalink to this definition">¶</a></dt><dd><p>Values at which to evaluate the cdf</p>
 </dd>
-<dt id="arch.univariate.StudentsT.cdf.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.StudentsT.cdf.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Distribution parameters. Use <code class="docutils literal notranslate"><span class="pre">None</span></code> for parameterless
+<dt id="arch.univariate.StudentsT.cdf.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.StudentsT.cdf.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Distribution parameters. Use <code class="docutils literal notranslate"><span class="pre">None</span></code> for parameterless
 distributions.</p>
 </dd>
 </dl>
@@ -1250,7 +1250,7 @@ <h1 id="arch-univariate-studentst-cdf">arch.univariate.StudentsT.cdf<a class="he
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diff --git a/devel/univariate/generated/generated/arch.univariate.StudentsT.constraints.html b/devel/univariate/generated/generated/arch.univariate.StudentsT.constraints.html
index 3c0068e3e3..db941ce6db 100644
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+++ b/devel/univariate/generated/generated/arch.univariate.StudentsT.constraints.html
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@@ -1176,7 +1176,7 @@ <h1 id="arch-univariate-studentst-constraints">arch.univariate.StudentsT.constra
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diff --git a/devel/univariate/generated/generated/arch.univariate.StudentsT.generator.html b/devel/univariate/generated/generated/arch.univariate.StudentsT.generator.html
index 925860335f..3793a7b8a9 100644
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+++ b/devel/univariate/generated/generated/arch.univariate.StudentsT.generator.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.StudentsT.generator - arch v6.2.1 (+21)</title>
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@@ -47,7 +47,7 @@
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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@@ -1139,7 +1139,7 @@ <h1 id="arch-univariate-studentst-generator">arch.univariate.StudentsT.generator
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diff --git a/devel/univariate/generated/generated/arch.univariate.StudentsT.loglikelihood.html b/devel/univariate/generated/generated/arch.univariate.StudentsT.loglikelihood.html
index 4b686e431a..ac217441a2 100644
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@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.StudentsT.loglikelihood - arch v6.2.1 (+21)</title>
+        <title>arch.univariate.StudentsT.loglikelihood - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
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@@ -92,7 +92,7 @@
 
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@@ -189,10 +189,10 @@
 
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 <h1 id="arch-univariate-studentst-loglikelihood">arch.univariate.StudentsT.loglikelihood<a class="headerlink" href="#arch-univariate-studentst-loglikelihood" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.StudentsT.loglikelihood">
-<span class="sig-prename descclassname"><span class="pre">StudentsT.</span></span><span class="sig-name descname"><span class="pre">loglikelihood</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.StudentsT.loglikelihood.parameters" title="arch.univariate.StudentsT.loglikelihood.parameters (Python parameter) — Shape parameter of the t distribution"><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.StudentsT.loglikelihood.resids" title="arch.univariate.StudentsT.loglikelihood.resids (Python parameter) — The residuals to use in the log-likelihood calculation"><span class="n"><span class="pre">resids</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.StudentsT.loglikelihood.sigma2" title="arch.univariate.StudentsT.loglikelihood.sigma2 (Python parameter) — Conditional variances of resids"><span class="n"><span class="pre">sigma2</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.StudentsT.loglikelihood.individual" title="arch.univariate.StudentsT.loglikelihood.individual (Python parameter) — Flag indicating whether to return the vector of individual log likelihoods (True) or the sum (False)"><span class="n"><span class="pre">individual</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/distribution.html#StudentsT.loglikelihood"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.StudentsT.loglikelihood" title="Link to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">StudentsT.</span></span><span class="sig-name descname"><span class="pre">loglikelihood</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.StudentsT.loglikelihood.parameters" title="arch.univariate.StudentsT.loglikelihood.parameters (Python parameter) — Shape parameter of the t distribution"><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.StudentsT.loglikelihood.resids" title="arch.univariate.StudentsT.loglikelihood.resids (Python parameter) — The residuals to use in the log-likelihood calculation"><span class="n"><span class="pre">resids</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.StudentsT.loglikelihood.sigma2" title="arch.univariate.StudentsT.loglikelihood.sigma2 (Python parameter) — Conditional variances of resids"><span class="n"><span class="pre">sigma2</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.StudentsT.loglikelihood.individual" title="arch.univariate.StudentsT.loglikelihood.individual (Python parameter) — Flag indicating whether to return the vector of individual log likelihoods (True) or the sum (False)"><span class="n"><span class="pre">individual</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/distribution.html#StudentsT.loglikelihood"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.StudentsT.loglikelihood" title="Link to this definition">¶</a></dt>
 <dd><p>Computes the log-likelihood of assuming residuals are have a
 standardized (to have unit variance) Student’s t distribution,
 conditional on the variance.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.StudentsT.loglikelihood-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.StudentsT.loglikelihood-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.StudentsT.loglikelihood.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.univariate.StudentsT.loglikelihood.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Shape parameter of the t distribution</p>
+<dt id="arch.univariate.StudentsT.loglikelihood.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a></span></span><a class="headerlink" href="#arch.univariate.StudentsT.loglikelihood.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Shape parameter of the t distribution</p>
 </dd>
-<dt id="arch.univariate.StudentsT.loglikelihood.resids"><span class="n sig-name">resids</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.univariate.StudentsT.loglikelihood.resids" title="Permalink to this definition">¶</a></dt><dd><p>The residuals to use in the log-likelihood calculation</p>
+<dt id="arch.univariate.StudentsT.loglikelihood.resids"><span class="n sig-name">resids</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a></span></span><a class="headerlink" href="#arch.univariate.StudentsT.loglikelihood.resids" title="Permalink to this definition">¶</a></dt><dd><p>The residuals to use in the log-likelihood calculation</p>
 </dd>
-<dt id="arch.univariate.StudentsT.loglikelihood.sigma2"><span class="n sig-name">sigma2</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.univariate.StudentsT.loglikelihood.sigma2" title="Permalink to this definition">¶</a></dt><dd><p>Conditional variances of resids</p>
+<dt id="arch.univariate.StudentsT.loglikelihood.sigma2"><span class="n sig-name">sigma2</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a></span></span><a class="headerlink" href="#arch.univariate.StudentsT.loglikelihood.sigma2" title="Permalink to this definition">¶</a></dt><dd><p>Conditional variances of resids</p>
 </dd>
 <dt id="arch.univariate.StudentsT.loglikelihood.individual"><span class="n sig-name">individual</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)">bool</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code><a class="headerlink" href="#arch.univariate.StudentsT.loglikelihood.individual" title="Permalink to this definition">¶</a></dt><dd><p>Flag indicating whether to return the vector of individual log
 likelihoods (True) or the sum (False)</p>
@@ -1292,7 +1292,7 @@ <h1 id="arch-univariate-studentst-loglikelihood">arch.univariate.StudentsT.logli
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+++ b/devel/univariate/generated/generated/arch.univariate.StudentsT.moment.html
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@@ -1115,14 +1115,14 @@
 <h1 id="arch-univariate-studentst-moment">arch.univariate.StudentsT.moment<a class="headerlink" href="#arch-univariate-studentst-moment" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.StudentsT.moment">
-<span class="sig-prename descclassname"><span class="pre">StudentsT.</span></span><span class="sig-name descname"><span class="pre">moment</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.StudentsT.moment.n" title="arch.univariate.StudentsT.moment.n (Python parameter) — Order of moment"><span class="n"><span class="pre">n</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.StudentsT.moment.parameters" title="arch.univariate.StudentsT.moment.parameters (Python parameter) — Distribution parameters. Use None for parameterless distributions."><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/distribution.html#StudentsT.moment"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.StudentsT.moment" title="Link to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">StudentsT.</span></span><span class="sig-name descname"><span class="pre">moment</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.StudentsT.moment.n" title="arch.univariate.StudentsT.moment.n (Python parameter) — Order of moment"><span class="n"><span class="pre">n</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.StudentsT.moment.parameters" title="arch.univariate.StudentsT.moment.parameters (Python parameter) — Distribution parameters. Use None for parameterless distributions."><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/distribution.html#StudentsT.moment"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.StudentsT.moment" title="Link to this definition">¶</a></dt>
 <dd><p>Moment of order n</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.StudentsT.moment-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.StudentsT.moment-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
 <dt id="arch.univariate.StudentsT.moment.n"><span class="n sig-name">n</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a></span></span><a class="headerlink" href="#arch.univariate.StudentsT.moment.n" title="Permalink to this definition">¶</a></dt><dd><p>Order of moment</p>
 </dd>
-<dt id="arch.univariate.StudentsT.moment.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.StudentsT.moment.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Distribution parameters. Use None for parameterless distributions.</p>
+<dt id="arch.univariate.StudentsT.moment.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.StudentsT.moment.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Distribution parameters. Use None for parameterless distributions.</p>
 </dd>
 </dl>
 </dd>
@@ -1249,7 +1249,7 @@ <h1 id="arch-univariate-studentst-moment">arch.univariate.StudentsT.moment<a cla
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diff --git a/devel/univariate/generated/generated/arch.univariate.StudentsT.name.html b/devel/univariate/generated/generated/arch.univariate.StudentsT.name.html
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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@@ -1139,7 +1139,7 @@ <h1 id="arch-univariate-studentst-name">arch.univariate.StudentsT.name<a class="
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diff --git a/devel/univariate/generated/generated/arch.univariate.StudentsT.parameter_names.html b/devel/univariate/generated/generated/arch.univariate.StudentsT.parameter_names.html
index ea08e9d332..7d8a8943a5 100644
--- a/devel/univariate/generated/generated/arch.univariate.StudentsT.parameter_names.html
+++ b/devel/univariate/generated/generated/arch.univariate.StudentsT.parameter_names.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.StudentsT.parameter_names - arch v6.2.1 (+21)</title>
+        <title>arch.univariate.StudentsT.parameter_names - arch v6.2.1 (+23)</title>
       
     
 
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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+            arch v6.2.1 (+23)
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@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
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-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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@@ -1187,7 +1187,7 @@ <h1 id="arch-univariate-studentst-parameter-names">arch.univariate.StudentsT.par
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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diff --git a/devel/univariate/generated/generated/arch.univariate.StudentsT.partial_moment.html b/devel/univariate/generated/generated/arch.univariate.StudentsT.partial_moment.html
index 3755a2069e..1ffde4a80d 100644
--- a/devel/univariate/generated/generated/arch.univariate.StudentsT.partial_moment.html
+++ b/devel/univariate/generated/generated/arch.univariate.StudentsT.partial_moment.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.StudentsT.partial_moment - arch v6.2.1 (+21)</title>
+        <title>arch.univariate.StudentsT.partial_moment - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
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@@ -92,7 +92,7 @@
 
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-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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@@ -189,10 +189,10 @@
 
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   <label class="md-nav__title" for="__drawer">
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+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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@@ -1129,7 +1129,7 @@
 <h1 id="arch-univariate-studentst-partial-moment">arch.univariate.StudentsT.partial_moment<a class="headerlink" href="#arch-univariate-studentst-partial-moment" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.StudentsT.partial_moment">
-<span class="sig-prename descclassname"><span class="pre">StudentsT.</span></span><span class="sig-name descname"><span class="pre">partial_moment</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.StudentsT.partial_moment.n" title="arch.univariate.StudentsT.partial_moment.n (Python parameter) — Order of partial moment"><span class="n"><span class="pre">n</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.StudentsT.partial_moment.z" title="arch.univariate.StudentsT.partial_moment.z (Python parameter) — Upper bound for partial moment integral"><span class="n"><span class="pre">z</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mf">0.0</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.StudentsT.partial_moment.parameters" title="arch.univariate.StudentsT.partial_moment.parameters (Python parameter) — Distribution parameters.  Use None for parameterless distributions."><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/distribution.html#StudentsT.partial_moment"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.StudentsT.partial_moment" title="Link to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">StudentsT.</span></span><span class="sig-name descname"><span class="pre">partial_moment</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.StudentsT.partial_moment.n" title="arch.univariate.StudentsT.partial_moment.n (Python parameter) — Order of partial moment"><span class="n"><span class="pre">n</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.StudentsT.partial_moment.z" title="arch.univariate.StudentsT.partial_moment.z (Python parameter) — Upper bound for partial moment integral"><span class="n"><span class="pre">z</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mf">0.0</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.StudentsT.partial_moment.parameters" title="arch.univariate.StudentsT.partial_moment.parameters (Python parameter) — Distribution parameters.  Use None for parameterless distributions."><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/distribution.html#StudentsT.partial_moment"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.StudentsT.partial_moment" title="Link to this definition">¶</a></dt>
 <dd><p>Order n lower partial moment from -inf to z</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.StudentsT.partial_moment-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.StudentsT.partial_moment-parameters" title="Permalink to this headline">¶</a></dt>
@@ -1138,7 +1138,7 @@ <h1 id="arch-univariate-studentst-partial-moment">arch.univariate.StudentsT.part
 </dd>
 <dt id="arch.univariate.StudentsT.partial_moment.z"><span class="n sig-name">z</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mf">0.0</span></code><a class="headerlink" href="#arch.univariate.StudentsT.partial_moment.z" title="Permalink to this definition">¶</a></dt><dd><p>Upper bound for partial moment integral</p>
 </dd>
-<dt id="arch.univariate.StudentsT.partial_moment.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.StudentsT.partial_moment.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Distribution parameters.  Use None for parameterless distributions.</p>
+<dt id="arch.univariate.StudentsT.partial_moment.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.StudentsT.partial_moment.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Distribution parameters.  Use None for parameterless distributions.</p>
 </dd>
 </dl>
 </dd>
@@ -1278,7 +1278,7 @@ <h1 id="arch-univariate-studentst-partial-moment">arch.univariate.StudentsT.part
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@@ -15,7 +15,7 @@
     
   
       
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@@ -1115,14 +1115,14 @@
 <h1 id="arch-univariate-studentst-ppf">arch.univariate.StudentsT.ppf<a class="headerlink" href="#arch-univariate-studentst-ppf" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.StudentsT.ppf">
-<span class="sig-prename descclassname"><span class="pre">StudentsT.</span></span><span class="sig-name descname"><span class="pre">ppf</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.StudentsT.ppf.pits" title="arch.univariate.StudentsT.ppf.pits (Python parameter) — Probability-integral-transformed values in the interval (0, 1)."><span class="n"><span class="pre">pits</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.StudentsT.ppf.parameters" title="arch.univariate.StudentsT.ppf.parameters (Python parameter) — Distribution parameters. Use None for parameterless distributions."><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/distribution.html#StudentsT.ppf"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.StudentsT.ppf" title="Link to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">StudentsT.</span></span><span class="sig-name descname"><span class="pre">ppf</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.StudentsT.ppf.pits" title="arch.univariate.StudentsT.ppf.pits (Python parameter) — Probability-integral-transformed values in the interval (0, 1)."><span class="n"><span class="pre">pits</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.StudentsT.ppf.parameters" title="arch.univariate.StudentsT.ppf.parameters (Python parameter) — Distribution parameters. Use None for parameterless distributions."><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/distribution.html#StudentsT.ppf"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.StudentsT.ppf" title="Link to this definition">¶</a></dt>
 <dd><p>Inverse cumulative density function (ICDF)</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.StudentsT.ppf-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.StudentsT.ppf-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.StudentsT.ppf.pits"><span class="n sig-name">pits</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.univariate.StudentsT.ppf.pits" title="Permalink to this definition">¶</a></dt><dd><p>Probability-integral-transformed values in the interval (0, 1).</p>
+<dt id="arch.univariate.StudentsT.ppf.pits"><span class="n sig-name">pits</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a></span></span><a class="headerlink" href="#arch.univariate.StudentsT.ppf.pits" title="Permalink to this definition">¶</a></dt><dd><p>Probability-integral-transformed values in the interval (0, 1).</p>
 </dd>
-<dt id="arch.univariate.StudentsT.ppf.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.StudentsT.ppf.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Distribution parameters. Use <code class="docutils literal notranslate"><span class="pre">None</span></code> for parameterless
+<dt id="arch.univariate.StudentsT.ppf.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.StudentsT.ppf.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Distribution parameters. Use <code class="docutils literal notranslate"><span class="pre">None</span></code> for parameterless
 distributions.</p>
 </dd>
 </dl>
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diff --git a/devel/univariate/generated/generated/arch.univariate.StudentsT.simulate.html b/devel/univariate/generated/generated/arch.univariate.StudentsT.simulate.html
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 <h1 id="arch-univariate-studentst-simulate">arch.univariate.StudentsT.simulate<a class="headerlink" href="#arch-univariate-studentst-simulate" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.StudentsT.simulate">
-<span class="sig-prename descclassname"><span class="pre">StudentsT.</span></span><span class="sig-name descname"><span class="pre">simulate</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.StudentsT.simulate.parameters" title="arch.univariate.StudentsT.simulate.parameters (Python parameter) — Distribution parameters"><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.12)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/distribution.html#StudentsT.simulate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.StudentsT.simulate" title="Link to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">StudentsT.</span></span><span class="sig-name descname"><span class="pre">simulate</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.StudentsT.simulate.parameters" title="arch.univariate.StudentsT.simulate.parameters (Python parameter) — Distribution parameters"><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a></span></span></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.12)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/distribution.html#StudentsT.simulate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.StudentsT.simulate" title="Link to this definition">¶</a></dt>
 <dd><p>Simulates i.i.d. draws from the distribution</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.StudentsT.simulate-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.StudentsT.simulate-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.StudentsT.simulate.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.univariate.StudentsT.simulate.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Distribution parameters</p>
+<dt id="arch.univariate.StudentsT.simulate.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a></span></span><a class="headerlink" href="#arch.univariate.StudentsT.simulate.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Distribution parameters</p>
 </dd>
 </dl>
 </dd>
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diff --git a/devel/univariate/generated/generated/arch.univariate.StudentsT.starting_values.html b/devel/univariate/generated/generated/arch.univariate.StudentsT.starting_values.html
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-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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@@ -1302,7 +1302,7 @@
 <h1 id="arch-univariate-zeromean-fit">arch.univariate.ZeroMean.fit<a class="headerlink" href="#arch-univariate-zeromean-fit" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.ZeroMean.fit">
-<span class="sig-prename descclassname"><span class="pre">ZeroMean.</span></span><span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ZeroMean.fit.update_freq" title="arch.univariate.ZeroMean.fit.update_freq (Python parameter) — Frequency of iteration updates.  Output is generated every update_freq iterations. Set to 0 to disable iterative output."><span class="n"><span class="pre">update_freq</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ZeroMean.fit.disp" title="arch.univariate.ZeroMean.fit.disp (Python parameter) — Either 'final' to print optimization result or 'off' to display nothing. If using a boolean, False is &quot;off&quot; and True is &quot;final&quot;"><span class="n"><span class="pre">disp</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'off'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'final'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;final&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ZeroMean.fit.starting_values" title="arch.univariate.ZeroMean.fit.starting_values (Python parameter) — Array of starting values to use.  If not provided, starting values are constructed by the model components."><span class="n"><span class="pre">starting_values</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ZeroMean.fit.cov_type" title="arch.univariate.ZeroMean.fit.cov_type (Python parameter) — Estimation method of parameter covariance.  Supported options are 'robust', which does not assume the Information Matrix Equality holds and 'classic' which does.  In the ARCH literature, 'robust' corresponds to Bollerslev-Wooldridge covariance estimator."><span class="n"><span class="pre">cov_type</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'robust'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'classic'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;robust&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ZeroMean.fit.show_warning" title="arch.univariate.ZeroMean.fit.show_warning (Python parameter) — Flag indicating whether convergence warnings should be shown."><span class="n"><span class="pre">show_warning</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ZeroMean.fit.first_obs" title="arch.univariate.ZeroMean.fit.first_obs (Python parameter) — First observation to use when estimating model"><span class="n"><span class="pre">first_obs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.12)"><span class="pre">datetime</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)"><span class="pre">datetime64</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.1)"><span class="pre">Timestamp</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ZeroMean.fit.last_obs" title="arch.univariate.ZeroMean.fit.last_obs (Python parameter) — Last observation to use when estimating model"><span class="n"><span class="pre">last_obs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.12)"><span class="pre">datetime</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)"><span class="pre">datetime64</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.1)"><span class="pre">Timestamp</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ZeroMean.fit.tol" title="arch.univariate.ZeroMean.fit.tol (Python parameter) — Tolerance for termination."><span class="n"><span class="pre">tol</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ZeroMean.fit.options" title="arch.univariate.ZeroMean.fit.options (Python parameter) — Options to pass to scipy.optimize.minimize.  Valid entries include 'ftol', 'eps', 'disp', and 'maxiter'."><span class="n"><span class="pre">options</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.12)"><span class="pre">Any</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ZeroMean.fit.backcast" title="arch.univariate.ZeroMean.fit.backcast (Python parameter) — Value to use as backcast. Should be measure \sigma^2_0 since model-specific non-linear transformations are applied to value before computing the variance recursions."><span class="n"><span class="pre">backcast</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference internal" href="../arch.univariate.base.ARCHModelResult.html#arch.univariate.base.ARCHModelResult" title="arch.univariate.base.ARCHModelResult (Python class) — Results from estimation of an ARCHModel model"><span class="pre">ARCHModelResult</span></a></span></span></span><a class="headerlink" href="#arch.univariate.ZeroMean.fit" title="Link to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">ZeroMean.</span></span><span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ZeroMean.fit.update_freq" title="arch.univariate.ZeroMean.fit.update_freq (Python parameter) — Frequency of iteration updates.  Output is generated every update_freq iterations. Set to 0 to disable iterative output."><span class="n"><span class="pre">update_freq</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ZeroMean.fit.disp" title="arch.univariate.ZeroMean.fit.disp (Python parameter) — Either 'final' to print optimization result or 'off' to display nothing. If using a boolean, False is &quot;off&quot; and True is &quot;final&quot;"><span class="n"><span class="pre">disp</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'off'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'final'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;final&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ZeroMean.fit.starting_values" title="arch.univariate.ZeroMean.fit.starting_values (Python parameter) — Array of starting values to use.  If not provided, starting values are constructed by the model components."><span class="n"><span class="pre">starting_values</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ZeroMean.fit.cov_type" title="arch.univariate.ZeroMean.fit.cov_type (Python parameter) — Estimation method of parameter covariance.  Supported options are 'robust', which does not assume the Information Matrix Equality holds and 'classic' which does.  In the ARCH literature, 'robust' corresponds to Bollerslev-Wooldridge covariance estimator."><span class="n"><span class="pre">cov_type</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'robust'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'classic'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;robust&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ZeroMean.fit.show_warning" title="arch.univariate.ZeroMean.fit.show_warning (Python parameter) — Flag indicating whether convergence warnings should be shown."><span class="n"><span class="pre">show_warning</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ZeroMean.fit.first_obs" title="arch.univariate.ZeroMean.fit.first_obs (Python parameter) — First observation to use when estimating model"><span class="n"><span class="pre">first_obs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.12)"><span class="pre">datetime</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)"><span class="pre">datetime64</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.4)"><span class="pre">Timestamp</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ZeroMean.fit.last_obs" title="arch.univariate.ZeroMean.fit.last_obs (Python parameter) — Last observation to use when estimating model"><span class="n"><span class="pre">last_obs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.12)"><span class="pre">datetime</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)"><span class="pre">datetime64</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.4)"><span class="pre">Timestamp</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ZeroMean.fit.tol" title="arch.univariate.ZeroMean.fit.tol (Python parameter) — Tolerance for termination."><span class="n"><span class="pre">tol</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ZeroMean.fit.options" title="arch.univariate.ZeroMean.fit.options (Python parameter) — Options to pass to scipy.optimize.minimize.  Valid entries include 'ftol', 'eps', 'disp', and 'maxiter'."><span class="n"><span class="pre">options</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.12)"><span class="pre">Any</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ZeroMean.fit.backcast" title="arch.univariate.ZeroMean.fit.backcast (Python parameter) — Value to use as backcast. Should be measure \sigma^2_0 since model-specific non-linear transformations are applied to value before computing the variance recursions."><span class="n"><span class="pre">backcast</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference internal" href="../arch.univariate.base.ARCHModelResult.html#arch.univariate.base.ARCHModelResult" title="arch.univariate.base.ARCHModelResult (Python class) — Results from estimation of an ARCHModel model"><span class="pre">ARCHModelResult</span></a></span></span></span><a class="headerlink" href="#arch.univariate.ZeroMean.fit" title="Link to this definition">¶</a></dt>
 <dd><p>Estimate model parameters</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.ZeroMean.fit-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.ZeroMean.fit-parameters" title="Permalink to this headline">¶</a></dt>
@@ -1313,7 +1313,7 @@ <h1 id="arch-univariate-zeromean-fit">arch.univariate.ZeroMean.fit<a class="head
 <dt id="arch.univariate.ZeroMean.fit.disp"><span class="n sig-name">disp</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)">bool</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'off'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'final'</span></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;final&#39;</span></code><a class="headerlink" href="#arch.univariate.ZeroMean.fit.disp" title="Permalink to this definition">¶</a></dt><dd><p>Either ‘final’ to print optimization result or ‘off’ to display
 nothing. If using a boolean, False is “off” and True is “final”</p>
 </dd>
-<dt id="arch.univariate.ZeroMean.fit.starting_values"><span class="n sig-name">starting_values</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ZeroMean.fit.starting_values" title="Permalink to this definition">¶</a></dt><dd><p>Array of starting values to use.  If not provided, starting values
+<dt id="arch.univariate.ZeroMean.fit.starting_values"><span class="n sig-name">starting_values</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ZeroMean.fit.starting_values" title="Permalink to this definition">¶</a></dt><dd><p>Array of starting values to use.  If not provided, starting values
 are constructed by the model components.</p>
 </dd>
 <dt id="arch.univariate.ZeroMean.fit.cov_type"><span class="n sig-name">cov_type</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><span class="s">'robust'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'classic'</span></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;robust&#39;</span></code><a class="headerlink" href="#arch.univariate.ZeroMean.fit.cov_type" title="Permalink to this definition">¶</a></dt><dd><p>Estimation method of parameter covariance.  Supported options are
@@ -1323,9 +1323,9 @@ <h1 id="arch-univariate-zeromean-fit">arch.univariate.ZeroMean.fit<a class="head
 </dd>
 <dt id="arch.univariate.ZeroMean.fit.show_warning"><span class="n sig-name">show_warning</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)">bool</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code><a class="headerlink" href="#arch.univariate.ZeroMean.fit.show_warning" title="Permalink to this definition">¶</a></dt><dd><p>Flag indicating whether convergence warnings should be shown.</p>
 </dd>
-<dt id="arch.univariate.ZeroMean.fit.first_obs"><span class="n sig-name">first_obs</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)">str</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.12)">datetime</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)">datetime64</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.1)">Timestamp</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ZeroMean.fit.first_obs" title="Permalink to this definition">¶</a></dt><dd><p>First observation to use when estimating model</p>
+<dt id="arch.univariate.ZeroMean.fit.first_obs"><span class="n sig-name">first_obs</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)">str</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.12)">datetime</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)">datetime64</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.4)">Timestamp</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ZeroMean.fit.first_obs" title="Permalink to this definition">¶</a></dt><dd><p>First observation to use when estimating model</p>
 </dd>
-<dt id="arch.univariate.ZeroMean.fit.last_obs"><span class="n sig-name">last_obs</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)">str</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.12)">datetime</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)">datetime64</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.1)">Timestamp</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ZeroMean.fit.last_obs" title="Permalink to this definition">¶</a></dt><dd><p>Last observation to use when estimating model</p>
+<dt id="arch.univariate.ZeroMean.fit.last_obs"><span class="n sig-name">last_obs</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)">str</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.12)">datetime</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)">datetime64</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.4)">Timestamp</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ZeroMean.fit.last_obs" title="Permalink to this definition">¶</a></dt><dd><p>Last observation to use when estimating model</p>
 </dd>
 <dt id="arch.univariate.ZeroMean.fit.tol"><span class="n sig-name">tol</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ZeroMean.fit.tol" title="Permalink to this definition">¶</a></dt><dd><p>Tolerance for termination.</p>
 </dd>
@@ -1465,7 +1465,7 @@ <h1 id="arch-univariate-zeromean-fit">arch.univariate.ZeroMean.fit<a class="head
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diff --git a/devel/univariate/generated/generated/arch.univariate.ZeroMean.fix.html b/devel/univariate/generated/generated/arch.univariate.ZeroMean.fix.html
index f1ef305d4a..00a70003d2 100644
--- a/devel/univariate/generated/generated/arch.univariate.ZeroMean.fix.html
+++ b/devel/univariate/generated/generated/arch.univariate.ZeroMean.fix.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ZeroMean.fix - arch v6.2.1 (+21)</title>
+        <title>arch.univariate.ZeroMean.fix - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
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-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
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-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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     <div class="md-nav__source">
@@ -1204,18 +1204,18 @@
 <h1 id="arch-univariate-zeromean-fix">arch.univariate.ZeroMean.fix<a class="headerlink" href="#arch-univariate-zeromean-fix" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.ZeroMean.fix">
-<span class="sig-prename descclassname"><span class="pre">ZeroMean.</span></span><span class="sig-name descname"><span class="pre">fix</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ZeroMean.fix.params" title="arch.univariate.ZeroMean.fix.params (Python parameter) — User specified parameters to use when generating the result. Must have the correct number of parameters for a given choice of mean model, volatility model and distribution."><span class="n"><span class="pre">params</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ZeroMean.fix.first_obs" title="arch.univariate.ZeroMean.fix.first_obs (Python parameter) — First observation to use when fixing model"><span class="n"><span class="pre">first_obs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.12)"><span class="pre">datetime</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)"><span class="pre">datetime64</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.1)"><span class="pre">Timestamp</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ZeroMean.fix.last_obs" title="arch.univariate.ZeroMean.fix.last_obs (Python parameter) — Last observation to use when fixing model"><span class="n"><span class="pre">last_obs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.12)"><span class="pre">datetime</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)"><span class="pre">datetime64</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.1)"><span class="pre">Timestamp</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference internal" href="../arch.univariate.base.ARCHModelFixedResult.html#arch.univariate.base.ARCHModelFixedResult" title="arch.univariate.base.ARCHModelFixedResult (Python class) — Results for fixed parameters for an ARCHModel model"><span class="pre">ARCHModelFixedResult</span></a></span></span></span><a class="headerlink" href="#arch.univariate.ZeroMean.fix" title="Link to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">ZeroMean.</span></span><span class="sig-name descname"><span class="pre">fix</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ZeroMean.fix.params" title="arch.univariate.ZeroMean.fix.params (Python parameter) — User specified parameters to use when generating the result. Must have the correct number of parameters for a given choice of mean model, volatility model and distribution."><span class="n"><span class="pre">params</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ZeroMean.fix.first_obs" title="arch.univariate.ZeroMean.fix.first_obs (Python parameter) — First observation to use when fixing model"><span class="n"><span class="pre">first_obs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.12)"><span class="pre">datetime</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)"><span class="pre">datetime64</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.4)"><span class="pre">Timestamp</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ZeroMean.fix.last_obs" title="arch.univariate.ZeroMean.fix.last_obs (Python parameter) — Last observation to use when fixing model"><span class="n"><span class="pre">last_obs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.12)"><span class="pre">datetime</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)"><span class="pre">datetime64</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.4)"><span class="pre">Timestamp</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference internal" href="../arch.univariate.base.ARCHModelFixedResult.html#arch.univariate.base.ARCHModelFixedResult" title="arch.univariate.base.ARCHModelFixedResult (Python class) — Results for fixed parameters for an ARCHModel model"><span class="pre">ARCHModelFixedResult</span></a></span></span></span><a class="headerlink" href="#arch.univariate.ZeroMean.fix" title="Link to this definition">¶</a></dt>
 <dd><p>Allows an ARCHModelFixedResult to be constructed from fixed parameters.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.ZeroMean.fix-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.ZeroMean.fix-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.ZeroMean.fix.params"><span class="n sig-name">params</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.univariate.ZeroMean.fix.params" title="Permalink to this definition">¶</a></dt><dd><p>User specified parameters to use when generating the result. Must
+<dt id="arch.univariate.ZeroMean.fix.params"><span class="n sig-name">params</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a></span></span><a class="headerlink" href="#arch.univariate.ZeroMean.fix.params" title="Permalink to this definition">¶</a></dt><dd><p>User specified parameters to use when generating the result. Must
 have the correct number of parameters for a given choice of mean
 model, volatility model and distribution.</p>
 </dd>
-<dt id="arch.univariate.ZeroMean.fix.first_obs"><span class="n sig-name">first_obs</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)">str</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.12)">datetime</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)">datetime64</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.1)">Timestamp</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ZeroMean.fix.first_obs" title="Permalink to this definition">¶</a></dt><dd><p>First observation to use when fixing model</p>
+<dt id="arch.univariate.ZeroMean.fix.first_obs"><span class="n sig-name">first_obs</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)">str</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.12)">datetime</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)">datetime64</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.4)">Timestamp</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ZeroMean.fix.first_obs" title="Permalink to this definition">¶</a></dt><dd><p>First observation to use when fixing model</p>
 </dd>
-<dt id="arch.univariate.ZeroMean.fix.last_obs"><span class="n sig-name">last_obs</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)">str</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.12)">datetime</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)">datetime64</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.1)">Timestamp</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ZeroMean.fix.last_obs" title="Permalink to this definition">¶</a></dt><dd><p>Last observation to use when fixing model</p>
+<dt id="arch.univariate.ZeroMean.fix.last_obs"><span class="n sig-name">last_obs</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)">str</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.12)">datetime</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)">datetime64</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.4)">Timestamp</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ZeroMean.fix.last_obs" title="Permalink to this definition">¶</a></dt><dd><p>Last observation to use when fixing model</p>
 </dd>
 </dl>
 </dd>
@@ -1344,7 +1344,7 @@ <h1 id="arch-univariate-zeromean-fix">arch.univariate.ZeroMean.fix<a class="head
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diff --git a/devel/univariate/generated/generated/arch.univariate.ZeroMean.forecast.html b/devel/univariate/generated/generated/arch.univariate.ZeroMean.forecast.html
index be5d8f9399..f29a00a25f 100644
--- a/devel/univariate/generated/generated/arch.univariate.ZeroMean.forecast.html
+++ b/devel/univariate/generated/generated/arch.univariate.ZeroMean.forecast.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ZeroMean.forecast - arch v6.2.1 (+21)</title>
+        <title>arch.univariate.ZeroMean.forecast - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
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 <h1 id="arch-univariate-zeromean-forecast">arch.univariate.ZeroMean.forecast<a class="headerlink" href="#arch-univariate-zeromean-forecast" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.ZeroMean.forecast">
-<span class="sig-prename descclassname"><span class="pre">ZeroMean.</span></span><span class="sig-name descname"><span class="pre">forecast</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ZeroMean.forecast.params" title="arch.univariate.ZeroMean.forecast.params (Python parameter) — Parameters required to forecast. Must be identical in shape to the parameters computed by fitting the model."><span class="n"><span class="pre">params</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ZeroMean.forecast.horizon" title="arch.univariate.ZeroMean.forecast.horizon (Python parameter) — Number of steps to forecast"><span class="n"><span class="pre">horizon</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ZeroMean.forecast.start" title="arch.univariate.ZeroMean.forecast.start (Python parameter) — An integer, datetime or str indicating the first observation to produce the forecast for.  Datetimes can only be used with pandas inputs that have a datetime index. Strings must be convertible to a date time, such as in '1945-01-01'."><span class="n"><span class="pre">start</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.12)"><span class="pre">datetime</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)"><span class="pre">datetime64</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.1)"><span class="pre">Timestamp</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ZeroMean.forecast.align" title="arch.univariate.ZeroMean.forecast.align (Python parameter) — Either 'origin' or 'target'.  When set of 'origin', the t-th row of forecasts contains the forecasts for t+1, t+2, ..., t+h. When set to 'target', the t-th row contains the 1-step ahead forecast from time t-1, the 2 step from time t-2, ..., and the h-step from time t-h.  'target' simplified computing forecast errors since the realization and h-step forecast are aligned."><span class="n"><span class="pre">align</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'origin'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'target'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;origin&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ZeroMean.forecast.method" title="arch.univariate.ZeroMean.forecast.method (Python parameter) — Method to use when producing the forecast. The default is analytic. The method only affects the variance forecast generation.  Not all volatility models support all methods. In particular, volatility models that do not evolve in squares such as EGARCH or TARCH do not support the 'analytic' method for horizons &gt; 1."><span class="n"><span class="pre">method</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'analytic'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'simulation'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'bootstrap'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;analytic&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ZeroMean.forecast.simulations" title="arch.univariate.ZeroMean.forecast.simulations (Python parameter) — Number of simulations to run when computing the forecast using either simulation or bootstrap."><span class="n"><span class="pre">simulations</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ZeroMean.forecast.rng" title="arch.univariate.ZeroMean.forecast.rng (Python parameter) — Custom random number generator to use in simulation-based forecasts. Must produce random samples using the syntax rng(size) where size the 2-element tuple (simulations, horizon)."><span class="n"><span class="pre">rng</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.12)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ZeroMean.forecast.random_state" title="arch.univariate.ZeroMean.forecast.random_state (Python parameter) — NumPy RandomState instance to use when method is 'bootstrap'"><span class="n"><span class="pre">random_state</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.26)"><span class="pre">RandomState</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ZeroMean.forecast.reindex" title="arch.univariate.ZeroMean.forecast.reindex (Python parameter) — Whether to reindex the forecasts to have the same dimension as the series being forecast. Prior to 4.18 this was the default. As of 4.19 this is now optional. If not provided, a warning is raised about the future change in the default which will occur after September 2021."><span class="n"><span class="pre">reindex</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ZeroMean.forecast.x" title="arch.univariate.ZeroMean.forecast.x (Python parameter) — Values to use for exogenous regressors if any are included in the model. Three formats are accepted:"><span class="n"><span class="pre">x</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Hashable" title="(in Python v3.12)"><span class="pre">Hashable</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference internal" href="../arch.univariate.base.ARCHModelForecast.html#arch.univariate.base.ARCHModelForecast" title="arch.univariate.base.ARCHModelForecast (Python class) — Container for forecasts from an ARCH Model"><span class="pre">ARCHModelForecast</span></a></span></span></span><a class="headerlink" href="#arch.univariate.ZeroMean.forecast" title="Link to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">ZeroMean.</span></span><span class="sig-name descname"><span class="pre">forecast</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ZeroMean.forecast.params" title="arch.univariate.ZeroMean.forecast.params (Python parameter) — Parameters required to forecast. Must be identical in shape to the parameters computed by fitting the model."><span class="n"><span class="pre">params</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ZeroMean.forecast.horizon" title="arch.univariate.ZeroMean.forecast.horizon (Python parameter) — Number of steps to forecast"><span class="n"><span class="pre">horizon</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ZeroMean.forecast.start" title="arch.univariate.ZeroMean.forecast.start (Python parameter) — An integer, datetime or str indicating the first observation to produce the forecast for.  Datetimes can only be used with pandas inputs that have a datetime index. Strings must be convertible to a date time, such as in '1945-01-01'."><span class="n"><span class="pre">start</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.12)"><span class="pre">datetime</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)"><span class="pre">datetime64</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.4)"><span class="pre">Timestamp</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ZeroMean.forecast.align" title="arch.univariate.ZeroMean.forecast.align (Python parameter) — Either 'origin' or 'target'.  When set of 'origin', the t-th row of forecasts contains the forecasts for t+1, t+2, ..., t+h. When set to 'target', the t-th row contains the 1-step ahead forecast from time t-1, the 2 step from time t-2, ..., and the h-step from time t-h.  'target' simplified computing forecast errors since the realization and h-step forecast are aligned."><span class="n"><span class="pre">align</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'origin'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'target'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;origin&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ZeroMean.forecast.method" title="arch.univariate.ZeroMean.forecast.method (Python parameter) — Method to use when producing the forecast. The default is analytic. The method only affects the variance forecast generation.  Not all volatility models support all methods. In particular, volatility models that do not evolve in squares such as EGARCH or TARCH do not support the 'analytic' method for horizons &gt; 1."><span class="n"><span class="pre">method</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'analytic'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'simulation'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'bootstrap'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;analytic&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ZeroMean.forecast.simulations" title="arch.univariate.ZeroMean.forecast.simulations (Python parameter) — Number of simulations to run when computing the forecast using either simulation or bootstrap."><span class="n"><span class="pre">simulations</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ZeroMean.forecast.rng" title="arch.univariate.ZeroMean.forecast.rng (Python parameter) — Custom random number generator to use in simulation-based forecasts. Must produce random samples using the syntax rng(size) where size the 2-element tuple (simulations, horizon)."><span class="n"><span class="pre">rng</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.12)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ZeroMean.forecast.random_state" title="arch.univariate.ZeroMean.forecast.random_state (Python parameter) — NumPy RandomState instance to use when method is 'bootstrap'"><span class="n"><span class="pre">random_state</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.26)"><span class="pre">RandomState</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ZeroMean.forecast.reindex" title="arch.univariate.ZeroMean.forecast.reindex (Python parameter) — Whether to reindex the forecasts to have the same dimension as the series being forecast. Prior to 4.18 this was the default. As of 4.19 this is now optional. If not provided, a warning is raised about the future change in the default which will occur after September 2021."><span class="n"><span class="pre">reindex</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ZeroMean.forecast.x" title="arch.univariate.ZeroMean.forecast.x (Python parameter) — Values to use for exogenous regressors if any are included in the model. Three formats are accepted:"><span class="n"><span class="pre">x</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Hashable" title="(in Python v3.12)"><span class="pre">Hashable</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference internal" href="../arch.univariate.base.ARCHModelForecast.html#arch.univariate.base.ARCHModelForecast" title="arch.univariate.base.ARCHModelForecast (Python class) — Container for forecasts from an ARCH Model"><span class="pre">ARCHModelForecast</span></a></span></span></span><a class="headerlink" href="#arch.univariate.ZeroMean.forecast" title="Link to this definition">¶</a></dt>
 <dd><p>Construct forecasts from estimated model</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.ZeroMean.forecast-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.ZeroMean.forecast-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.ZeroMean.forecast.params"><span class="n sig-name">params</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.univariate.ZeroMean.forecast.params" title="Permalink to this definition">¶</a></dt><dd><p>Parameters required to forecast. Must be identical in
+<dt id="arch.univariate.ZeroMean.forecast.params"><span class="n sig-name">params</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a></span></span><a class="headerlink" href="#arch.univariate.ZeroMean.forecast.params" title="Permalink to this definition">¶</a></dt><dd><p>Parameters required to forecast. Must be identical in
 shape to the parameters computed by fitting the model.</p>
 </dd>
 <dt id="arch.univariate.ZeroMean.forecast.horizon"><span class="n sig-name">horizon</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code><a class="headerlink" href="#arch.univariate.ZeroMean.forecast.horizon" title="Permalink to this definition">¶</a></dt><dd><p>Number of steps to forecast</p>
 </dd>
-<dt id="arch.univariate.ZeroMean.forecast.start"><span class="n sig-name">start</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)">str</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.12)">datetime</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)">datetime64</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.1)">Timestamp</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ZeroMean.forecast.start" title="Permalink to this definition">¶</a></dt><dd><p>An integer, datetime or str indicating the first observation to
+<dt id="arch.univariate.ZeroMean.forecast.start"><span class="n sig-name">start</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)">str</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.12)">datetime</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)">datetime64</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.4)">Timestamp</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ZeroMean.forecast.start" title="Permalink to this definition">¶</a></dt><dd><p>An integer, datetime or str indicating the first observation to
 produce the forecast for.  Datetimes can only be used with pandas
 inputs that have a datetime index. Strings must be convertible
 to a date time, such as in ‘1945-01-01’.</p>
@@ -1348,7 +1348,7 @@ <h1 id="arch-univariate-zeromean-forecast">arch.univariate.ZeroMean.forecast<a c
 </div>
 </p>
 </dd>
-<dt id="arch.univariate.ZeroMean.forecast.x"><span class="n sig-name">x</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)">dict</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Hashable" title="(in Python v3.12)">Hashable</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ZeroMean.forecast.x" title="Permalink to this definition">¶</a></dt><dd><p><p>Values to use for exogenous regressors if any are included in the
+<dt id="arch.univariate.ZeroMean.forecast.x"><span class="n sig-name">x</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)">dict</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Hashable" title="(in Python v3.12)">Hashable</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ZeroMean.forecast.x" title="Permalink to this definition">¶</a></dt><dd><p><p>Values to use for exogenous regressors if any are included in the
 model. Three formats are accepted:</p>
 <ul class="simple">
 <li><p>2-d array-like: This format can be used when there is a single
@@ -1530,7 +1530,7 @@ <h1 id="arch-univariate-zeromean-forecast">arch.univariate.ZeroMean.forecast<a c
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diff --git a/devel/univariate/generated/generated/arch.univariate.ZeroMean.name.html b/devel/univariate/generated/generated/arch.univariate.ZeroMean.name.html
index e76dbdd575..7d278af159 100644
--- a/devel/univariate/generated/generated/arch.univariate.ZeroMean.name.html
+++ b/devel/univariate/generated/generated/arch.univariate.ZeroMean.name.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ZeroMean.name - arch v6.2.1 (+21)</title>
+        <title>arch.univariate.ZeroMean.name - arch v6.2.1 (+23)</title>
       
     
 
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@@ -102,7 +102,7 @@
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-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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@@ -189,10 +189,10 @@
 
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       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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@@ -1214,7 +1214,7 @@ <h1 id="arch-univariate-zeromean-name">arch.univariate.ZeroMean.name<a class="he
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diff --git a/devel/univariate/generated/generated/arch.univariate.ZeroMean.num_params.html b/devel/univariate/generated/generated/arch.univariate.ZeroMean.num_params.html
index bac55ec5c4..7cf2da18d1 100644
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+++ b/devel/univariate/generated/generated/arch.univariate.ZeroMean.num_params.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ZeroMean.num_params - arch v6.2.1 (+21)</title>
+        <title>arch.univariate.ZeroMean.num_params - arch v6.2.1 (+23)</title>
       
     
 
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
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+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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@@ -1214,7 +1214,7 @@ <h1 id="arch-univariate-zeromean-num-params">arch.univariate.ZeroMean.num_params
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+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
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diff --git a/devel/univariate/generated/generated/arch.univariate.ZeroMean.parameter_names.html b/devel/univariate/generated/generated/arch.univariate.ZeroMean.parameter_names.html
index 338479088a..7e06ff6067 100644
--- a/devel/univariate/generated/generated/arch.univariate.ZeroMean.parameter_names.html
+++ b/devel/univariate/generated/generated/arch.univariate.ZeroMean.parameter_names.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ZeroMean.parameter_names - arch v6.2.1 (+21)</title>
+        <title>arch.univariate.ZeroMean.parameter_names - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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@@ -1262,7 +1262,7 @@ <h1 id="arch-univariate-zeromean-parameter-names">arch.univariate.ZeroMean.param
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diff --git a/devel/univariate/generated/generated/arch.univariate.ZeroMean.resids.html b/devel/univariate/generated/generated/arch.univariate.ZeroMean.resids.html
index dd5e5701e6..ee501e5465 100644
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+++ b/devel/univariate/generated/generated/arch.univariate.ZeroMean.resids.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ZeroMean.resids - arch v6.2.1 (+21)</title>
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@@ -47,7 +47,7 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
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   <nav class="md-header__inner md-grid" aria-label="Header">
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+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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     <label class="md-header__button md-icon" for="__drawer">
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         <div class="md-header__topic">
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-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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@@ -1204,16 +1204,16 @@
 <h1 id="arch-univariate-zeromean-resids">arch.univariate.ZeroMean.resids<a class="headerlink" href="#arch-univariate-zeromean-resids" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.ZeroMean.resids">
-<span class="sig-prename descclassname"><span class="pre">ZeroMean.</span></span><span class="sig-name descname"><span class="pre">resids</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ZeroMean.resids.params" title="arch.univariate.ZeroMean.resids.params (Python parameter) — Model parameters"><span class="n"><span class="pre">params</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ZeroMean.resids.y" title="arch.univariate.ZeroMean.resids.y (Python parameter) — Alternative values to use when computing model residuals"><span class="n"><span class="pre">y</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ZeroMean.resids.regressors" title="arch.univariate.ZeroMean.resids.regressors (Python parameter) — Alternative regressor values to use when computing model residuals"><span class="n"><span class="pre">regressors</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/mean.html#ZeroMean.resids"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.ZeroMean.resids" title="Link to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">ZeroMean.</span></span><span class="sig-name descname"><span class="pre">resids</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ZeroMean.resids.params" title="arch.univariate.ZeroMean.resids.params (Python parameter) — Model parameters"><span class="n"><span class="pre">params</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ZeroMean.resids.y" title="arch.univariate.ZeroMean.resids.y (Python parameter) — Alternative values to use when computing model residuals"><span class="n"><span class="pre">y</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ZeroMean.resids.regressors" title="arch.univariate.ZeroMean.resids.regressors (Python parameter) — Alternative regressor values to use when computing model residuals"><span class="n"><span class="pre">regressors</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/mean.html#ZeroMean.resids"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.ZeroMean.resids" title="Link to this definition">¶</a></dt>
 <dd><p>Compute model residuals</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.ZeroMean.resids-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.ZeroMean.resids-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
 <dt id="arch.univariate.ZeroMean.resids.params"><span class="n sig-name">params</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.ZeroMean.resids.params" title="Permalink to this definition">¶</a></dt><dd><p>Model parameters</p>
 </dd>
-<dt id="arch.univariate.ZeroMean.resids.y"><span class="n sig-name">y</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ZeroMean.resids.y" title="Permalink to this definition">¶</a></dt><dd><p>Alternative values to use when computing model residuals</p>
+<dt id="arch.univariate.ZeroMean.resids.y"><span class="n sig-name">y</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ZeroMean.resids.y" title="Permalink to this definition">¶</a></dt><dd><p>Alternative values to use when computing model residuals</p>
 </dd>
-<dt id="arch.univariate.ZeroMean.resids.regressors"><span class="n sig-name">regressors</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ZeroMean.resids.regressors" title="Permalink to this definition">¶</a></dt><dd><p>Alternative regressor values to use when computing model residuals</p>
+<dt id="arch.univariate.ZeroMean.resids.regressors"><span class="n sig-name">regressors</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ZeroMean.resids.regressors" title="Permalink to this definition">¶</a></dt><dd><p>Alternative regressor values to use when computing model residuals</p>
 </dd>
 </dl>
 </dd>
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diff --git a/devel/univariate/generated/generated/arch.univariate.ZeroMean.simulate.html b/devel/univariate/generated/generated/arch.univariate.ZeroMean.simulate.html
index 3c61ee189d..5570631766 100644
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@@ -1246,12 +1246,12 @@
 <h1 id="arch-univariate-zeromean-simulate">arch.univariate.ZeroMean.simulate<a class="headerlink" href="#arch-univariate-zeromean-simulate" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.ZeroMean.simulate">
-<span class="sig-prename descclassname"><span class="pre">ZeroMean.</span></span><span class="sig-name descname"><span class="pre">simulate</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ZeroMean.simulate.params" title="arch.univariate.ZeroMean.simulate.params (Python parameter) — Parameters to use when simulating the model.  Parameter order is [volatility distribution]. There are no mean parameters."><span class="n"><span class="pre">params</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ZeroMean.simulate.nobs" title="arch.univariate.ZeroMean.simulate.nobs (Python parameter) — Length of series to simulate"><span class="n"><span class="pre">nobs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ZeroMean.simulate.burn" title="arch.univariate.ZeroMean.simulate.burn (Python parameter) — Number of values to simulate to initialize the model and remove dependence on initial values."><span class="n"><span class="pre">burn</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">500</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ZeroMean.simulate.initial_value" title="arch.univariate.ZeroMean.simulate.initial_value (Python parameter) — This value is not used."><span class="n"><span class="pre">initial_value</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ZeroMean.simulate.x" title="arch.univariate.ZeroMean.simulate.x (Python parameter) — This value is not used."><span class="n"><span class="pre">x</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ZeroMean.simulate.initial_value_vol" title="arch.univariate.ZeroMean.simulate.initial_value_vol (Python parameter) — An array or scalar to use when initializing the volatility process."><span class="n"><span class="pre">initial_value_vol</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/mean.html#ZeroMean.simulate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.ZeroMean.simulate" title="Link to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">ZeroMean.</span></span><span class="sig-name descname"><span class="pre">simulate</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ZeroMean.simulate.params" title="arch.univariate.ZeroMean.simulate.params (Python parameter) — Parameters to use when simulating the model.  Parameter order is [volatility distribution]. There are no mean parameters."><span class="n"><span class="pre">params</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ZeroMean.simulate.nobs" title="arch.univariate.ZeroMean.simulate.nobs (Python parameter) — Length of series to simulate"><span class="n"><span class="pre">nobs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ZeroMean.simulate.burn" title="arch.univariate.ZeroMean.simulate.burn (Python parameter) — Number of values to simulate to initialize the model and remove dependence on initial values."><span class="n"><span class="pre">burn</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">500</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ZeroMean.simulate.initial_value" title="arch.univariate.ZeroMean.simulate.initial_value (Python parameter) — This value is not used."><span class="n"><span class="pre">initial_value</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ZeroMean.simulate.x" title="arch.univariate.ZeroMean.simulate.x (Python parameter) — This value is not used."><span class="n"><span class="pre">x</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.ZeroMean.simulate.initial_value_vol" title="arch.univariate.ZeroMean.simulate.initial_value_vol (Python parameter) — An array or scalar to use when initializing the volatility process."><span class="n"><span class="pre">initial_value_vol</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><span class="pre">DataFrame</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/mean.html#ZeroMean.simulate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.ZeroMean.simulate" title="Link to this definition">¶</a></dt>
 <dd><p>Simulated data from a zero mean model</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.ZeroMean.simulate-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.ZeroMean.simulate-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.ZeroMean.simulate.params"><span class="n sig-name">params</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="p">]</span></span></span><a class="headerlink" href="#arch.univariate.ZeroMean.simulate.params" title="Permalink to this definition">¶</a></dt><dd><p>Parameters to use when simulating the model.  Parameter order is
+<dt id="arch.univariate.ZeroMean.simulate.params"><span class="n sig-name">params</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="p">]</span></span></span><a class="headerlink" href="#arch.univariate.ZeroMean.simulate.params" title="Permalink to this definition">¶</a></dt><dd><p>Parameters to use when simulating the model.  Parameter order is
 [volatility distribution]. There are no mean parameters.</p>
 </dd>
 <dt id="arch.univariate.ZeroMean.simulate.nobs"><span class="n sig-name">nobs</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a></span></span><a class="headerlink" href="#arch.univariate.ZeroMean.simulate.nobs" title="Permalink to this definition">¶</a></dt><dd><p>Length of series to simulate</p>
@@ -1261,7 +1261,7 @@ <h1 id="arch-univariate-zeromean-simulate">arch.univariate.ZeroMean.simulate<a c
 </dd>
 <dt id="arch.univariate.ZeroMean.simulate.initial_value"><span class="n sig-name">initial_value</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ZeroMean.simulate.initial_value" title="Permalink to this definition">¶</a></dt><dd><p>This value is not used.</p>
 </dd>
-<dt id="arch.univariate.ZeroMean.simulate.x"><span class="n sig-name">x</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ZeroMean.simulate.x" title="Permalink to this definition">¶</a></dt><dd><p>This value is not used.</p>
+<dt id="arch.univariate.ZeroMean.simulate.x"><span class="n sig-name">x</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ZeroMean.simulate.x" title="Permalink to this definition">¶</a></dt><dd><p>This value is not used.</p>
 </dd>
 <dt id="arch.univariate.ZeroMean.simulate.initial_value_vol"><span class="n sig-name">initial_value_vol</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.ZeroMean.simulate.initial_value_vol" title="Permalink to this definition">¶</a></dt><dd><p>An array or scalar to use when initializing the volatility process.</p>
 </dd>
@@ -1273,7 +1273,7 @@ <h1 id="arch-univariate-zeromean-simulate">arch.univariate.ZeroMean.simulate<a c
 containing the errors used in the simulation</p>
 </dd>
 <dt class="field-odd" id="arch.univariate.ZeroMean.simulate-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.ZeroMean.simulate-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><code class="xref py py-class docutils literal notranslate"><span class="pre">pandas.DataFrame</span></code></a></p>
+<dd class="field-odd"><p><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><code class="xref py py-class docutils literal notranslate"><span class="pre">pandas.DataFrame</span></code></a></p>
 </dd>
 </dl>
 <p class="example rubric">Examples</p>
@@ -1407,7 +1407,7 @@ <h1 id="arch-univariate-zeromean-simulate">arch.univariate.ZeroMean.simulate<a c
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
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   </body>
diff --git a/devel/univariate/generated/generated/arch.univariate.ZeroMean.starting_values.html b/devel/univariate/generated/generated/arch.univariate.ZeroMean.starting_values.html
index 8535f661b6..7c72837de6 100644
--- a/devel/univariate/generated/generated/arch.univariate.ZeroMean.starting_values.html
+++ b/devel/univariate/generated/generated/arch.univariate.ZeroMean.starting_values.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ZeroMean.starting_values - arch v6.2.1 (+21)</title>
+        <title>arch.univariate.ZeroMean.starting_values - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
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@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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@@ -1263,7 +1263,7 @@ <h1 id="arch-univariate-zeromean-starting-values">arch.univariate.ZeroMean.start
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+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
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diff --git a/devel/univariate/generated/generated/arch.univariate.ZeroMean.volatility.html b/devel/univariate/generated/generated/arch.univariate.ZeroMean.volatility.html
index 0a7c69381a..4dd8a88e63 100644
--- a/devel/univariate/generated/generated/arch.univariate.ZeroMean.volatility.html
+++ b/devel/univariate/generated/generated/arch.univariate.ZeroMean.volatility.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ZeroMean.volatility - arch v6.2.1 (+21)</title>
+        <title>arch.univariate.ZeroMean.volatility - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
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   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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     <div class="md-nav__source">
@@ -1215,7 +1215,7 @@ <h1 id="arch-univariate-zeromean-volatility">arch.univariate.ZeroMean.volatility
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-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
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   </body>
diff --git a/devel/univariate/generated/generated/arch.univariate.ZeroMean.x.html b/devel/univariate/generated/generated/arch.univariate.ZeroMean.x.html
index 8fa9dac942..3019d70b00 100644
--- a/devel/univariate/generated/generated/arch.univariate.ZeroMean.x.html
+++ b/devel/univariate/generated/generated/arch.univariate.ZeroMean.x.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ZeroMean.x - arch v6.2.1 (+21)</title>
+        <title>arch.univariate.ZeroMean.x - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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     <div class="md-nav__source">
@@ -1096,7 +1096,7 @@
 <h1 id="arch-univariate-zeromean-x">arch.univariate.ZeroMean.x<a class="headerlink" href="#arch-univariate-zeromean-x" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.ZeroMean.x">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ZeroMean.</span></span><span class="sig-name descname"><span class="pre">x</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span><a class="headerlink" href="#arch.univariate.ZeroMean.x" title="Link to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ZeroMean.</span></span><span class="sig-name descname"><span class="pre">x</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span><a class="headerlink" href="#arch.univariate.ZeroMean.x" title="Link to this definition">¶</a></dt>
 <dd><p>Gets the value of the exogenous regressors in the model</p>
 </dd></dl>
 
@@ -1214,7 +1214,7 @@ <h1 id="arch-univariate-zeromean-x">arch.univariate.ZeroMean.x<a class="headerli
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
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   </body>
diff --git a/devel/univariate/generated/generated/arch.univariate.ZeroMean.y.html b/devel/univariate/generated/generated/arch.univariate.ZeroMean.y.html
index 65dd798663..769508bd77 100644
--- a/devel/univariate/generated/generated/arch.univariate.ZeroMean.y.html
+++ b/devel/univariate/generated/generated/arch.univariate.ZeroMean.y.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.ZeroMean.y - arch v6.2.1 (+21)</title>
+        <title>arch.univariate.ZeroMean.y - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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     <div class="md-nav__source">
@@ -1096,7 +1096,7 @@
 <h1 id="arch-univariate-zeromean-y">arch.univariate.ZeroMean.y<a class="headerlink" href="#arch-univariate-zeromean-y" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.ZeroMean.y">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ZeroMean.</span></span><span class="sig-name descname"><span class="pre">y</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span><a class="headerlink" href="#arch.univariate.ZeroMean.y" title="Link to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ZeroMean.</span></span><span class="sig-name descname"><span class="pre">y</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span><a class="headerlink" href="#arch.univariate.ZeroMean.y" title="Link to this definition">¶</a></dt>
 <dd><p>Returns the dependent variable</p>
 </dd></dl>
 
@@ -1214,7 +1214,7 @@ <h1 id="arch-univariate-zeromean-y">arch.univariate.ZeroMean.y<a class="headerli
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index 978cad51aa..f79f3eff75 100644
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@@ -189,10 +189,10 @@
 
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@@ -1327,7 +1327,7 @@
 <h1 id="arch-univariate-base-archmodel-fit">arch.univariate.base.ARCHModel.fit<a class="headerlink" href="#arch-univariate-base-archmodel-fit" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.base.ARCHModel.fit">
-<span class="sig-prename descclassname"><span class="pre">ARCHModel.</span></span><span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModel.fit.update_freq" title="arch.univariate.base.ARCHModel.fit.update_freq (Python parameter) — Frequency of iteration updates.  Output is generated every update_freq iterations. Set to 0 to disable iterative output."><span class="n"><span class="pre">update_freq</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModel.fit.disp" title="arch.univariate.base.ARCHModel.fit.disp (Python parameter) — Either 'final' to print optimization result or 'off' to display nothing. If using a boolean, False is &quot;off&quot; and True is &quot;final&quot;"><span class="n"><span class="pre">disp</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'off'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'final'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;final&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModel.fit.starting_values" title="arch.univariate.base.ARCHModel.fit.starting_values (Python parameter) — Array of starting values to use.  If not provided, starting values are constructed by the model components."><span class="n"><span class="pre">starting_values</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModel.fit.cov_type" title="arch.univariate.base.ARCHModel.fit.cov_type (Python parameter) — Estimation method of parameter covariance.  Supported options are 'robust', which does not assume the Information Matrix Equality holds and 'classic' which does.  In the ARCH literature, 'robust' corresponds to Bollerslev-Wooldridge covariance estimator."><span class="n"><span class="pre">cov_type</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'robust'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'classic'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;robust&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModel.fit.show_warning" title="arch.univariate.base.ARCHModel.fit.show_warning (Python parameter) — Flag indicating whether convergence warnings should be shown."><span class="n"><span class="pre">show_warning</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModel.fit.first_obs" title="arch.univariate.base.ARCHModel.fit.first_obs (Python parameter) — First observation to use when estimating model"><span class="n"><span class="pre">first_obs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.12)"><span class="pre">datetime</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)"><span class="pre">datetime64</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.1)"><span class="pre">Timestamp</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModel.fit.last_obs" title="arch.univariate.base.ARCHModel.fit.last_obs (Python parameter) — Last observation to use when estimating model"><span class="n"><span class="pre">last_obs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.12)"><span class="pre">datetime</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)"><span class="pre">datetime64</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.1)"><span class="pre">Timestamp</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModel.fit.tol" title="arch.univariate.base.ARCHModel.fit.tol (Python parameter) — Tolerance for termination."><span class="n"><span class="pre">tol</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModel.fit.options" title="arch.univariate.base.ARCHModel.fit.options (Python parameter) — Options to pass to scipy.optimize.minimize.  Valid entries include 'ftol', 'eps', 'disp', and 'maxiter'."><span class="n"><span class="pre">options</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.12)"><span class="pre">Any</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModel.fit.backcast" title="arch.univariate.base.ARCHModel.fit.backcast (Python parameter) — Value to use as backcast. Should be measure \sigma^2_0 since model-specific non-linear transformations are applied to value before computing the variance recursions."><span class="n"><span class="pre">backcast</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference internal" href="../arch.univariate.base.ARCHModelResult.html#arch.univariate.base.ARCHModelResult" title="arch.univariate.base.ARCHModelResult (Python class) — Results from estimation of an ARCHModel model"><span class="pre">ARCHModelResult</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/base.html#ARCHModel.fit"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.base.ARCHModel.fit" title="Link to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">ARCHModel.</span></span><span class="sig-name descname"><span class="pre">fit</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModel.fit.update_freq" title="arch.univariate.base.ARCHModel.fit.update_freq (Python parameter) — Frequency of iteration updates.  Output is generated every update_freq iterations. Set to 0 to disable iterative output."><span class="n"><span class="pre">update_freq</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModel.fit.disp" title="arch.univariate.base.ARCHModel.fit.disp (Python parameter) — Either 'final' to print optimization result or 'off' to display nothing. If using a boolean, False is &quot;off&quot; and True is &quot;final&quot;"><span class="n"><span class="pre">disp</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'off'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'final'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;final&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModel.fit.starting_values" title="arch.univariate.base.ARCHModel.fit.starting_values (Python parameter) — Array of starting values to use.  If not provided, starting values are constructed by the model components."><span class="n"><span class="pre">starting_values</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModel.fit.cov_type" title="arch.univariate.base.ARCHModel.fit.cov_type (Python parameter) — Estimation method of parameter covariance.  Supported options are 'robust', which does not assume the Information Matrix Equality holds and 'classic' which does.  In the ARCH literature, 'robust' corresponds to Bollerslev-Wooldridge covariance estimator."><span class="n"><span class="pre">cov_type</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'robust'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'classic'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;robust&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModel.fit.show_warning" title="arch.univariate.base.ARCHModel.fit.show_warning (Python parameter) — Flag indicating whether convergence warnings should be shown."><span class="n"><span class="pre">show_warning</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModel.fit.first_obs" title="arch.univariate.base.ARCHModel.fit.first_obs (Python parameter) — First observation to use when estimating model"><span class="n"><span class="pre">first_obs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.12)"><span class="pre">datetime</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)"><span class="pre">datetime64</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.4)"><span class="pre">Timestamp</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModel.fit.last_obs" title="arch.univariate.base.ARCHModel.fit.last_obs (Python parameter) — Last observation to use when estimating model"><span class="n"><span class="pre">last_obs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.12)"><span class="pre">datetime</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)"><span class="pre">datetime64</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.4)"><span class="pre">Timestamp</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModel.fit.tol" title="arch.univariate.base.ARCHModel.fit.tol (Python parameter) — Tolerance for termination."><span class="n"><span class="pre">tol</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModel.fit.options" title="arch.univariate.base.ARCHModel.fit.options (Python parameter) — Options to pass to scipy.optimize.minimize.  Valid entries include 'ftol', 'eps', 'disp', and 'maxiter'."><span class="n"><span class="pre">options</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.12)"><span class="pre">Any</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModel.fit.backcast" title="arch.univariate.base.ARCHModel.fit.backcast (Python parameter) — Value to use as backcast. Should be measure \sigma^2_0 since model-specific non-linear transformations are applied to value before computing the variance recursions."><span class="n"><span class="pre">backcast</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference internal" href="../arch.univariate.base.ARCHModelResult.html#arch.univariate.base.ARCHModelResult" title="arch.univariate.base.ARCHModelResult (Python class) — Results from estimation of an ARCHModel model"><span class="pre">ARCHModelResult</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/base.html#ARCHModel.fit"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.base.ARCHModel.fit" title="Link to this definition">¶</a></dt>
 <dd><p>Estimate model parameters</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.base.ARCHModel.fit-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.base.ARCHModel.fit-parameters" title="Permalink to this headline">¶</a></dt>
@@ -1338,7 +1338,7 @@ <h1 id="arch-univariate-base-archmodel-fit">arch.univariate.base.ARCHModel.fit<a
 <dt id="arch.univariate.base.ARCHModel.fit.disp"><span class="n sig-name">disp</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)">bool</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'off'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'final'</span></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;final&#39;</span></code><a class="headerlink" href="#arch.univariate.base.ARCHModel.fit.disp" title="Permalink to this definition">¶</a></dt><dd><p>Either ‘final’ to print optimization result or ‘off’ to display
 nothing. If using a boolean, False is “off” and True is “final”</p>
 </dd>
-<dt id="arch.univariate.base.ARCHModel.fit.starting_values"><span class="n sig-name">starting_values</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.base.ARCHModel.fit.starting_values" title="Permalink to this definition">¶</a></dt><dd><p>Array of starting values to use.  If not provided, starting values
+<dt id="arch.univariate.base.ARCHModel.fit.starting_values"><span class="n sig-name">starting_values</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.base.ARCHModel.fit.starting_values" title="Permalink to this definition">¶</a></dt><dd><p>Array of starting values to use.  If not provided, starting values
 are constructed by the model components.</p>
 </dd>
 <dt id="arch.univariate.base.ARCHModel.fit.cov_type"><span class="n sig-name">cov_type</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><span class="s">'robust'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'classic'</span></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;robust&#39;</span></code><a class="headerlink" href="#arch.univariate.base.ARCHModel.fit.cov_type" title="Permalink to this definition">¶</a></dt><dd><p>Estimation method of parameter covariance.  Supported options are
@@ -1348,9 +1348,9 @@ <h1 id="arch-univariate-base-archmodel-fit">arch.univariate.base.ARCHModel.fit<a
 </dd>
 <dt id="arch.univariate.base.ARCHModel.fit.show_warning"><span class="n sig-name">show_warning</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)">bool</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">True</span></code><a class="headerlink" href="#arch.univariate.base.ARCHModel.fit.show_warning" title="Permalink to this definition">¶</a></dt><dd><p>Flag indicating whether convergence warnings should be shown.</p>
 </dd>
-<dt id="arch.univariate.base.ARCHModel.fit.first_obs"><span class="n sig-name">first_obs</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)">str</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.12)">datetime</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)">datetime64</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.1)">Timestamp</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.base.ARCHModel.fit.first_obs" title="Permalink to this definition">¶</a></dt><dd><p>First observation to use when estimating model</p>
+<dt id="arch.univariate.base.ARCHModel.fit.first_obs"><span class="n sig-name">first_obs</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)">str</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.12)">datetime</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)">datetime64</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.4)">Timestamp</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.base.ARCHModel.fit.first_obs" title="Permalink to this definition">¶</a></dt><dd><p>First observation to use when estimating model</p>
 </dd>
-<dt id="arch.univariate.base.ARCHModel.fit.last_obs"><span class="n sig-name">last_obs</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)">str</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.12)">datetime</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)">datetime64</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.1)">Timestamp</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.base.ARCHModel.fit.last_obs" title="Permalink to this definition">¶</a></dt><dd><p>Last observation to use when estimating model</p>
+<dt id="arch.univariate.base.ARCHModel.fit.last_obs"><span class="n sig-name">last_obs</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)">str</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.12)">datetime</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)">datetime64</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.4)">Timestamp</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.base.ARCHModel.fit.last_obs" title="Permalink to this definition">¶</a></dt><dd><p>Last observation to use when estimating model</p>
 </dd>
 <dt id="arch.univariate.base.ARCHModel.fit.tol"><span class="n sig-name">tol</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.base.ARCHModel.fit.tol" title="Permalink to this definition">¶</a></dt><dd><p>Tolerance for termination.</p>
 </dd>
@@ -1490,7 +1490,7 @@ <h1 id="arch-univariate-base-archmodel-fit">arch.univariate.base.ARCHModel.fit<a
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diff --git a/devel/univariate/generated/generated/arch.univariate.base.ARCHModel.fix.html b/devel/univariate/generated/generated/arch.univariate.base.ARCHModel.fix.html
index 94d5fa19bc..88adb2131a 100644
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+++ b/devel/univariate/generated/generated/arch.univariate.base.ARCHModel.fix.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.base.ARCHModel.fix - arch v6.2.1 (+21)</title>
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@@ -47,7 +47,7 @@
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@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -1229,18 +1229,18 @@
 <h1 id="arch-univariate-base-archmodel-fix">arch.univariate.base.ARCHModel.fix<a class="headerlink" href="#arch-univariate-base-archmodel-fix" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.base.ARCHModel.fix">
-<span class="sig-prename descclassname"><span class="pre">ARCHModel.</span></span><span class="sig-name descname"><span class="pre">fix</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModel.fix.params" title="arch.univariate.base.ARCHModel.fix.params (Python parameter) — User specified parameters to use when generating the result. Must have the correct number of parameters for a given choice of mean model, volatility model and distribution."><span class="n"><span class="pre">params</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModel.fix.first_obs" title="arch.univariate.base.ARCHModel.fix.first_obs (Python parameter) — First observation to use when fixing model"><span class="n"><span class="pre">first_obs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.12)"><span class="pre">datetime</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)"><span class="pre">datetime64</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.1)"><span class="pre">Timestamp</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModel.fix.last_obs" title="arch.univariate.base.ARCHModel.fix.last_obs (Python parameter) — Last observation to use when fixing model"><span class="n"><span class="pre">last_obs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.12)"><span class="pre">datetime</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)"><span class="pre">datetime64</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.1)"><span class="pre">Timestamp</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference internal" href="../arch.univariate.base.ARCHModelFixedResult.html#arch.univariate.base.ARCHModelFixedResult" title="arch.univariate.base.ARCHModelFixedResult (Python class) — Results for fixed parameters for an ARCHModel model"><span class="pre">ARCHModelFixedResult</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/base.html#ARCHModel.fix"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.base.ARCHModel.fix" title="Link to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">ARCHModel.</span></span><span class="sig-name descname"><span class="pre">fix</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModel.fix.params" title="arch.univariate.base.ARCHModel.fix.params (Python parameter) — User specified parameters to use when generating the result. Must have the correct number of parameters for a given choice of mean model, volatility model and distribution."><span class="n"><span class="pre">params</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModel.fix.first_obs" title="arch.univariate.base.ARCHModel.fix.first_obs (Python parameter) — First observation to use when fixing model"><span class="n"><span class="pre">first_obs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.12)"><span class="pre">datetime</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)"><span class="pre">datetime64</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.4)"><span class="pre">Timestamp</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModel.fix.last_obs" title="arch.univariate.base.ARCHModel.fix.last_obs (Python parameter) — Last observation to use when fixing model"><span class="n"><span class="pre">last_obs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.12)"><span class="pre">datetime</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)"><span class="pre">datetime64</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.4)"><span class="pre">Timestamp</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference internal" href="../arch.univariate.base.ARCHModelFixedResult.html#arch.univariate.base.ARCHModelFixedResult" title="arch.univariate.base.ARCHModelFixedResult (Python class) — Results for fixed parameters for an ARCHModel model"><span class="pre">ARCHModelFixedResult</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/base.html#ARCHModel.fix"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.base.ARCHModel.fix" title="Link to this definition">¶</a></dt>
 <dd><p>Allows an ARCHModelFixedResult to be constructed from fixed parameters.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.base.ARCHModel.fix-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.base.ARCHModel.fix-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.base.ARCHModel.fix.params"><span class="n sig-name">params</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.univariate.base.ARCHModel.fix.params" title="Permalink to this definition">¶</a></dt><dd><p>User specified parameters to use when generating the result. Must
+<dt id="arch.univariate.base.ARCHModel.fix.params"><span class="n sig-name">params</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a></span></span><a class="headerlink" href="#arch.univariate.base.ARCHModel.fix.params" title="Permalink to this definition">¶</a></dt><dd><p>User specified parameters to use when generating the result. Must
 have the correct number of parameters for a given choice of mean
 model, volatility model and distribution.</p>
 </dd>
-<dt id="arch.univariate.base.ARCHModel.fix.first_obs"><span class="n sig-name">first_obs</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)">str</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.12)">datetime</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)">datetime64</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.1)">Timestamp</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.base.ARCHModel.fix.first_obs" title="Permalink to this definition">¶</a></dt><dd><p>First observation to use when fixing model</p>
+<dt id="arch.univariate.base.ARCHModel.fix.first_obs"><span class="n sig-name">first_obs</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)">str</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.12)">datetime</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)">datetime64</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.4)">Timestamp</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.base.ARCHModel.fix.first_obs" title="Permalink to this definition">¶</a></dt><dd><p>First observation to use when fixing model</p>
 </dd>
-<dt id="arch.univariate.base.ARCHModel.fix.last_obs"><span class="n sig-name">last_obs</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)">str</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.12)">datetime</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)">datetime64</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.1)">Timestamp</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.base.ARCHModel.fix.last_obs" title="Permalink to this definition">¶</a></dt><dd><p>Last observation to use when fixing model</p>
+<dt id="arch.univariate.base.ARCHModel.fix.last_obs"><span class="n sig-name">last_obs</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)">str</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.12)">datetime</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)">datetime64</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.4)">Timestamp</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.base.ARCHModel.fix.last_obs" title="Permalink to this definition">¶</a></dt><dd><p>Last observation to use when fixing model</p>
 </dd>
 </dl>
 </dd>
@@ -1369,7 +1369,7 @@ <h1 id="arch-univariate-base-archmodel-fix">arch.univariate.base.ARCHModel.fix<a
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 <h1 id="arch-univariate-base-archmodel-forecast">arch.univariate.base.ARCHModel.forecast<a class="headerlink" href="#arch-univariate-base-archmodel-forecast" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.base.ARCHModel.forecast">
-<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARCHModel.</span></span><span class="sig-name descname"><span class="pre">forecast</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModel.forecast.params" title="arch.univariate.base.ARCHModel.forecast.params (Python parameter) — Parameters required to forecast. Must be identical in shape to the parameters computed by fitting the model."><span class="n"><span class="pre">params</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModel.forecast.horizon" title="arch.univariate.base.ARCHModel.forecast.horizon (Python parameter) — Number of steps to forecast"><span class="n"><span class="pre">horizon</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModel.forecast.start" title="arch.univariate.base.ARCHModel.forecast.start (Python parameter) — An integer, datetime or str indicating the first observation to produce the forecast for.  Datetimes can only be used with pandas inputs that have a datetime index. Strings must be convertible to a date time, such as in '1945-01-01'."><span class="n"><span class="pre">start</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.12)"><span class="pre">datetime</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)"><span class="pre">datetime64</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.1)"><span class="pre">Timestamp</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModel.forecast.align" title="arch.univariate.base.ARCHModel.forecast.align (Python parameter) — Either 'origin' or 'target'.  When set of 'origin', the t-th row of forecasts contains the forecasts for t+1, t+2, ..., t+h. When set to 'target', the t-th row contains the 1-step ahead forecast from time t-1, the 2 step from time t-2, ..., and the h-step from time t-h.  'target' simplified computing forecast errors since the realization and h-step forecast are aligned."><span class="n"><span class="pre">align</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'origin'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'target'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;origin&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModel.forecast.method" title="arch.univariate.base.ARCHModel.forecast.method (Python parameter) — Method to use when producing the forecast. The default is analytic. The method only affects the variance forecast generation.  Not all volatility models support all methods. In particular, volatility models that do not evolve in squares such as EGARCH or TARCH do not support the 'analytic' method for horizons &gt; 1."><span class="n"><span class="pre">method</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'analytic'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'simulation'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'bootstrap'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;analytic&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModel.forecast.simulations" title="arch.univariate.base.ARCHModel.forecast.simulations (Python parameter) — Number of simulations to run when computing the forecast using either simulation or bootstrap."><span class="n"><span class="pre">simulations</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModel.forecast.rng" title="arch.univariate.base.ARCHModel.forecast.rng (Python parameter) — Custom random number generator to use in simulation-based forecasts. Must produce random samples using the syntax rng(size) where size the 2-element tuple (simulations, horizon)."><span class="n"><span class="pre">rng</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.12)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModel.forecast.random_state" title="arch.univariate.base.ARCHModel.forecast.random_state (Python parameter) — NumPy RandomState instance to use when method is 'bootstrap'"><span class="n"><span class="pre">random_state</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.26)"><span class="pre">RandomState</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModel.forecast.reindex" title="arch.univariate.base.ARCHModel.forecast.reindex (Python parameter) — Whether to reindex the forecasts to have the same dimension as the series being forecast. Prior to 4.18 this was the default. As of 4.19 this is now optional. If not provided, a warning is raised about the future change in the default which will occur after September 2021."><span class="n"><span class="pre">reindex</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModel.forecast.x" title="arch.univariate.base.ARCHModel.forecast.x (Python parameter) — Values to use for exogenous regressors if any are included in the model. Three formats are accepted:"><span class="n"><span class="pre">x</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Hashable" title="(in Python v3.12)"><span class="pre">Hashable</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference internal" href="../arch.univariate.base.ARCHModelForecast.html#arch.univariate.base.ARCHModelForecast" title="arch.univariate.base.ARCHModelForecast (Python class) — Container for forecasts from an ARCH Model"><span class="pre">ARCHModelForecast</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/base.html#ARCHModel.forecast"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.base.ARCHModel.forecast" title="Link to this definition">¶</a></dt>
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARCHModel.</span></span><span class="sig-name descname"><span class="pre">forecast</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModel.forecast.params" title="arch.univariate.base.ARCHModel.forecast.params (Python parameter) — Parameters required to forecast. Must be identical in shape to the parameters computed by fitting the model."><span class="n"><span class="pre">params</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModel.forecast.horizon" title="arch.univariate.base.ARCHModel.forecast.horizon (Python parameter) — Number of steps to forecast"><span class="n"><span class="pre">horizon</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModel.forecast.start" title="arch.univariate.base.ARCHModel.forecast.start (Python parameter) — An integer, datetime or str indicating the first observation to produce the forecast for.  Datetimes can only be used with pandas inputs that have a datetime index. Strings must be convertible to a date time, such as in '1945-01-01'."><span class="n"><span class="pre">start</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.12)"><span class="pre">datetime</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)"><span class="pre">datetime64</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.4)"><span class="pre">Timestamp</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModel.forecast.align" title="arch.univariate.base.ARCHModel.forecast.align (Python parameter) — Either 'origin' or 'target'.  When set of 'origin', the t-th row of forecasts contains the forecasts for t+1, t+2, ..., t+h. When set to 'target', the t-th row contains the 1-step ahead forecast from time t-1, the 2 step from time t-2, ..., and the h-step from time t-h.  'target' simplified computing forecast errors since the realization and h-step forecast are aligned."><span class="n"><span class="pre">align</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'origin'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'target'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;origin&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModel.forecast.method" title="arch.univariate.base.ARCHModel.forecast.method (Python parameter) — Method to use when producing the forecast. The default is analytic. The method only affects the variance forecast generation.  Not all volatility models support all methods. In particular, volatility models that do not evolve in squares such as EGARCH or TARCH do not support the 'analytic' method for horizons &gt; 1."><span class="n"><span class="pre">method</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'analytic'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'simulation'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'bootstrap'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;analytic&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModel.forecast.simulations" title="arch.univariate.base.ARCHModel.forecast.simulations (Python parameter) — Number of simulations to run when computing the forecast using either simulation or bootstrap."><span class="n"><span class="pre">simulations</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModel.forecast.rng" title="arch.univariate.base.ARCHModel.forecast.rng (Python parameter) — Custom random number generator to use in simulation-based forecasts. Must produce random samples using the syntax rng(size) where size the 2-element tuple (simulations, horizon)."><span class="n"><span class="pre">rng</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.12)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModel.forecast.random_state" title="arch.univariate.base.ARCHModel.forecast.random_state (Python parameter) — NumPy RandomState instance to use when method is 'bootstrap'"><span class="n"><span class="pre">random_state</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.26)"><span class="pre">RandomState</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModel.forecast.reindex" title="arch.univariate.base.ARCHModel.forecast.reindex (Python parameter) — Whether to reindex the forecasts to have the same dimension as the series being forecast. Prior to 4.18 this was the default. As of 4.19 this is now optional. If not provided, a warning is raised about the future change in the default which will occur after September 2021."><span class="n"><span class="pre">reindex</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModel.forecast.x" title="arch.univariate.base.ARCHModel.forecast.x (Python parameter) — Values to use for exogenous regressors if any are included in the model. Three formats are accepted:"><span class="n"><span class="pre">x</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Hashable" title="(in Python v3.12)"><span class="pre">Hashable</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference internal" href="../arch.univariate.base.ARCHModelForecast.html#arch.univariate.base.ARCHModelForecast" title="arch.univariate.base.ARCHModelForecast (Python class) — Container for forecasts from an ARCH Model"><span class="pre">ARCHModelForecast</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/base.html#ARCHModel.forecast"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.base.ARCHModel.forecast" title="Link to this definition">¶</a></dt>
 <dd><p>Construct forecasts from estimated model</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.base.ARCHModel.forecast-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.base.ARCHModel.forecast-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.base.ARCHModel.forecast.params"><span class="n sig-name">params</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.univariate.base.ARCHModel.forecast.params" title="Permalink to this definition">¶</a></dt><dd><p>Parameters required to forecast. Must be identical in
+<dt id="arch.univariate.base.ARCHModel.forecast.params"><span class="n sig-name">params</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a></span></span><a class="headerlink" href="#arch.univariate.base.ARCHModel.forecast.params" title="Permalink to this definition">¶</a></dt><dd><p>Parameters required to forecast. Must be identical in
 shape to the parameters computed by fitting the model.</p>
 </dd>
 <dt id="arch.univariate.base.ARCHModel.forecast.horizon"><span class="n sig-name">horizon</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code><a class="headerlink" href="#arch.univariate.base.ARCHModel.forecast.horizon" title="Permalink to this definition">¶</a></dt><dd><p>Number of steps to forecast</p>
 </dd>
-<dt id="arch.univariate.base.ARCHModel.forecast.start"><span class="n sig-name">start</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)">str</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.12)">datetime</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)">datetime64</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.1)">Timestamp</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.base.ARCHModel.forecast.start" title="Permalink to this definition">¶</a></dt><dd><p>An integer, datetime or str indicating the first observation to
+<dt id="arch.univariate.base.ARCHModel.forecast.start"><span class="n sig-name">start</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)">str</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.12)">datetime</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)">datetime64</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.4)">Timestamp</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.base.ARCHModel.forecast.start" title="Permalink to this definition">¶</a></dt><dd><p>An integer, datetime or str indicating the first observation to
 produce the forecast for.  Datetimes can only be used with pandas
 inputs that have a datetime index. Strings must be convertible
 to a date time, such as in ‘1945-01-01’.</p>
@@ -1373,7 +1373,7 @@ <h1 id="arch-univariate-base-archmodel-forecast">arch.univariate.base.ARCHModel.
 </div>
 </p>
 </dd>
-<dt id="arch.univariate.base.ARCHModel.forecast.x"><span class="n sig-name">x</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)">dict</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Hashable" title="(in Python v3.12)">Hashable</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.base.ARCHModel.forecast.x" title="Permalink to this definition">¶</a></dt><dd><p><p>Values to use for exogenous regressors if any are included in the
+<dt id="arch.univariate.base.ARCHModel.forecast.x"><span class="n sig-name">x</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)">dict</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Hashable" title="(in Python v3.12)">Hashable</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.base.ARCHModel.forecast.x" title="Permalink to this definition">¶</a></dt><dd><p><p>Values to use for exogenous regressors if any are included in the
 model. Three formats are accepted:</p>
 <ul class="simple">
 <li><p>2-d array-like: This format can be used when there is a single
@@ -1555,7 +1555,7 @@ <h1 id="arch-univariate-base-archmodel-forecast">arch.univariate.base.ARCHModel.
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diff --git a/devel/univariate/generated/generated/arch.univariate.base.ARCHModel.name.html b/devel/univariate/generated/generated/arch.univariate.base.ARCHModel.name.html
index 0014002f91..b125c04102 100644
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+++ b/devel/univariate/generated/generated/arch.univariate.base.ARCHModel.name.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.base.ARCHModel.name - arch v6.2.1 (+21)</title>
+        <title>arch.univariate.base.ARCHModel.name - arch v6.2.1 (+23)</title>
       
     
 
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+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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@@ -1239,7 +1239,7 @@ <h1 id="arch-univariate-base-archmodel-name">arch.univariate.base.ARCHModel.name
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-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
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diff --git a/devel/univariate/generated/generated/arch.univariate.base.ARCHModel.num_params.html b/devel/univariate/generated/generated/arch.univariate.base.ARCHModel.num_params.html
index 9973b77dfa..50e0b3ca14 100644
--- a/devel/univariate/generated/generated/arch.univariate.base.ARCHModel.num_params.html
+++ b/devel/univariate/generated/generated/arch.univariate.base.ARCHModel.num_params.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.base.ARCHModel.num_params - arch v6.2.1 (+21)</title>
+        <title>arch.univariate.base.ARCHModel.num_params - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
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+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
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@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
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@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
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-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
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   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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@@ -1239,7 +1239,7 @@ <h1 id="arch-univariate-base-archmodel-num-params">arch.univariate.base.ARCHMode
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-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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diff --git a/devel/univariate/generated/generated/arch.univariate.base.ARCHModel.parameter_names.html b/devel/univariate/generated/generated/arch.univariate.base.ARCHModel.parameter_names.html
index 4f1e47f677..b630028f6b 100644
--- a/devel/univariate/generated/generated/arch.univariate.base.ARCHModel.parameter_names.html
+++ b/devel/univariate/generated/generated/arch.univariate.base.ARCHModel.parameter_names.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.base.ARCHModel.parameter_names - arch v6.2.1 (+21)</title>
+        <title>arch.univariate.base.ARCHModel.parameter_names - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
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@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
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-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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@@ -1287,7 +1287,7 @@ <h1 id="arch-univariate-base-archmodel-parameter-names">arch.univariate.base.ARC
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-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
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diff --git a/devel/univariate/generated/generated/arch.univariate.base.ARCHModel.resids.html b/devel/univariate/generated/generated/arch.univariate.base.ARCHModel.resids.html
index 5dd7eab5ee..d19b39d61f 100644
--- a/devel/univariate/generated/generated/arch.univariate.base.ARCHModel.resids.html
+++ b/devel/univariate/generated/generated/arch.univariate.base.ARCHModel.resids.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.base.ARCHModel.resids - arch v6.2.1 (+21)</title>
+        <title>arch.univariate.base.ARCHModel.resids - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
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@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
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@@ -1229,16 +1229,16 @@
 <h1 id="arch-univariate-base-archmodel-resids">arch.univariate.base.ARCHModel.resids<a class="headerlink" href="#arch-univariate-base-archmodel-resids" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.base.ARCHModel.resids">
-<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARCHModel.</span></span><span class="sig-name descname"><span class="pre">resids</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModel.resids.params" title="arch.univariate.base.ARCHModel.resids.params (Python parameter) — Model parameters"><span class="n"><span class="pre">params</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModel.resids.y" title="arch.univariate.base.ARCHModel.resids.y (Python parameter) — Alternative values to use when computing model residuals"><span class="n"><span class="pre">y</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModel.resids.regressors" title="arch.univariate.base.ARCHModel.resids.regressors (Python parameter) — Alternative regressor values to use when computing model residuals"><span class="n"><span class="pre">regressors</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/base.html#ARCHModel.resids"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.base.ARCHModel.resids" title="Link to this definition">¶</a></dt>
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARCHModel.</span></span><span class="sig-name descname"><span class="pre">resids</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModel.resids.params" title="arch.univariate.base.ARCHModel.resids.params (Python parameter) — Model parameters"><span class="n"><span class="pre">params</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModel.resids.y" title="arch.univariate.base.ARCHModel.resids.y (Python parameter) — Alternative values to use when computing model residuals"><span class="n"><span class="pre">y</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModel.resids.regressors" title="arch.univariate.base.ARCHModel.resids.regressors (Python parameter) — Alternative regressor values to use when computing model residuals"><span class="n"><span class="pre">regressors</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/base.html#ARCHModel.resids"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.base.ARCHModel.resids" title="Link to this definition">¶</a></dt>
 <dd><p>Compute model residuals</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.base.ARCHModel.resids-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.base.ARCHModel.resids-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
 <dt id="arch.univariate.base.ARCHModel.resids.params"><span class="n sig-name">params</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.base.ARCHModel.resids.params" title="Permalink to this definition">¶</a></dt><dd><p>Model parameters</p>
 </dd>
-<dt id="arch.univariate.base.ARCHModel.resids.y"><span class="n sig-name">y</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.base.ARCHModel.resids.y" title="Permalink to this definition">¶</a></dt><dd><p>Alternative values to use when computing model residuals</p>
+<dt id="arch.univariate.base.ARCHModel.resids.y"><span class="n sig-name">y</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.base.ARCHModel.resids.y" title="Permalink to this definition">¶</a></dt><dd><p>Alternative values to use when computing model residuals</p>
 </dd>
-<dt id="arch.univariate.base.ARCHModel.resids.regressors"><span class="n sig-name">regressors</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.base.ARCHModel.resids.regressors" title="Permalink to this definition">¶</a></dt><dd><p>Alternative regressor values to use when computing model residuals</p>
+<dt id="arch.univariate.base.ARCHModel.resids.regressors"><span class="n sig-name">regressors</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.base.ARCHModel.resids.regressors" title="Permalink to this definition">¶</a></dt><dd><p>Alternative regressor values to use when computing model residuals</p>
 </dd>
 </dl>
 </dd>
@@ -1365,7 +1365,7 @@ <h1 id="arch-univariate-base-archmodel-resids">arch.univariate.base.ARCHModel.re
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diff --git a/devel/univariate/generated/generated/arch.univariate.base.ARCHModel.simulate.html b/devel/univariate/generated/generated/arch.univariate.base.ARCHModel.simulate.html
index 0e1d11a960..12dd0a859f 100644
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@@ -15,7 +15,7 @@
     
   
       
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+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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@@ -1121,7 +1121,7 @@
 <h1 id="arch-univariate-base-archmodel-simulate">arch.univariate.base.ARCHModel.simulate<a class="headerlink" href="#arch-univariate-base-archmodel-simulate" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.base.ARCHModel.simulate">
-<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARCHModel.</span></span><span class="sig-name descname"><span class="pre">simulate</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">params</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">nobs</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">burn</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">500</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">initial_value</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">x</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">initial_value_vol</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/base.html#ARCHModel.simulate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.base.ARCHModel.simulate" title="Link to this definition">¶</a></dt>
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARCHModel.</span></span><span class="sig-name descname"><span class="pre">simulate</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">params</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">nobs</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">burn</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">500</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">initial_value</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">x</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="n"><span class="pre">initial_value_vol</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><span class="pre">DataFrame</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/base.html#ARCHModel.simulate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.base.ARCHModel.simulate" title="Link to this definition">¶</a></dt>
 <dd></dd></dl>
 
 
@@ -1238,7 +1238,7 @@ <h1 id="arch-univariate-base-archmodel-simulate">arch.univariate.base.ARCHModel.
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diff --git a/devel/univariate/generated/generated/arch.univariate.base.ARCHModel.starting_values.html b/devel/univariate/generated/generated/arch.univariate.base.ARCHModel.starting_values.html
index 6443c87966..6429dcd4d6 100644
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+++ b/devel/univariate/generated/generated/arch.univariate.base.ARCHModel.starting_values.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.base.ARCHModel.starting_values - arch v6.2.1 (+21)</title>
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+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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@@ -189,10 +189,10 @@
 
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+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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@@ -1288,7 +1288,7 @@ <h1 id="arch-univariate-base-archmodel-starting-values">arch.univariate.base.ARC
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diff --git a/devel/univariate/generated/generated/arch.univariate.base.ARCHModel.volatility.html b/devel/univariate/generated/generated/arch.univariate.base.ARCHModel.volatility.html
index 9da7f39efe..6cbf3cd2b8 100644
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+++ b/devel/univariate/generated/generated/arch.univariate.base.ARCHModel.volatility.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.base.ARCHModel.volatility - arch v6.2.1 (+21)</title>
+        <title>arch.univariate.base.ARCHModel.volatility - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
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+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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+            arch v6.2.1 (+23)
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@@ -189,10 +189,10 @@
 
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+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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diff --git a/devel/univariate/generated/generated/arch.univariate.base.ARCHModel.y.html b/devel/univariate/generated/generated/arch.univariate.base.ARCHModel.y.html
index 08ce25bfea..39dc065dcb 100644
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+++ b/devel/univariate/generated/generated/arch.univariate.base.ARCHModel.y.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.base.ARCHModel.y - arch v6.2.1 (+21)</title>
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@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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@@ -1121,7 +1121,7 @@
 <h1 id="arch-univariate-base-archmodel-y">arch.univariate.base.ARCHModel.y<a class="headerlink" href="#arch-univariate-base-archmodel-y" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.base.ARCHModel.y">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARCHModel.</span></span><span class="sig-name descname"><span class="pre">y</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span><a class="headerlink" href="#arch.univariate.base.ARCHModel.y" title="Link to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARCHModel.</span></span><span class="sig-name descname"><span class="pre">y</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span><a class="headerlink" href="#arch.univariate.base.ARCHModel.y" title="Link to this definition">¶</a></dt>
 <dd><p>Returns the dependent variable</p>
 </dd></dl>
 
@@ -1239,7 +1239,7 @@ <h1 id="arch-univariate-base-archmodel-y">arch.univariate.base.ARCHModel.y<a cla
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
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+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
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diff --git a/devel/univariate/generated/generated/arch.univariate.base.ARCHModelFixedResult.aic.html b/devel/univariate/generated/generated/arch.univariate.base.ARCHModelFixedResult.aic.html
index 11d49048a1..dbf391039c 100644
--- a/devel/univariate/generated/generated/arch.univariate.base.ARCHModelFixedResult.aic.html
+++ b/devel/univariate/generated/generated/arch.univariate.base.ARCHModelFixedResult.aic.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.base.ARCHModelFixedResult.aic - arch v6.2.1 (+21)</title>
+        <title>arch.univariate.base.ARCHModelFixedResult.aic - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
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-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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     <div class="md-nav__source">
@@ -1125,7 +1125,7 @@ <h1 id="arch-univariate-base-archmodelfixedresult-aic">arch.univariate.base.ARCH
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
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   </body>
diff --git a/devel/univariate/generated/generated/arch.univariate.base.ARCHModelFixedResult.arch_lm_test.html b/devel/univariate/generated/generated/arch.univariate.base.ARCHModelFixedResult.arch_lm_test.html
index 16ade6588f..e2d9749ad3 100644
--- a/devel/univariate/generated/generated/arch.univariate.base.ARCHModelFixedResult.arch_lm_test.html
+++ b/devel/univariate/generated/generated/arch.univariate.base.ARCHModelFixedResult.arch_lm_test.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.base.ARCHModelFixedResult.arch_lm_test - arch v6.2.1 (+21)</title>
+        <title>arch.univariate.base.ARCHModelFixedResult.arch_lm_test - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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     <div class="md-nav__source">
@@ -1236,7 +1236,7 @@ <h1 id="arch-univariate-base-archmodelfixedresult-arch-lm-test">arch.univariate.
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-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
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diff --git a/devel/univariate/generated/generated/arch.univariate.base.ARCHModelFixedResult.bic.html b/devel/univariate/generated/generated/arch.univariate.base.ARCHModelFixedResult.bic.html
index 38bd89c501..410d79a1d4 100644
--- a/devel/univariate/generated/generated/arch.univariate.base.ARCHModelFixedResult.bic.html
+++ b/devel/univariate/generated/generated/arch.univariate.base.ARCHModelFixedResult.bic.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.base.ARCHModelFixedResult.bic - arch v6.2.1 (+21)</title>
+        <title>arch.univariate.base.ARCHModelFixedResult.bic - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -1125,7 +1125,7 @@ <h1 id="arch-univariate-base-archmodelfixedresult-bic">arch.univariate.base.ARCH
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
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diff --git a/devel/univariate/generated/generated/arch.univariate.base.ARCHModelFixedResult.conditional_volatility.html b/devel/univariate/generated/generated/arch.univariate.base.ARCHModelFixedResult.conditional_volatility.html
index de193dfdfa..299c2b2f30 100644
--- a/devel/univariate/generated/generated/arch.univariate.base.ARCHModelFixedResult.conditional_volatility.html
+++ b/devel/univariate/generated/generated/arch.univariate.base.ARCHModelFixedResult.conditional_volatility.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.base.ARCHModelFixedResult.conditional_volatility - arch v6.2.1 (+21)</title>
+        <title>arch.univariate.base.ARCHModelFixedResult.conditional_volatility - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
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@@ -92,7 +92,7 @@
 
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@@ -1046,7 +1046,7 @@
 <h1 id="arch-univariate-base-archmodelfixedresult-conditional-volatility">arch.univariate.base.ARCHModelFixedResult.conditional_volatility<a class="headerlink" href="#arch-univariate-base-archmodelfixedresult-conditional-volatility" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.base.ARCHModelFixedResult.conditional_volatility">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARCHModelFixedResult.</span></span><span class="sig-name descname"><span class="pre">conditional_volatility</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span><a class="headerlink" href="#arch.univariate.base.ARCHModelFixedResult.conditional_volatility" title="Link to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARCHModelFixedResult.</span></span><span class="sig-name descname"><span class="pre">conditional_volatility</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span><a class="headerlink" href="#arch.univariate.base.ARCHModelFixedResult.conditional_volatility" title="Link to this definition">¶</a></dt>
 <dd><p>Estimated conditional volatility</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.base.ARCHModelFixedResult.conditional_volatility-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.univariate.base.ARCHModelFixedResult.conditional_volatility-returns" title="Permalink to this headline">¶</a></dt>
@@ -1175,7 +1175,7 @@ <h1 id="arch-univariate-base-archmodelfixedresult-conditional-volatility">arch.u
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diff --git a/devel/univariate/generated/generated/arch.univariate.base.ARCHModelFixedResult.forecast.html b/devel/univariate/generated/generated/arch.univariate.base.ARCHModelFixedResult.forecast.html
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-        <title>arch.univariate.base.ARCHModelFixedResult.forecast - arch v6.2.1 (+21)</title>
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 <h1 id="arch-univariate-base-archmodelfixedresult-forecast">arch.univariate.base.ARCHModelFixedResult.forecast<a class="headerlink" href="#arch-univariate-base-archmodelfixedresult-forecast" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.base.ARCHModelFixedResult.forecast">
-<span class="sig-prename descclassname"><span class="pre">ARCHModelFixedResult.</span></span><span class="sig-name descname"><span class="pre">forecast</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelFixedResult.forecast.params" title="arch.univariate.base.ARCHModelFixedResult.forecast.params (Python parameter) — Alternative parameters to use.  If not provided, the parameters estimated when fitting the model are used.  Must be identical in shape to the parameters computed by fitting the model."><span class="n"><span class="pre">params</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelFixedResult.forecast.horizon" title="arch.univariate.base.ARCHModelFixedResult.forecast.horizon (Python parameter) — Number of steps to forecast"><span class="n"><span class="pre">horizon</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelFixedResult.forecast.start" title="arch.univariate.base.ARCHModelFixedResult.forecast.start (Python parameter) — An integer, datetime or str indicating the first observation to produce the forecast for.  Datetimes can only be used with pandas inputs that have a datetime index. Strings must be convertible to a date time, such as in '1945-01-01'."><span class="n"><span class="pre">start</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.12)"><span class="pre">datetime</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)"><span class="pre">datetime64</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.1)"><span class="pre">Timestamp</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelFixedResult.forecast.align" title="arch.univariate.base.ARCHModelFixedResult.forecast.align (Python parameter) — Either 'origin' or 'target'.  When set of 'origin', the t-th row of forecasts contains the forecasts for t+1, t+2, ..., t+h. When set to 'target', the t-th row contains the 1-step ahead forecast from time t-1, the 2 step from time t-2, ..., and the h-step from time t-h.  'target' simplified computing forecast errors since the realization and h-step forecast are aligned."><span class="n"><span class="pre">align</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'origin'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'target'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;origin&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelFixedResult.forecast.method" title="arch.univariate.base.ARCHModelFixedResult.forecast.method (Python parameter) — Method to use when producing the forecast. The default is analytic. The method only affects the variance forecast generation.  Not all volatility models support all methods. In particular, volatility models that do not evolve in squares such as EGARCH or TARCH do not support the 'analytic' method for horizons &gt; 1."><span class="n"><span class="pre">method</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'analytic'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'simulation'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'bootstrap'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;analytic&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelFixedResult.forecast.simulations" title="arch.univariate.base.ARCHModelFixedResult.forecast.simulations (Python parameter) — Number of simulations to run when computing the forecast using either simulation or bootstrap."><span class="n"><span class="pre">simulations</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelFixedResult.forecast.rng" title="arch.univariate.base.ARCHModelFixedResult.forecast.rng (Python parameter) — Custom random number generator to use in simulation-based forecasts. Must produce random samples using the syntax rng(size) where size the 2-element tuple (simulations, horizon)."><span class="n"><span class="pre">rng</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.12)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelFixedResult.forecast.random_state" title="arch.univariate.base.ARCHModelFixedResult.forecast.random_state (Python parameter) — NumPy RandomState instance to use when method is 'bootstrap'"><span class="n"><span class="pre">random_state</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.26)"><span class="pre">RandomState</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelFixedResult.forecast.reindex" title="arch.univariate.base.ARCHModelFixedResult.forecast.reindex (Python parameter) — Whether to reindex the forecasts to have the same dimension as the series being forecast."><span class="n"><span class="pre">reindex</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelFixedResult.forecast.x" title="arch.univariate.base.ARCHModelFixedResult.forecast.x (Python parameter) — Values to use for exogenous regressors if any are included in the model. Three formats are accepted:"><span class="n"><span class="pre">x</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Hashable" title="(in Python v3.12)"><span class="pre">Hashable</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference internal" href="../arch.univariate.base.ARCHModelForecast.html#arch.univariate.base.ARCHModelForecast" title="arch.univariate.base.ARCHModelForecast (Python class) — Container for forecasts from an ARCH Model"><span class="pre">ARCHModelForecast</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/base.html#ARCHModelFixedResult.forecast"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.base.ARCHModelFixedResult.forecast" title="Link to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">ARCHModelFixedResult.</span></span><span class="sig-name descname"><span class="pre">forecast</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelFixedResult.forecast.params" title="arch.univariate.base.ARCHModelFixedResult.forecast.params (Python parameter) — Alternative parameters to use.  If not provided, the parameters estimated when fitting the model are used.  Must be identical in shape to the parameters computed by fitting the model."><span class="n"><span class="pre">params</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelFixedResult.forecast.horizon" title="arch.univariate.base.ARCHModelFixedResult.forecast.horizon (Python parameter) — Number of steps to forecast"><span class="n"><span class="pre">horizon</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelFixedResult.forecast.start" title="arch.univariate.base.ARCHModelFixedResult.forecast.start (Python parameter) — An integer, datetime or str indicating the first observation to produce the forecast for.  Datetimes can only be used with pandas inputs that have a datetime index. Strings must be convertible to a date time, such as in '1945-01-01'."><span class="n"><span class="pre">start</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.12)"><span class="pre">datetime</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)"><span class="pre">datetime64</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.4)"><span class="pre">Timestamp</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelFixedResult.forecast.align" title="arch.univariate.base.ARCHModelFixedResult.forecast.align (Python parameter) — Either 'origin' or 'target'.  When set of 'origin', the t-th row of forecasts contains the forecasts for t+1, t+2, ..., t+h. When set to 'target', the t-th row contains the 1-step ahead forecast from time t-1, the 2 step from time t-2, ..., and the h-step from time t-h.  'target' simplified computing forecast errors since the realization and h-step forecast are aligned."><span class="n"><span class="pre">align</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'origin'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'target'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;origin&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelFixedResult.forecast.method" title="arch.univariate.base.ARCHModelFixedResult.forecast.method (Python parameter) — Method to use when producing the forecast. The default is analytic. The method only affects the variance forecast generation.  Not all volatility models support all methods. In particular, volatility models that do not evolve in squares such as EGARCH or TARCH do not support the 'analytic' method for horizons &gt; 1."><span class="n"><span class="pre">method</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'analytic'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'simulation'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'bootstrap'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;analytic&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelFixedResult.forecast.simulations" title="arch.univariate.base.ARCHModelFixedResult.forecast.simulations (Python parameter) — Number of simulations to run when computing the forecast using either simulation or bootstrap."><span class="n"><span class="pre">simulations</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelFixedResult.forecast.rng" title="arch.univariate.base.ARCHModelFixedResult.forecast.rng (Python parameter) — Custom random number generator to use in simulation-based forecasts. Must produce random samples using the syntax rng(size) where size the 2-element tuple (simulations, horizon)."><span class="n"><span class="pre">rng</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.12)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelFixedResult.forecast.random_state" title="arch.univariate.base.ARCHModelFixedResult.forecast.random_state (Python parameter) — NumPy RandomState instance to use when method is 'bootstrap'"><span class="n"><span class="pre">random_state</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.26)"><span class="pre">RandomState</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelFixedResult.forecast.reindex" title="arch.univariate.base.ARCHModelFixedResult.forecast.reindex (Python parameter) — Whether to reindex the forecasts to have the same dimension as the series being forecast."><span class="n"><span class="pre">reindex</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelFixedResult.forecast.x" title="arch.univariate.base.ARCHModelFixedResult.forecast.x (Python parameter) — Values to use for exogenous regressors if any are included in the model. Three formats are accepted:"><span class="n"><span class="pre">x</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Hashable" title="(in Python v3.12)"><span class="pre">Hashable</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference internal" href="../arch.univariate.base.ARCHModelForecast.html#arch.univariate.base.ARCHModelForecast" title="arch.univariate.base.ARCHModelForecast (Python class) — Container for forecasts from an ARCH Model"><span class="pre">ARCHModelForecast</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/base.html#ARCHModelFixedResult.forecast"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.base.ARCHModelFixedResult.forecast" title="Link to this definition">¶</a></dt>
 <dd><p>Construct forecasts from estimated model</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.base.ARCHModelFixedResult.forecast-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.base.ARCHModelFixedResult.forecast-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.base.ARCHModelFixedResult.forecast.params"><span class="n sig-name">params</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.base.ARCHModelFixedResult.forecast.params" title="Permalink to this definition">¶</a></dt><dd><p>Alternative parameters to use.  If not provided, the parameters
+<dt id="arch.univariate.base.ARCHModelFixedResult.forecast.params"><span class="n sig-name">params</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.base.ARCHModelFixedResult.forecast.params" title="Permalink to this definition">¶</a></dt><dd><p>Alternative parameters to use.  If not provided, the parameters
 estimated when fitting the model are used.  Must be identical in
 shape to the parameters computed by fitting the model.</p>
 </dd>
 <dt id="arch.univariate.base.ARCHModelFixedResult.forecast.horizon"><span class="n sig-name">horizon</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code><a class="headerlink" href="#arch.univariate.base.ARCHModelFixedResult.forecast.horizon" title="Permalink to this definition">¶</a></dt><dd><p>Number of steps to forecast</p>
 </dd>
-<dt id="arch.univariate.base.ARCHModelFixedResult.forecast.start"><span class="n sig-name">start</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)">str</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.12)">datetime</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)">datetime64</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.1)">Timestamp</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.base.ARCHModelFixedResult.forecast.start" title="Permalink to this definition">¶</a></dt><dd><p>An integer, datetime or str indicating the first observation to
+<dt id="arch.univariate.base.ARCHModelFixedResult.forecast.start"><span class="n sig-name">start</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)">str</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.12)">datetime</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)">datetime64</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.4)">Timestamp</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.base.ARCHModelFixedResult.forecast.start" title="Permalink to this definition">¶</a></dt><dd><p>An integer, datetime or str indicating the first observation to
 produce the forecast for.  Datetimes can only be used with pandas
 inputs that have a datetime index. Strings must be convertible
 to a date time, such as in ‘1945-01-01’.</p>
@@ -1257,7 +1257,7 @@ <h1 id="arch-univariate-base-archmodelfixedresult-forecast">arch.univariate.base
 </div>
 </p>
 </dd>
-<dt id="arch.univariate.base.ARCHModelFixedResult.forecast.x"><span class="n sig-name">x</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)">dict</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Hashable" title="(in Python v3.12)">Hashable</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.base.ARCHModelFixedResult.forecast.x" title="Permalink to this definition">¶</a></dt><dd><p><p>Values to use for exogenous regressors if any are included in the
+<dt id="arch.univariate.base.ARCHModelFixedResult.forecast.x"><span class="n sig-name">x</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)">dict</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Hashable" title="(in Python v3.12)">Hashable</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.base.ARCHModelFixedResult.forecast.x" title="Permalink to this definition">¶</a></dt><dd><p><p>Values to use for exogenous regressors if any are included in the
 model. Three formats are accepted:</p>
 <ul class="simple">
 <li><p>2-d array-like: This format can be used when there is a single
@@ -1428,7 +1428,7 @@ <h1 id="arch-univariate-base-archmodelfixedresult-forecast">arch.univariate.base
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diff --git a/devel/univariate/generated/generated/arch.univariate.base.ARCHModelFixedResult.hedgehog_plot.html b/devel/univariate/generated/generated/arch.univariate.base.ARCHModelFixedResult.hedgehog_plot.html
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--- a/devel/univariate/generated/generated/arch.univariate.base.ARCHModelFixedResult.hedgehog_plot.html
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@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.base.ARCHModelFixedResult.hedgehog_plot - arch v6.2.1 (+21)</title>
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@@ -1170,12 +1170,12 @@
 <h1 id="arch-univariate-base-archmodelfixedresult-hedgehog-plot">arch.univariate.base.ARCHModelFixedResult.hedgehog_plot<a class="headerlink" href="#arch-univariate-base-archmodelfixedresult-hedgehog-plot" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.base.ARCHModelFixedResult.hedgehog_plot">
-<span class="sig-prename descclassname"><span class="pre">ARCHModelFixedResult.</span></span><span class="sig-name descname"><span class="pre">hedgehog_plot</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelFixedResult.hedgehog_plot.params" title="arch.univariate.base.ARCHModelFixedResult.hedgehog_plot.params (Python parameter) — Alternative parameters to use.  If not provided, the parameters computed by fitting the model are used.  Must be 1-d and identical in shape to the parameters computed by fitting the model."><span class="n"><span class="pre">params</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelFixedResult.hedgehog_plot.horizon" title="arch.univariate.base.ARCHModelFixedResult.hedgehog_plot.horizon (Python parameter) — Number of steps to forecast"><span class="n"><span class="pre">horizon</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">10</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelFixedResult.hedgehog_plot.step" title="arch.univariate.base.ARCHModelFixedResult.hedgehog_plot.step (Python parameter) — Non-negative number of forecasts to skip between spines"><span class="n"><span class="pre">step</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">10</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelFixedResult.hedgehog_plot.start" title="arch.univariate.base.ARCHModelFixedResult.hedgehog_plot.start (Python parameter) — An integer, datetime or str indicating the first observation to produce the forecast for.  Datetimes can only be used with pandas inputs that have a datetime index.  Strings must be convertible to a date time, such as in '1945-01-01'.  If not provided, the start is set to the earliest forecastable date."><span class="n"><span class="pre">start</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.12)"><span class="pre">datetime</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)"><span class="pre">datetime64</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.1)"><span class="pre">Timestamp</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelFixedResult.hedgehog_plot.plot_type" title="arch.univariate.base.ARCHModelFixedResult.hedgehog_plot.plot_type (Python parameter) — Quantity to plot, the forecast volatility or the forecast mean"><span class="n"><span class="pre">plot_type</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'volatility'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'mean'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;volatility&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelFixedResult.hedgehog_plot.method" title="arch.univariate.base.ARCHModelFixedResult.hedgehog_plot.method (Python parameter) — Method to use when producing the forecast. The default is analytic. The method only affects the variance forecast generation.  Not all volatility models support all methods. In particular, volatility models that do not evolve in squares such as EGARCH or TARCH do not support the 'analytic' method for horizons &gt; 1."><span class="n"><span class="pre">method</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'analytic'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'simulation'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'bootstrap'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;analytic&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelFixedResult.hedgehog_plot.simulations" title="arch.univariate.base.ARCHModelFixedResult.hedgehog_plot.simulations (Python parameter) — Number of simulations to run when computing the forecast using either simulation or bootstrap."><span class="n"><span class="pre">simulations</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://matplotlib.org/stable/api/figure_api.html#matplotlib.figure.Figure" title="(in Matplotlib v3.8.0)"><span class="pre">Figure</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/base.html#ARCHModelFixedResult.hedgehog_plot"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.base.ARCHModelFixedResult.hedgehog_plot" title="Link to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">ARCHModelFixedResult.</span></span><span class="sig-name descname"><span class="pre">hedgehog_plot</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelFixedResult.hedgehog_plot.params" title="arch.univariate.base.ARCHModelFixedResult.hedgehog_plot.params (Python parameter) — Alternative parameters to use.  If not provided, the parameters computed by fitting the model are used.  Must be 1-d and identical in shape to the parameters computed by fitting the model."><span class="n"><span class="pre">params</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelFixedResult.hedgehog_plot.horizon" title="arch.univariate.base.ARCHModelFixedResult.hedgehog_plot.horizon (Python parameter) — Number of steps to forecast"><span class="n"><span class="pre">horizon</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">10</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelFixedResult.hedgehog_plot.step" title="arch.univariate.base.ARCHModelFixedResult.hedgehog_plot.step (Python parameter) — Non-negative number of forecasts to skip between spines"><span class="n"><span class="pre">step</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">10</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelFixedResult.hedgehog_plot.start" title="arch.univariate.base.ARCHModelFixedResult.hedgehog_plot.start (Python parameter) — An integer, datetime or str indicating the first observation to produce the forecast for.  Datetimes can only be used with pandas inputs that have a datetime index.  Strings must be convertible to a date time, such as in '1945-01-01'.  If not provided, the start is set to the earliest forecastable date."><span class="n"><span class="pre">start</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.12)"><span class="pre">datetime</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)"><span class="pre">datetime64</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.4)"><span class="pre">Timestamp</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelFixedResult.hedgehog_plot.plot_type" title="arch.univariate.base.ARCHModelFixedResult.hedgehog_plot.plot_type (Python parameter) — Quantity to plot, the forecast volatility or the forecast mean"><span class="n"><span class="pre">plot_type</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'volatility'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'mean'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;volatility&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelFixedResult.hedgehog_plot.method" title="arch.univariate.base.ARCHModelFixedResult.hedgehog_plot.method (Python parameter) — Method to use when producing the forecast. The default is analytic. The method only affects the variance forecast generation.  Not all volatility models support all methods. In particular, volatility models that do not evolve in squares such as EGARCH or TARCH do not support the 'analytic' method for horizons &gt; 1."><span class="n"><span class="pre">method</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'analytic'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'simulation'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'bootstrap'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;analytic&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelFixedResult.hedgehog_plot.simulations" title="arch.univariate.base.ARCHModelFixedResult.hedgehog_plot.simulations (Python parameter) — Number of simulations to run when computing the forecast using either simulation or bootstrap."><span class="n"><span class="pre">simulations</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://matplotlib.org/stable/api/figure_api.html#matplotlib.figure.Figure" title="(in Matplotlib v3.8.2)"><span class="pre">Figure</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/base.html#ARCHModelFixedResult.hedgehog_plot"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.base.ARCHModelFixedResult.hedgehog_plot" title="Link to this definition">¶</a></dt>
 <dd><p>Plot forecasts from estimated model</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.base.ARCHModelFixedResult.hedgehog_plot-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.base.ARCHModelFixedResult.hedgehog_plot-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.base.ARCHModelFixedResult.hedgehog_plot.params"><span class="n sig-name">params</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.base.ARCHModelFixedResult.hedgehog_plot.params" title="Permalink to this definition">¶</a></dt><dd><p>Alternative parameters to use.  If not provided, the parameters
+<dt id="arch.univariate.base.ARCHModelFixedResult.hedgehog_plot.params"><span class="n sig-name">params</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.base.ARCHModelFixedResult.hedgehog_plot.params" title="Permalink to this definition">¶</a></dt><dd><p>Alternative parameters to use.  If not provided, the parameters
 computed by fitting the model are used.  Must be 1-d and identical
 in shape to the parameters computed by fitting the model.</p>
 </dd>
@@ -1183,7 +1183,7 @@ <h1 id="arch-univariate-base-archmodelfixedresult-hedgehog-plot">arch.univariate
 </dd>
 <dt id="arch.univariate.base.ARCHModelFixedResult.hedgehog_plot.step"><span class="n sig-name">step</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">10</span></code><a class="headerlink" href="#arch.univariate.base.ARCHModelFixedResult.hedgehog_plot.step" title="Permalink to this definition">¶</a></dt><dd><p>Non-negative number of forecasts to skip between spines</p>
 </dd>
-<dt id="arch.univariate.base.ARCHModelFixedResult.hedgehog_plot.start"><span class="n sig-name">start</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)">str</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.12)">datetime</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)">datetime64</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.1)">Timestamp</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.base.ARCHModelFixedResult.hedgehog_plot.start" title="Permalink to this definition">¶</a></dt><dd><p>An integer, datetime or str indicating the first observation to
+<dt id="arch.univariate.base.ARCHModelFixedResult.hedgehog_plot.start"><span class="n sig-name">start</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)">str</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.12)">datetime</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)">datetime64</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.4)">Timestamp</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.base.ARCHModelFixedResult.hedgehog_plot.start" title="Permalink to this definition">¶</a></dt><dd><p>An integer, datetime or str indicating the first observation to
 produce the forecast for.  Datetimes can only be used with pandas
 inputs that have a datetime index.  Strings must be convertible
 to a date time, such as in ‘1945-01-01’.  If not provided, the start
@@ -1336,7 +1336,7 @@ <h1 id="arch-univariate-base-archmodelfixedresult-hedgehog-plot">arch.univariate
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
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   </body>
diff --git a/devel/univariate/generated/generated/arch.univariate.base.ARCHModelFixedResult.loglikelihood.html b/devel/univariate/generated/generated/arch.univariate.base.ARCHModelFixedResult.loglikelihood.html
index 27eb5eda2a..371b6c67ae 100644
--- a/devel/univariate/generated/generated/arch.univariate.base.ARCHModelFixedResult.loglikelihood.html
+++ b/devel/univariate/generated/generated/arch.univariate.base.ARCHModelFixedResult.loglikelihood.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.base.ARCHModelFixedResult.loglikelihood - arch v6.2.1 (+21)</title>
+        <title>arch.univariate.base.ARCHModelFixedResult.loglikelihood - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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@@ -1124,7 +1124,7 @@ <h1 id="arch-univariate-base-archmodelfixedresult-loglikelihood">arch.univariate
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
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   </body>
diff --git a/devel/univariate/generated/generated/arch.univariate.base.ARCHModelFixedResult.model.html b/devel/univariate/generated/generated/arch.univariate.base.ARCHModelFixedResult.model.html
index 63a794b03a..822c007e72 100644
--- a/devel/univariate/generated/generated/arch.univariate.base.ARCHModelFixedResult.model.html
+++ b/devel/univariate/generated/generated/arch.univariate.base.ARCHModelFixedResult.model.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.base.ARCHModelFixedResult.model - arch v6.2.1 (+21)</title>
+        <title>arch.univariate.base.ARCHModelFixedResult.model - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -1124,7 +1124,7 @@ <h1 id="arch-univariate-base-archmodelfixedresult-model">arch.univariate.base.AR
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
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diff --git a/devel/univariate/generated/generated/arch.univariate.base.ARCHModelFixedResult.nobs.html b/devel/univariate/generated/generated/arch.univariate.base.ARCHModelFixedResult.nobs.html
index 2589ac8bf6..441288153d 100644
--- a/devel/univariate/generated/generated/arch.univariate.base.ARCHModelFixedResult.nobs.html
+++ b/devel/univariate/generated/generated/arch.univariate.base.ARCHModelFixedResult.nobs.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.base.ARCHModelFixedResult.nobs - arch v6.2.1 (+21)</title>
+        <title>arch.univariate.base.ARCHModelFixedResult.nobs - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
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@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
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-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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     <div class="md-nav__source">
@@ -1124,7 +1124,7 @@ <h1 id="arch-univariate-base-archmodelfixedresult-nobs">arch.univariate.base.ARC
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-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
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diff --git a/devel/univariate/generated/generated/arch.univariate.base.ARCHModelFixedResult.num_params.html b/devel/univariate/generated/generated/arch.univariate.base.ARCHModelFixedResult.num_params.html
index 97b7ff0124..78d9e53123 100644
--- a/devel/univariate/generated/generated/arch.univariate.base.ARCHModelFixedResult.num_params.html
+++ b/devel/univariate/generated/generated/arch.univariate.base.ARCHModelFixedResult.num_params.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.base.ARCHModelFixedResult.num_params - arch v6.2.1 (+21)</title>
+        <title>arch.univariate.base.ARCHModelFixedResult.num_params - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
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-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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@@ -1124,7 +1124,7 @@ <h1 id="arch-univariate-base-archmodelfixedresult-num-params">arch.univariate.ba
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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   </body>
diff --git a/devel/univariate/generated/generated/arch.univariate.base.ARCHModelFixedResult.params.html b/devel/univariate/generated/generated/arch.univariate.base.ARCHModelFixedResult.params.html
index 251803d445..ee122c062e 100644
--- a/devel/univariate/generated/generated/arch.univariate.base.ARCHModelFixedResult.params.html
+++ b/devel/univariate/generated/generated/arch.univariate.base.ARCHModelFixedResult.params.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.base.ARCHModelFixedResult.params - arch v6.2.1 (+21)</title>
+        <title>arch.univariate.base.ARCHModelFixedResult.params - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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     <div class="md-nav__source">
@@ -1006,7 +1006,7 @@
 <h1 id="arch-univariate-base-archmodelfixedresult-params">arch.univariate.base.ARCHModelFixedResult.params<a class="headerlink" href="#arch-univariate-base-archmodelfixedresult-params" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.base.ARCHModelFixedResult.params">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARCHModelFixedResult.</span></span><span class="sig-name descname"><span class="pre">params</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span><a class="headerlink" href="#arch.univariate.base.ARCHModelFixedResult.params" title="Link to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARCHModelFixedResult.</span></span><span class="sig-name descname"><span class="pre">params</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a></span><a class="headerlink" href="#arch.univariate.base.ARCHModelFixedResult.params" title="Link to this definition">¶</a></dt>
 <dd><p>Model Parameters</p>
 </dd></dl>
 
@@ -1124,7 +1124,7 @@ <h1 id="arch-univariate-base-archmodelfixedresult-params">arch.univariate.base.A
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
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diff --git a/devel/univariate/generated/generated/arch.univariate.base.ARCHModelFixedResult.plot.html b/devel/univariate/generated/generated/arch.univariate.base.ARCHModelFixedResult.plot.html
index b5c8148742..0a330da2c6 100644
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+++ b/devel/univariate/generated/generated/arch.univariate.base.ARCHModelFixedResult.plot.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.base.ARCHModelFixedResult.plot - arch v6.2.1 (+21)</title>
+        <title>arch.univariate.base.ARCHModelFixedResult.plot - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
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         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
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-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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     <div class="md-nav__source">
@@ -1100,7 +1100,7 @@
 <h1 id="arch-univariate-base-archmodelfixedresult-plot">arch.univariate.base.ARCHModelFixedResult.plot<a class="headerlink" href="#arch-univariate-base-archmodelfixedresult-plot" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.base.ARCHModelFixedResult.plot">
-<span class="sig-prename descclassname"><span class="pre">ARCHModelFixedResult.</span></span><span class="sig-name descname"><span class="pre">plot</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelFixedResult.plot.annualize" title="arch.univariate.base.ARCHModelFixedResult.plot.annualize (Python parameter) — String containing frequency of data that indicates plot should contain annualized volatility.  Supported values are 'D' (daily), 'W' (weekly) and 'M' (monthly), which scale variance by 252, 52, and 12, respectively."><span class="n"><span class="pre">annualize</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelFixedResult.plot.scale" title="arch.univariate.base.ARCHModelFixedResult.plot.scale (Python parameter) — Value to use when scaling returns to annualize.  If scale is provided, annualize is ignored and the value in scale is used."><span class="n"><span class="pre">scale</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://matplotlib.org/stable/api/figure_api.html#matplotlib.figure.Figure" title="(in Matplotlib v3.8.0)"><span class="pre">Figure</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/base.html#ARCHModelFixedResult.plot"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.base.ARCHModelFixedResult.plot" title="Link to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">ARCHModelFixedResult.</span></span><span class="sig-name descname"><span class="pre">plot</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelFixedResult.plot.annualize" title="arch.univariate.base.ARCHModelFixedResult.plot.annualize (Python parameter) — String containing frequency of data that indicates plot should contain annualized volatility.  Supported values are 'D' (daily), 'W' (weekly) and 'M' (monthly), which scale variance by 252, 52, and 12, respectively."><span class="n"><span class="pre">annualize</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelFixedResult.plot.scale" title="arch.univariate.base.ARCHModelFixedResult.plot.scale (Python parameter) — Value to use when scaling returns to annualize.  If scale is provided, annualize is ignored and the value in scale is used."><span class="n"><span class="pre">scale</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://matplotlib.org/stable/api/figure_api.html#matplotlib.figure.Figure" title="(in Matplotlib v3.8.2)"><span class="pre">Figure</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/base.html#ARCHModelFixedResult.plot"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.base.ARCHModelFixedResult.plot" title="Link to this definition">¶</a></dt>
 <dd><p>Plot standardized residuals and conditional volatility</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.base.ARCHModelFixedResult.plot-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.base.ARCHModelFixedResult.plot-parameters" title="Permalink to this headline">¶</a></dt>
@@ -1256,7 +1256,7 @@ <h1 id="arch-univariate-base-archmodelfixedresult-plot">arch.univariate.base.ARC
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
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diff --git a/devel/univariate/generated/generated/arch.univariate.base.ARCHModelFixedResult.resid.html b/devel/univariate/generated/generated/arch.univariate.base.ARCHModelFixedResult.resid.html
index d552f27ce8..58dc6dc184 100644
--- a/devel/univariate/generated/generated/arch.univariate.base.ARCHModelFixedResult.resid.html
+++ b/devel/univariate/generated/generated/arch.univariate.base.ARCHModelFixedResult.resid.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.base.ARCHModelFixedResult.resid - arch v6.2.1 (+21)</title>
+        <title>arch.univariate.base.ARCHModelFixedResult.resid - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
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@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
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   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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     <div class="md-nav__source">
@@ -1006,7 +1006,7 @@
 <h1 id="arch-univariate-base-archmodelfixedresult-resid">arch.univariate.base.ARCHModelFixedResult.resid<a class="headerlink" href="#arch-univariate-base-archmodelfixedresult-resid" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.base.ARCHModelFixedResult.resid">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARCHModelFixedResult.</span></span><span class="sig-name descname"><span class="pre">resid</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span><a class="headerlink" href="#arch.univariate.base.ARCHModelFixedResult.resid" title="Link to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARCHModelFixedResult.</span></span><span class="sig-name descname"><span class="pre">resid</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a></span><a class="headerlink" href="#arch.univariate.base.ARCHModelFixedResult.resid" title="Link to this definition">¶</a></dt>
 <dd><p>Model residuals</p>
 </dd></dl>
 
@@ -1124,7 +1124,7 @@ <h1 id="arch-univariate-base-archmodelfixedresult-resid">arch.univariate.base.AR
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
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diff --git a/devel/univariate/generated/generated/arch.univariate.base.ARCHModelFixedResult.std_resid.html b/devel/univariate/generated/generated/arch.univariate.base.ARCHModelFixedResult.std_resid.html
index e086a1f40d..de0cc9ebe0 100644
--- a/devel/univariate/generated/generated/arch.univariate.base.ARCHModelFixedResult.std_resid.html
+++ b/devel/univariate/generated/generated/arch.univariate.base.ARCHModelFixedResult.std_resid.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.base.ARCHModelFixedResult.std_resid - arch v6.2.1 (+21)</title>
+        <title>arch.univariate.base.ARCHModelFixedResult.std_resid - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
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@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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@@ -102,7 +102,7 @@
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+            arch v6.2.1 (+23)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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@@ -1006,7 +1006,7 @@
 <h1 id="arch-univariate-base-archmodelfixedresult-std-resid">arch.univariate.base.ARCHModelFixedResult.std_resid<a class="headerlink" href="#arch-univariate-base-archmodelfixedresult-std-resid" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.base.ARCHModelFixedResult.std_resid">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARCHModelFixedResult.</span></span><span class="sig-name descname"><span class="pre">std_resid</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span><a class="headerlink" href="#arch.univariate.base.ARCHModelFixedResult.std_resid" title="Link to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARCHModelFixedResult.</span></span><span class="sig-name descname"><span class="pre">std_resid</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a></span><a class="headerlink" href="#arch.univariate.base.ARCHModelFixedResult.std_resid" title="Link to this definition">¶</a></dt>
 <dd><p>Residuals standardized by conditional volatility</p>
 </dd></dl>
 
@@ -1124,7 +1124,7 @@ <h1 id="arch-univariate-base-archmodelfixedresult-std-resid">arch.univariate.bas
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
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diff --git a/devel/univariate/generated/generated/arch.univariate.base.ARCHModelFixedResult.summary.html b/devel/univariate/generated/generated/arch.univariate.base.ARCHModelFixedResult.summary.html
index c916fd1586..1490f90e81 100644
--- a/devel/univariate/generated/generated/arch.univariate.base.ARCHModelFixedResult.summary.html
+++ b/devel/univariate/generated/generated/arch.univariate.base.ARCHModelFixedResult.summary.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.base.ARCHModelFixedResult.summary - arch v6.2.1 (+21)</title>
+        <title>arch.univariate.base.ARCHModelFixedResult.summary - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
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   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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     <div class="md-nav__source">
@@ -1046,7 +1046,7 @@
 <h1 id="arch-univariate-base-archmodelfixedresult-summary">arch.univariate.base.ARCHModelFixedResult.summary<a class="headerlink" href="#arch-univariate-base-archmodelfixedresult-summary" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.base.ARCHModelFixedResult.summary">
-<span class="sig-prename descclassname"><span class="pre">ARCHModelFixedResult.</span></span><span class="sig-name descname"><span class="pre">summary</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://www.statsmodels.org/dev/generated/statsmodels.iolib.summary.Summary.html#statsmodels.iolib.summary.Summary" title="(in statsmodels 0.15.0 (+59) v0.15.0 (+59))"><span class="pre">Summary</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/base.html#ARCHModelFixedResult.summary"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.base.ARCHModelFixedResult.summary" title="Link to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">ARCHModelFixedResult.</span></span><span class="sig-name descname"><span class="pre">summary</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://www.statsmodels.org/dev/generated/statsmodels.iolib.summary.Summary.html#statsmodels.iolib.summary.Summary" title="(in statsmodels 0.15.0 (+132) v0.15.0 (+132))"><span class="pre">Summary</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/base.html#ARCHModelFixedResult.summary"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.base.ARCHModelFixedResult.summary" title="Link to this definition">¶</a></dt>
 <dd><p>Constructs a summary of the results from a fit model.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.base.ARCHModelFixedResult.summary-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.univariate.base.ARCHModelFixedResult.summary-returns" title="Permalink to this headline">¶</a></dt>
@@ -1173,7 +1173,7 @@ <h1 id="arch-univariate-base-archmodelfixedresult-summary">arch.univariate.base.
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diff --git a/devel/univariate/generated/generated/arch.univariate.base.ARCHModelForecast.mean.html b/devel/univariate/generated/generated/arch.univariate.base.ARCHModelForecast.mean.html
index 67b782f4bc..9765f01e2f 100644
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@@ -15,7 +15,7 @@
     
   
       
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@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
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-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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@@ -881,7 +881,7 @@
 <h1 id="arch-univariate-base-archmodelforecast-mean">arch.univariate.base.ARCHModelForecast.mean<a class="headerlink" href="#arch-univariate-base-archmodelforecast-mean" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.base.ARCHModelForecast.mean">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARCHModelForecast.</span></span><span class="sig-name descname"><span class="pre">mean</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a></span><a class="headerlink" href="#arch.univariate.base.ARCHModelForecast.mean" title="Link to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARCHModelForecast.</span></span><span class="sig-name descname"><span class="pre">mean</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><span class="pre">DataFrame</span></a></span><a class="headerlink" href="#arch.univariate.base.ARCHModelForecast.mean" title="Link to this definition">¶</a></dt>
 <dd><p>Forecast values for the conditional mean of the process</p>
 </dd></dl>
 
@@ -999,7 +999,7 @@ <h1 id="arch-univariate-base-archmodelforecast-mean">arch.univariate.base.ARCHMo
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-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
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diff --git a/devel/univariate/generated/generated/arch.univariate.base.ARCHModelForecast.residual_variance.html b/devel/univariate/generated/generated/arch.univariate.base.ARCHModelForecast.residual_variance.html
index 9fbeced869..6ec5e6ae90 100644
--- a/devel/univariate/generated/generated/arch.univariate.base.ARCHModelForecast.residual_variance.html
+++ b/devel/univariate/generated/generated/arch.univariate.base.ARCHModelForecast.residual_variance.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.base.ARCHModelForecast.residual_variance - arch v6.2.1 (+21)</title>
+        <title>arch.univariate.base.ARCHModelForecast.residual_variance - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
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@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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     <div class="md-nav__source">
@@ -881,7 +881,7 @@
 <h1 id="arch-univariate-base-archmodelforecast-residual-variance">arch.univariate.base.ARCHModelForecast.residual_variance<a class="headerlink" href="#arch-univariate-base-archmodelforecast-residual-variance" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.base.ARCHModelForecast.residual_variance">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARCHModelForecast.</span></span><span class="sig-name descname"><span class="pre">residual_variance</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a></span><a class="headerlink" href="#arch.univariate.base.ARCHModelForecast.residual_variance" title="Link to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARCHModelForecast.</span></span><span class="sig-name descname"><span class="pre">residual_variance</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><span class="pre">DataFrame</span></a></span><a class="headerlink" href="#arch.univariate.base.ARCHModelForecast.residual_variance" title="Link to this definition">¶</a></dt>
 <dd><p>Forecast values for the conditional variance of the residuals</p>
 </dd></dl>
 
@@ -999,7 +999,7 @@ <h1 id="arch-univariate-base-archmodelforecast-residual-variance">arch.univariat
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
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diff --git a/devel/univariate/generated/generated/arch.univariate.base.ARCHModelForecast.simulations.html b/devel/univariate/generated/generated/arch.univariate.base.ARCHModelForecast.simulations.html
index b0312d2d6c..78ecd151c5 100644
--- a/devel/univariate/generated/generated/arch.univariate.base.ARCHModelForecast.simulations.html
+++ b/devel/univariate/generated/generated/arch.univariate.base.ARCHModelForecast.simulations.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.base.ARCHModelForecast.simulations - arch v6.2.1 (+21)</title>
+        <title>arch.univariate.base.ARCHModelForecast.simulations - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
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@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -1047,7 +1047,7 @@ <h1 id="arch-univariate-base-archmodelforecast-simulations">arch.univariate.base
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
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   </body>
diff --git a/devel/univariate/generated/generated/arch.univariate.base.ARCHModelForecast.variance.html b/devel/univariate/generated/generated/arch.univariate.base.ARCHModelForecast.variance.html
index cda6728b80..9d4cd69f0c 100644
--- a/devel/univariate/generated/generated/arch.univariate.base.ARCHModelForecast.variance.html
+++ b/devel/univariate/generated/generated/arch.univariate.base.ARCHModelForecast.variance.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.base.ARCHModelForecast.variance - arch v6.2.1 (+21)</title>
+        <title>arch.univariate.base.ARCHModelForecast.variance - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -881,7 +881,7 @@
 <h1 id="arch-univariate-base-archmodelforecast-variance">arch.univariate.base.ARCHModelForecast.variance<a class="headerlink" href="#arch-univariate-base-archmodelforecast-variance" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.base.ARCHModelForecast.variance">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARCHModelForecast.</span></span><span class="sig-name descname"><span class="pre">variance</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a></span><a class="headerlink" href="#arch.univariate.base.ARCHModelForecast.variance" title="Link to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARCHModelForecast.</span></span><span class="sig-name descname"><span class="pre">variance</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><span class="pre">DataFrame</span></a></span><a class="headerlink" href="#arch.univariate.base.ARCHModelForecast.variance" title="Link to this definition">¶</a></dt>
 <dd><p>Forecast values for the conditional variance of the process</p>
 </dd></dl>
 
@@ -999,7 +999,7 @@ <h1 id="arch-univariate-base-archmodelforecast-variance">arch.univariate.base.AR
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
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   </body>
diff --git a/devel/univariate/generated/generated/arch.univariate.base.ARCHModelForecastSimulation.index.html b/devel/univariate/generated/generated/arch.univariate.base.ARCHModelForecastSimulation.index.html
index 199fd49015..103955ada3 100644
--- a/devel/univariate/generated/generated/arch.univariate.base.ARCHModelForecastSimulation.index.html
+++ b/devel/univariate/generated/generated/arch.univariate.base.ARCHModelForecastSimulation.index.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.base.ARCHModelForecastSimulation.index - arch v6.2.1 (+21)</title>
+        <title>arch.univariate.base.ARCHModelForecastSimulation.index - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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     <div class="md-nav__source">
@@ -896,7 +896,7 @@
 <h1 id="arch-univariate-base-archmodelforecastsimulation-index">arch.univariate.base.ARCHModelForecastSimulation.index<a class="headerlink" href="#arch-univariate-base-archmodelforecastsimulation-index" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.base.ARCHModelForecastSimulation.index">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARCHModelForecastSimulation.</span></span><span class="sig-name descname"><span class="pre">index</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Index.html#pandas.Index" title="(in pandas v2.1.1)"><span class="pre">Index</span></a></span><a class="headerlink" href="#arch.univariate.base.ARCHModelForecastSimulation.index" title="Link to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARCHModelForecastSimulation.</span></span><span class="sig-name descname"><span class="pre">index</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Index.html#pandas.Index" title="(in pandas v2.1.4)"><span class="pre">Index</span></a></span><a class="headerlink" href="#arch.univariate.base.ARCHModelForecastSimulation.index" title="Link to this definition">¶</a></dt>
 <dd><p>The index aligned to dimension 0 of the simulation paths</p>
 </dd></dl>
 
@@ -1014,7 +1014,7 @@ <h1 id="arch-univariate-base-archmodelforecastsimulation-index">arch.univariate.
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
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diff --git a/devel/univariate/generated/generated/arch.univariate.base.ARCHModelForecastSimulation.residual_variances.html b/devel/univariate/generated/generated/arch.univariate.base.ARCHModelForecastSimulation.residual_variances.html
index b741d2f052..1f0920f46a 100644
--- a/devel/univariate/generated/generated/arch.univariate.base.ARCHModelForecastSimulation.residual_variances.html
+++ b/devel/univariate/generated/generated/arch.univariate.base.ARCHModelForecastSimulation.residual_variances.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.base.ARCHModelForecastSimulation.residual_variances - arch v6.2.1 (+21)</title>
+        <title>arch.univariate.base.ARCHModelForecastSimulation.residual_variances - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
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-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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     <div class="md-nav__source">
@@ -1014,7 +1014,7 @@ <h1 id="arch-univariate-base-archmodelforecastsimulation-residual-variances">arc
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
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   </body>
diff --git a/devel/univariate/generated/generated/arch.univariate.base.ARCHModelForecastSimulation.residuals.html b/devel/univariate/generated/generated/arch.univariate.base.ARCHModelForecastSimulation.residuals.html
index e628d96d63..b19f04f84b 100644
--- a/devel/univariate/generated/generated/arch.univariate.base.ARCHModelForecastSimulation.residuals.html
+++ b/devel/univariate/generated/generated/arch.univariate.base.ARCHModelForecastSimulation.residuals.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.base.ARCHModelForecastSimulation.residuals - arch v6.2.1 (+21)</title>
+        <title>arch.univariate.base.ARCHModelForecastSimulation.residuals - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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     <div class="md-nav__source">
@@ -1014,7 +1014,7 @@ <h1 id="arch-univariate-base-archmodelforecastsimulation-residuals">arch.univari
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-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
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   </body>
diff --git a/devel/univariate/generated/generated/arch.univariate.base.ARCHModelForecastSimulation.values.html b/devel/univariate/generated/generated/arch.univariate.base.ARCHModelForecastSimulation.values.html
index 1758fad36c..724893d108 100644
--- a/devel/univariate/generated/generated/arch.univariate.base.ARCHModelForecastSimulation.values.html
+++ b/devel/univariate/generated/generated/arch.univariate.base.ARCHModelForecastSimulation.values.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.base.ARCHModelForecastSimulation.values - arch v6.2.1 (+21)</title>
+        <title>arch.univariate.base.ARCHModelForecastSimulation.values - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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     <div class="md-nav__source">
@@ -1014,7 +1014,7 @@ <h1 id="arch-univariate-base-archmodelforecastsimulation-values">arch.univariate
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-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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diff --git a/devel/univariate/generated/generated/arch.univariate.base.ARCHModelForecastSimulation.variances.html b/devel/univariate/generated/generated/arch.univariate.base.ARCHModelForecastSimulation.variances.html
index 80e0f450a6..c1426b5b51 100644
--- a/devel/univariate/generated/generated/arch.univariate.base.ARCHModelForecastSimulation.variances.html
+++ b/devel/univariate/generated/generated/arch.univariate.base.ARCHModelForecastSimulation.variances.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.base.ARCHModelForecastSimulation.variances - arch v6.2.1 (+21)</title>
+        <title>arch.univariate.base.ARCHModelForecastSimulation.variances - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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     <div class="md-nav__source">
@@ -1014,7 +1014,7 @@ <h1 id="arch-univariate-base-archmodelforecastsimulation-variances">arch.univari
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-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
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diff --git a/devel/univariate/generated/generated/arch.univariate.base.ARCHModelResult.aic.html b/devel/univariate/generated/generated/arch.univariate.base.ARCHModelResult.aic.html
index 6d469e3cfc..c4817d0f82 100644
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+++ b/devel/univariate/generated/generated/arch.univariate.base.ARCHModelResult.aic.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.base.ARCHModelResult.aic - arch v6.2.1 (+21)</title>
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@@ -47,7 +47,7 @@
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@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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@@ -1305,7 +1305,7 @@ <h1 id="arch-univariate-base-archmodelresult-aic">arch.univariate.base.ARCHModel
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-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
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diff --git a/devel/univariate/generated/generated/arch.univariate.base.ARCHModelResult.arch_lm_test.html b/devel/univariate/generated/generated/arch.univariate.base.ARCHModelResult.arch_lm_test.html
index 09a2f93e7c..10e8a8c853 100644
--- a/devel/univariate/generated/generated/arch.univariate.base.ARCHModelResult.arch_lm_test.html
+++ b/devel/univariate/generated/generated/arch.univariate.base.ARCHModelResult.arch_lm_test.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.base.ARCHModelResult.arch_lm_test - arch v6.2.1 (+21)</title>
+        <title>arch.univariate.base.ARCHModelResult.arch_lm_test - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
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@@ -1416,7 +1416,7 @@ <h1 id="arch-univariate-base-archmodelresult-arch-lm-test">arch.univariate.base.
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
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diff --git a/devel/univariate/generated/generated/arch.univariate.base.ARCHModelResult.bic.html b/devel/univariate/generated/generated/arch.univariate.base.ARCHModelResult.bic.html
index 3d9ff0bb5c..f9dde4dcf6 100644
--- a/devel/univariate/generated/generated/arch.univariate.base.ARCHModelResult.bic.html
+++ b/devel/univariate/generated/generated/arch.univariate.base.ARCHModelResult.bic.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.base.ARCHModelResult.bic - arch v6.2.1 (+21)</title>
+        <title>arch.univariate.base.ARCHModelResult.bic - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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     <div class="md-nav__source">
@@ -1305,7 +1305,7 @@ <h1 id="arch-univariate-base-archmodelresult-bic">arch.univariate.base.ARCHModel
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
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   </body>
diff --git a/devel/univariate/generated/generated/arch.univariate.base.ARCHModelResult.conditional_volatility.html b/devel/univariate/generated/generated/arch.univariate.base.ARCHModelResult.conditional_volatility.html
index da7feeed22..aa9f3d8735 100644
--- a/devel/univariate/generated/generated/arch.univariate.base.ARCHModelResult.conditional_volatility.html
+++ b/devel/univariate/generated/generated/arch.univariate.base.ARCHModelResult.conditional_volatility.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.base.ARCHModelResult.conditional_volatility - arch v6.2.1 (+21)</title>
+        <title>arch.univariate.base.ARCHModelResult.conditional_volatility - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -1226,7 +1226,7 @@
 <h1 id="arch-univariate-base-archmodelresult-conditional-volatility">arch.univariate.base.ARCHModelResult.conditional_volatility<a class="headerlink" href="#arch-univariate-base-archmodelresult-conditional-volatility" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.base.ARCHModelResult.conditional_volatility">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARCHModelResult.</span></span><span class="sig-name descname"><span class="pre">conditional_volatility</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.conditional_volatility" title="Link to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARCHModelResult.</span></span><span class="sig-name descname"><span class="pre">conditional_volatility</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.conditional_volatility" title="Link to this definition">¶</a></dt>
 <dd><p>Estimated conditional volatility</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.base.ARCHModelResult.conditional_volatility-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.conditional_volatility-returns" title="Permalink to this headline">¶</a></dt>
@@ -1355,7 +1355,7 @@ <h1 id="arch-univariate-base-archmodelresult-conditional-volatility">arch.univar
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
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   </body>
diff --git a/devel/univariate/generated/generated/arch.univariate.base.ARCHModelResult.conf_int.html b/devel/univariate/generated/generated/arch.univariate.base.ARCHModelResult.conf_int.html
index cf663b5f28..1be8658e6d 100644
--- a/devel/univariate/generated/generated/arch.univariate.base.ARCHModelResult.conf_int.html
+++ b/devel/univariate/generated/generated/arch.univariate.base.ARCHModelResult.conf_int.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.base.ARCHModelResult.conf_int - arch v6.2.1 (+21)</title>
+        <title>arch.univariate.base.ARCHModelResult.conf_int - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -1266,7 +1266,7 @@
 <h1 id="arch-univariate-base-archmodelresult-conf-int">arch.univariate.base.ARCHModelResult.conf_int<a class="headerlink" href="#arch-univariate-base-archmodelresult-conf-int" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.base.ARCHModelResult.conf_int">
-<span class="sig-prename descclassname"><span class="pre">ARCHModelResult.</span></span><span class="sig-name descname"><span class="pre">conf_int</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelResult.conf_int.alpha" title="arch.univariate.base.ARCHModelResult.conf_int.alpha (Python parameter) — Size (prob.) to use when constructing the confidence interval."><span class="n"><span class="pre">alpha</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mf">0.05</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/base.html#ARCHModelResult.conf_int"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.conf_int" title="Link to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">ARCHModelResult.</span></span><span class="sig-name descname"><span class="pre">conf_int</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelResult.conf_int.alpha" title="arch.univariate.base.ARCHModelResult.conf_int.alpha (Python parameter) — Size (prob.) to use when constructing the confidence interval."><span class="n"><span class="pre">alpha</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mf">0.05</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><span class="pre">DataFrame</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/base.html#ARCHModelResult.conf_int"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.conf_int" title="Link to this definition">¶</a></dt>
 <dd><p>Parameter confidence intervals</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.base.ARCHModelResult.conf_int-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.conf_int-parameters" title="Permalink to this headline">¶</a></dt>
@@ -1280,7 +1280,7 @@ <h1 id="arch-univariate-base-archmodelresult-conf-int">arch.univariate.base.ARCH
 ith parameter</p>
 </dd>
 <dt class="field-odd" id="arch.univariate.base.ARCHModelResult.conf_int-return-type">Return type<span class="colon">:</span><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.conf_int-return-type" title="Permalink to this headline">¶</a></dt>
-<dd class="field-odd"><p><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><code class="xref py py-class docutils literal notranslate"><span class="pre">pandas.DataFrame</span></code></a></p>
+<dd class="field-odd"><p><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><code class="xref py py-class docutils literal notranslate"><span class="pre">pandas.DataFrame</span></code></a></p>
 </dd>
 </dl>
 </dd></dl>
@@ -1399,7 +1399,7 @@ <h1 id="arch-univariate-base-archmodelresult-conf-int">arch.univariate.base.ARCH
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diff --git a/devel/univariate/generated/generated/arch.univariate.base.ARCHModelResult.convergence_flag.html b/devel/univariate/generated/generated/arch.univariate.base.ARCHModelResult.convergence_flag.html
index 69f2f3070b..93fb097555 100644
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diff --git a/devel/univariate/generated/generated/arch.univariate.base.ARCHModelResult.fit_start.html b/devel/univariate/generated/generated/arch.univariate.base.ARCHModelResult.fit_start.html
index 68f62b714e..06fe29d3f0 100644
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@@ -15,7 +15,7 @@
     
   
       
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@@ -102,7 +102,7 @@
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+            arch v6.2.1 (+23)
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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diff --git a/devel/univariate/generated/generated/arch.univariate.base.ARCHModelResult.fit_stop.html b/devel/univariate/generated/generated/arch.univariate.base.ARCHModelResult.fit_stop.html
index a19a7b340e..6d347f1b4b 100644
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+++ b/devel/univariate/generated/generated/arch.univariate.base.ARCHModelResult.fit_stop.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.base.ARCHModelResult.fit_stop - arch v6.2.1 (+21)</title>
+        <title>arch.univariate.base.ARCHModelResult.fit_stop - arch v6.2.1 (+23)</title>
       
     
 
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
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+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.2.1 (+21)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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@@ -1304,7 +1304,7 @@ <h1 id="arch-univariate-base-archmodelresult-fit-stop">arch.univariate.base.ARCH
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diff --git a/devel/univariate/generated/generated/arch.univariate.base.ARCHModelResult.forecast.html b/devel/univariate/generated/generated/arch.univariate.base.ARCHModelResult.forecast.html
index 21696b26d6..361546983b 100644
--- a/devel/univariate/generated/generated/arch.univariate.base.ARCHModelResult.forecast.html
+++ b/devel/univariate/generated/generated/arch.univariate.base.ARCHModelResult.forecast.html
@@ -15,7 +15,7 @@
     
   
       
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+        <title>arch.univariate.base.ARCHModelResult.forecast - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
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 <h1 id="arch-univariate-base-archmodelresult-forecast">arch.univariate.base.ARCHModelResult.forecast<a class="headerlink" href="#arch-univariate-base-archmodelresult-forecast" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.base.ARCHModelResult.forecast">
-<span class="sig-prename descclassname"><span class="pre">ARCHModelResult.</span></span><span class="sig-name descname"><span class="pre">forecast</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelResult.forecast.params" title="arch.univariate.base.ARCHModelResult.forecast.params (Python parameter) — Alternative parameters to use.  If not provided, the parameters estimated when fitting the model are used.  Must be identical in shape to the parameters computed by fitting the model."><span class="n"><span class="pre">params</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelResult.forecast.horizon" title="arch.univariate.base.ARCHModelResult.forecast.horizon (Python parameter) — Number of steps to forecast"><span class="n"><span class="pre">horizon</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelResult.forecast.start" title="arch.univariate.base.ARCHModelResult.forecast.start (Python parameter) — An integer, datetime or str indicating the first observation to produce the forecast for.  Datetimes can only be used with pandas inputs that have a datetime index. Strings must be convertible to a date time, such as in '1945-01-01'."><span class="n"><span class="pre">start</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.12)"><span class="pre">datetime</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)"><span class="pre">datetime64</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.1)"><span class="pre">Timestamp</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelResult.forecast.align" title="arch.univariate.base.ARCHModelResult.forecast.align (Python parameter) — Either 'origin' or 'target'.  When set of 'origin', the t-th row of forecasts contains the forecasts for t+1, t+2, ..., t+h. When set to 'target', the t-th row contains the 1-step ahead forecast from time t-1, the 2 step from time t-2, ..., and the h-step from time t-h.  'target' simplified computing forecast errors since the realization and h-step forecast are aligned."><span class="n"><span class="pre">align</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'origin'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'target'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;origin&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelResult.forecast.method" title="arch.univariate.base.ARCHModelResult.forecast.method (Python parameter) — Method to use when producing the forecast. The default is analytic. The method only affects the variance forecast generation.  Not all volatility models support all methods. In particular, volatility models that do not evolve in squares such as EGARCH or TARCH do not support the 'analytic' method for horizons &gt; 1."><span class="n"><span class="pre">method</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'analytic'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'simulation'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'bootstrap'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;analytic&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelResult.forecast.simulations" title="arch.univariate.base.ARCHModelResult.forecast.simulations (Python parameter) — Number of simulations to run when computing the forecast using either simulation or bootstrap."><span class="n"><span class="pre">simulations</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelResult.forecast.rng" title="arch.univariate.base.ARCHModelResult.forecast.rng (Python parameter) — Custom random number generator to use in simulation-based forecasts. Must produce random samples using the syntax rng(size) where size the 2-element tuple (simulations, horizon)."><span class="n"><span class="pre">rng</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.12)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelResult.forecast.random_state" title="arch.univariate.base.ARCHModelResult.forecast.random_state (Python parameter) — NumPy RandomState instance to use when method is 'bootstrap'"><span class="n"><span class="pre">random_state</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.26)"><span class="pre">RandomState</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelResult.forecast.reindex" title="arch.univariate.base.ARCHModelResult.forecast.reindex (Python parameter) — Whether to reindex the forecasts to have the same dimension as the series being forecast."><span class="n"><span class="pre">reindex</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelResult.forecast.x" title="arch.univariate.base.ARCHModelResult.forecast.x (Python parameter) — Values to use for exogenous regressors if any are included in the model. Three formats are accepted:"><span class="n"><span class="pre">x</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Hashable" title="(in Python v3.12)"><span class="pre">Hashable</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference internal" href="../arch.univariate.base.ARCHModelForecast.html#arch.univariate.base.ARCHModelForecast" title="arch.univariate.base.ARCHModelForecast (Python class) — Container for forecasts from an ARCH Model"><span class="pre">ARCHModelForecast</span></a></span></span></span><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.forecast" title="Link to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">ARCHModelResult.</span></span><span class="sig-name descname"><span class="pre">forecast</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelResult.forecast.params" title="arch.univariate.base.ARCHModelResult.forecast.params (Python parameter) — Alternative parameters to use.  If not provided, the parameters estimated when fitting the model are used.  Must be identical in shape to the parameters computed by fitting the model."><span class="n"><span class="pre">params</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelResult.forecast.horizon" title="arch.univariate.base.ARCHModelResult.forecast.horizon (Python parameter) — Number of steps to forecast"><span class="n"><span class="pre">horizon</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelResult.forecast.start" title="arch.univariate.base.ARCHModelResult.forecast.start (Python parameter) — An integer, datetime or str indicating the first observation to produce the forecast for.  Datetimes can only be used with pandas inputs that have a datetime index. Strings must be convertible to a date time, such as in '1945-01-01'."><span class="n"><span class="pre">start</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.12)"><span class="pre">datetime</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)"><span class="pre">datetime64</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.4)"><span class="pre">Timestamp</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelResult.forecast.align" title="arch.univariate.base.ARCHModelResult.forecast.align (Python parameter) — Either 'origin' or 'target'.  When set of 'origin', the t-th row of forecasts contains the forecasts for t+1, t+2, ..., t+h. When set to 'target', the t-th row contains the 1-step ahead forecast from time t-1, the 2 step from time t-2, ..., and the h-step from time t-h.  'target' simplified computing forecast errors since the realization and h-step forecast are aligned."><span class="n"><span class="pre">align</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'origin'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'target'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;origin&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelResult.forecast.method" title="arch.univariate.base.ARCHModelResult.forecast.method (Python parameter) — Method to use when producing the forecast. The default is analytic. The method only affects the variance forecast generation.  Not all volatility models support all methods. In particular, volatility models that do not evolve in squares such as EGARCH or TARCH do not support the 'analytic' method for horizons &gt; 1."><span class="n"><span class="pre">method</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'analytic'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'simulation'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'bootstrap'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;analytic&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelResult.forecast.simulations" title="arch.univariate.base.ARCHModelResult.forecast.simulations (Python parameter) — Number of simulations to run when computing the forecast using either simulation or bootstrap."><span class="n"><span class="pre">simulations</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelResult.forecast.rng" title="arch.univariate.base.ARCHModelResult.forecast.rng (Python parameter) — Custom random number generator to use in simulation-based forecasts. Must produce random samples using the syntax rng(size) where size the 2-element tuple (simulations, horizon)."><span class="n"><span class="pre">rng</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.12)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelResult.forecast.random_state" title="arch.univariate.base.ARCHModelResult.forecast.random_state (Python parameter) — NumPy RandomState instance to use when method is 'bootstrap'"><span class="n"><span class="pre">random_state</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.26)"><span class="pre">RandomState</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelResult.forecast.reindex" title="arch.univariate.base.ARCHModelResult.forecast.reindex (Python parameter) — Whether to reindex the forecasts to have the same dimension as the series being forecast."><span class="n"><span class="pre">reindex</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelResult.forecast.x" title="arch.univariate.base.ARCHModelResult.forecast.x (Python parameter) — Values to use for exogenous regressors if any are included in the model. Three formats are accepted:"><span class="n"><span class="pre">x</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)"><span class="pre">dict</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Hashable" title="(in Python v3.12)"><span class="pre">Hashable</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference internal" href="../arch.univariate.base.ARCHModelForecast.html#arch.univariate.base.ARCHModelForecast" title="arch.univariate.base.ARCHModelForecast (Python class) — Container for forecasts from an ARCH Model"><span class="pre">ARCHModelForecast</span></a></span></span></span><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.forecast" title="Link to this definition">¶</a></dt>
 <dd><p>Construct forecasts from estimated model</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.base.ARCHModelResult.forecast-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.forecast-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.base.ARCHModelResult.forecast.params"><span class="n sig-name">params</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.forecast.params" title="Permalink to this definition">¶</a></dt><dd><p>Alternative parameters to use.  If not provided, the parameters
+<dt id="arch.univariate.base.ARCHModelResult.forecast.params"><span class="n sig-name">params</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.forecast.params" title="Permalink to this definition">¶</a></dt><dd><p>Alternative parameters to use.  If not provided, the parameters
 estimated when fitting the model are used.  Must be identical in
 shape to the parameters computed by fitting the model.</p>
 </dd>
 <dt id="arch.univariate.base.ARCHModelResult.forecast.horizon"><span class="n sig-name">horizon</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.forecast.horizon" title="Permalink to this definition">¶</a></dt><dd><p>Number of steps to forecast</p>
 </dd>
-<dt id="arch.univariate.base.ARCHModelResult.forecast.start"><span class="n sig-name">start</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)">str</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.12)">datetime</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)">datetime64</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.1)">Timestamp</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.forecast.start" title="Permalink to this definition">¶</a></dt><dd><p>An integer, datetime or str indicating the first observation to
+<dt id="arch.univariate.base.ARCHModelResult.forecast.start"><span class="n sig-name">start</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)">str</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.12)">datetime</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)">datetime64</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.4)">Timestamp</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.forecast.start" title="Permalink to this definition">¶</a></dt><dd><p>An integer, datetime or str indicating the first observation to
 produce the forecast for.  Datetimes can only be used with pandas
 inputs that have a datetime index. Strings must be convertible
 to a date time, such as in ‘1945-01-01’.</p>
@@ -1437,7 +1437,7 @@ <h1 id="arch-univariate-base-archmodelresult-forecast">arch.univariate.base.ARCH
 </div>
 </p>
 </dd>
-<dt id="arch.univariate.base.ARCHModelResult.forecast.x"><span class="n sig-name">x</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)">dict</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Hashable" title="(in Python v3.12)">Hashable</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.forecast.x" title="Permalink to this definition">¶</a></dt><dd><p><p>Values to use for exogenous regressors if any are included in the
+<dt id="arch.univariate.base.ARCHModelResult.forecast.x"><span class="n sig-name">x</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#dict" title="(in Python v3.12)">dict</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Hashable" title="(in Python v3.12)">Hashable</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a><span class="p">,</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.forecast.x" title="Permalink to this definition">¶</a></dt><dd><p><p>Values to use for exogenous regressors if any are included in the
 model. Three formats are accepted:</p>
 <ul class="simple">
 <li><p>2-d array-like: This format can be used when there is a single
@@ -1608,7 +1608,7 @@ <h1 id="arch-univariate-base-archmodelresult-forecast">arch.univariate.base.ARCH
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 <h1 id="arch-univariate-base-archmodelresult-hedgehog-plot">arch.univariate.base.ARCHModelResult.hedgehog_plot<a class="headerlink" href="#arch-univariate-base-archmodelresult-hedgehog-plot" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.base.ARCHModelResult.hedgehog_plot">
-<span class="sig-prename descclassname"><span class="pre">ARCHModelResult.</span></span><span class="sig-name descname"><span class="pre">hedgehog_plot</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelResult.hedgehog_plot.params" title="arch.univariate.base.ARCHModelResult.hedgehog_plot.params (Python parameter) — Alternative parameters to use.  If not provided, the parameters computed by fitting the model are used.  Must be 1-d and identical in shape to the parameters computed by fitting the model."><span class="n"><span class="pre">params</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelResult.hedgehog_plot.horizon" title="arch.univariate.base.ARCHModelResult.hedgehog_plot.horizon (Python parameter) — Number of steps to forecast"><span class="n"><span class="pre">horizon</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">10</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelResult.hedgehog_plot.step" title="arch.univariate.base.ARCHModelResult.hedgehog_plot.step (Python parameter) — Non-negative number of forecasts to skip between spines"><span class="n"><span class="pre">step</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">10</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelResult.hedgehog_plot.start" title="arch.univariate.base.ARCHModelResult.hedgehog_plot.start (Python parameter) — An integer, datetime or str indicating the first observation to produce the forecast for.  Datetimes can only be used with pandas inputs that have a datetime index.  Strings must be convertible to a date time, such as in '1945-01-01'.  If not provided, the start is set to the earliest forecastable date."><span class="n"><span class="pre">start</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.12)"><span class="pre">datetime</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)"><span class="pre">datetime64</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.1)"><span class="pre">Timestamp</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelResult.hedgehog_plot.plot_type" title="arch.univariate.base.ARCHModelResult.hedgehog_plot.plot_type (Python parameter) — Quantity to plot, the forecast volatility or the forecast mean"><span class="n"><span class="pre">plot_type</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'volatility'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'mean'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;volatility&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelResult.hedgehog_plot.method" title="arch.univariate.base.ARCHModelResult.hedgehog_plot.method (Python parameter) — Method to use when producing the forecast. The default is analytic. The method only affects the variance forecast generation.  Not all volatility models support all methods. In particular, volatility models that do not evolve in squares such as EGARCH or TARCH do not support the 'analytic' method for horizons &gt; 1."><span class="n"><span class="pre">method</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'analytic'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'simulation'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'bootstrap'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;analytic&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelResult.hedgehog_plot.simulations" title="arch.univariate.base.ARCHModelResult.hedgehog_plot.simulations (Python parameter) — Number of simulations to run when computing the forecast using either simulation or bootstrap."><span class="n"><span class="pre">simulations</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://matplotlib.org/stable/api/figure_api.html#matplotlib.figure.Figure" title="(in Matplotlib v3.8.0)"><span class="pre">Figure</span></a></span></span></span><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.hedgehog_plot" title="Link to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">ARCHModelResult.</span></span><span class="sig-name descname"><span class="pre">hedgehog_plot</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelResult.hedgehog_plot.params" title="arch.univariate.base.ARCHModelResult.hedgehog_plot.params (Python parameter) — Alternative parameters to use.  If not provided, the parameters computed by fitting the model are used.  Must be 1-d and identical in shape to the parameters computed by fitting the model."><span class="n"><span class="pre">params</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelResult.hedgehog_plot.horizon" title="arch.univariate.base.ARCHModelResult.hedgehog_plot.horizon (Python parameter) — Number of steps to forecast"><span class="n"><span class="pre">horizon</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">10</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelResult.hedgehog_plot.step" title="arch.univariate.base.ARCHModelResult.hedgehog_plot.step (Python parameter) — Non-negative number of forecasts to skip between spines"><span class="n"><span class="pre">step</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">10</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelResult.hedgehog_plot.start" title="arch.univariate.base.ARCHModelResult.hedgehog_plot.start (Python parameter) — An integer, datetime or str indicating the first observation to produce the forecast for.  Datetimes can only be used with pandas inputs that have a datetime index.  Strings must be convertible to a date time, such as in '1945-01-01'.  If not provided, the start is set to the earliest forecastable date."><span class="n"><span class="pre">start</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.12)"><span class="pre">datetime</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)"><span class="pre">datetime64</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.4)"><span class="pre">Timestamp</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelResult.hedgehog_plot.plot_type" title="arch.univariate.base.ARCHModelResult.hedgehog_plot.plot_type (Python parameter) — Quantity to plot, the forecast volatility or the forecast mean"><span class="n"><span class="pre">plot_type</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'volatility'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'mean'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;volatility&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelResult.hedgehog_plot.method" title="arch.univariate.base.ARCHModelResult.hedgehog_plot.method (Python parameter) — Method to use when producing the forecast. The default is analytic. The method only affects the variance forecast generation.  Not all volatility models support all methods. In particular, volatility models that do not evolve in squares such as EGARCH or TARCH do not support the 'analytic' method for horizons &gt; 1."><span class="n"><span class="pre">method</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'analytic'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'simulation'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'bootstrap'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;analytic&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelResult.hedgehog_plot.simulations" title="arch.univariate.base.ARCHModelResult.hedgehog_plot.simulations (Python parameter) — Number of simulations to run when computing the forecast using either simulation or bootstrap."><span class="n"><span class="pre">simulations</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://matplotlib.org/stable/api/figure_api.html#matplotlib.figure.Figure" title="(in Matplotlib v3.8.2)"><span class="pre">Figure</span></a></span></span></span><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.hedgehog_plot" title="Link to this definition">¶</a></dt>
 <dd><p>Plot forecasts from estimated model</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.base.ARCHModelResult.hedgehog_plot-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.hedgehog_plot-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.base.ARCHModelResult.hedgehog_plot.params"><span class="n sig-name">params</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.hedgehog_plot.params" title="Permalink to this definition">¶</a></dt><dd><p>Alternative parameters to use.  If not provided, the parameters
+<dt id="arch.univariate.base.ARCHModelResult.hedgehog_plot.params"><span class="n sig-name">params</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.hedgehog_plot.params" title="Permalink to this definition">¶</a></dt><dd><p>Alternative parameters to use.  If not provided, the parameters
 computed by fitting the model are used.  Must be 1-d and identical
 in shape to the parameters computed by fitting the model.</p>
 </dd>
@@ -1363,7 +1363,7 @@ <h1 id="arch-univariate-base-archmodelresult-hedgehog-plot">arch.univariate.base
 </dd>
 <dt id="arch.univariate.base.ARCHModelResult.hedgehog_plot.step"><span class="n sig-name">step</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">10</span></code><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.hedgehog_plot.step" title="Permalink to this definition">¶</a></dt><dd><p>Non-negative number of forecasts to skip between spines</p>
 </dd>
-<dt id="arch.univariate.base.ARCHModelResult.hedgehog_plot.start"><span class="n sig-name">start</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)">str</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.12)">datetime</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)">datetime64</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.1)">Timestamp</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.hedgehog_plot.start" title="Permalink to this definition">¶</a></dt><dd><p>An integer, datetime or str indicating the first observation to
+<dt id="arch.univariate.base.ARCHModelResult.hedgehog_plot.start"><span class="n sig-name">start</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)">str</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/datetime.html#datetime.datetime" title="(in Python v3.12)">datetime</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/arrays.scalars.html#numpy.datetime64" title="(in NumPy v1.26)">datetime64</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Timestamp.html#pandas.Timestamp" title="(in pandas v2.1.4)">Timestamp</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.hedgehog_plot.start" title="Permalink to this definition">¶</a></dt><dd><p>An integer, datetime or str indicating the first observation to
 produce the forecast for.  Datetimes can only be used with pandas
 inputs that have a datetime index.  Strings must be convertible
 to a date time, such as in ‘1945-01-01’.  If not provided, the start
@@ -1516,7 +1516,7 @@ <h1 id="arch-univariate-base-archmodelresult-hedgehog-plot">arch.univariate.base
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/devel/univariate/generated/generated/arch.univariate.base.ARCHModelResult.loglikelihood.html b/devel/univariate/generated/generated/arch.univariate.base.ARCHModelResult.loglikelihood.html
index de07e430db..a6a99f799f 100644
--- a/devel/univariate/generated/generated/arch.univariate.base.ARCHModelResult.loglikelihood.html
+++ b/devel/univariate/generated/generated/arch.univariate.base.ARCHModelResult.loglikelihood.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.base.ARCHModelResult.loglikelihood - arch v6.2.1 (+21)</title>
+        <title>arch.univariate.base.ARCHModelResult.loglikelihood - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -1304,7 +1304,7 @@ <h1 id="arch-univariate-base-archmodelresult-loglikelihood">arch.univariate.base
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/devel/univariate/generated/generated/arch.univariate.base.ARCHModelResult.model.html b/devel/univariate/generated/generated/arch.univariate.base.ARCHModelResult.model.html
index 4077397369..c994af53fa 100644
--- a/devel/univariate/generated/generated/arch.univariate.base.ARCHModelResult.model.html
+++ b/devel/univariate/generated/generated/arch.univariate.base.ARCHModelResult.model.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.base.ARCHModelResult.model - arch v6.2.1 (+21)</title>
+        <title>arch.univariate.base.ARCHModelResult.model - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -1304,7 +1304,7 @@ <h1 id="arch-univariate-base-archmodelresult-model">arch.univariate.base.ARCHMod
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-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/devel/univariate/generated/generated/arch.univariate.base.ARCHModelResult.nobs.html b/devel/univariate/generated/generated/arch.univariate.base.ARCHModelResult.nobs.html
index 7ad07ca39a..041a9c32bf 100644
--- a/devel/univariate/generated/generated/arch.univariate.base.ARCHModelResult.nobs.html
+++ b/devel/univariate/generated/generated/arch.univariate.base.ARCHModelResult.nobs.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.base.ARCHModelResult.nobs - arch v6.2.1 (+21)</title>
+        <title>arch.univariate.base.ARCHModelResult.nobs - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -1304,7 +1304,7 @@ <h1 id="arch-univariate-base-archmodelresult-nobs">arch.univariate.base.ARCHMode
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
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diff --git a/devel/univariate/generated/generated/arch.univariate.base.ARCHModelResult.num_params.html b/devel/univariate/generated/generated/arch.univariate.base.ARCHModelResult.num_params.html
index 18f14ae716..7337675efd 100644
--- a/devel/univariate/generated/generated/arch.univariate.base.ARCHModelResult.num_params.html
+++ b/devel/univariate/generated/generated/arch.univariate.base.ARCHModelResult.num_params.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.base.ARCHModelResult.num_params - arch v6.2.1 (+21)</title>
+        <title>arch.univariate.base.ARCHModelResult.num_params - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -1304,7 +1304,7 @@ <h1 id="arch-univariate-base-archmodelresult-num-params">arch.univariate.base.AR
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/devel/univariate/generated/generated/arch.univariate.base.ARCHModelResult.optimization_result.html b/devel/univariate/generated/generated/arch.univariate.base.ARCHModelResult.optimization_result.html
index de2816a9fc..9fe7fdfd8f 100644
--- a/devel/univariate/generated/generated/arch.univariate.base.ARCHModelResult.optimization_result.html
+++ b/devel/univariate/generated/generated/arch.univariate.base.ARCHModelResult.optimization_result.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.base.ARCHModelResult.optimization_result - arch v6.2.1 (+21)</title>
+        <title>arch.univariate.base.ARCHModelResult.optimization_result - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -1226,7 +1226,7 @@
 <h1 id="arch-univariate-base-archmodelresult-optimization-result">arch.univariate.base.ARCHModelResult.optimization_result<a class="headerlink" href="#arch-univariate-base-archmodelresult-optimization-result" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.base.ARCHModelResult.optimization_result">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARCHModelResult.</span></span><span class="sig-name descname"><span class="pre">optimization_result</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.OptimizeResult.html#scipy.optimize.OptimizeResult" title="(in SciPy v1.11.3)"><span class="pre">OptimizeResult</span></a></span><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.optimization_result" title="Link to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARCHModelResult.</span></span><span class="sig-name descname"><span class="pre">optimization_result</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.OptimizeResult.html#scipy.optimize.OptimizeResult" title="(in SciPy v1.11.4)"><span class="pre">OptimizeResult</span></a></span><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.optimization_result" title="Link to this definition">¶</a></dt>
 <dd><p>Information about the convergence of the loglikelihood optimization</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.base.ARCHModelResult.optimization_result-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.optimization_result-returns" title="Permalink to this headline">¶</a></dt>
@@ -1352,7 +1352,7 @@ <h1 id="arch-univariate-base-archmodelresult-optimization-result">arch.univariat
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/devel/univariate/generated/generated/arch.univariate.base.ARCHModelResult.param_cov.html b/devel/univariate/generated/generated/arch.univariate.base.ARCHModelResult.param_cov.html
index 776c9ff581..f7428ffd89 100644
--- a/devel/univariate/generated/generated/arch.univariate.base.ARCHModelResult.param_cov.html
+++ b/devel/univariate/generated/generated/arch.univariate.base.ARCHModelResult.param_cov.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.base.ARCHModelResult.param_cov - arch v6.2.1 (+21)</title>
+        <title>arch.univariate.base.ARCHModelResult.param_cov - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -1186,7 +1186,7 @@
 <h1 id="arch-univariate-base-archmodelresult-param-cov">arch.univariate.base.ARCHModelResult.param_cov<a class="headerlink" href="#arch-univariate-base-archmodelresult-param-cov" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.base.ARCHModelResult.param_cov">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARCHModelResult.</span></span><span class="sig-name descname"><span class="pre">param_cov</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a></span><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.param_cov" title="Link to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARCHModelResult.</span></span><span class="sig-name descname"><span class="pre">param_cov</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><span class="pre">DataFrame</span></a></span><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.param_cov" title="Link to this definition">¶</a></dt>
 <dd><p>Parameter covariance</p>
 </dd></dl>
 
@@ -1304,7 +1304,7 @@ <h1 id="arch-univariate-base-archmodelresult-param-cov">arch.univariate.base.ARC
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/devel/univariate/generated/generated/arch.univariate.base.ARCHModelResult.params.html b/devel/univariate/generated/generated/arch.univariate.base.ARCHModelResult.params.html
index c323124359..81222aad79 100644
--- a/devel/univariate/generated/generated/arch.univariate.base.ARCHModelResult.params.html
+++ b/devel/univariate/generated/generated/arch.univariate.base.ARCHModelResult.params.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.base.ARCHModelResult.params - arch v6.2.1 (+21)</title>
+        <title>arch.univariate.base.ARCHModelResult.params - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
     <script>__md_scope=new URL("../../..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
     
@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -1186,7 +1186,7 @@
 <h1 id="arch-univariate-base-archmodelresult-params">arch.univariate.base.ARCHModelResult.params<a class="headerlink" href="#arch-univariate-base-archmodelresult-params" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.base.ARCHModelResult.params">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARCHModelResult.</span></span><span class="sig-name descname"><span class="pre">params</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.params" title="Link to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARCHModelResult.</span></span><span class="sig-name descname"><span class="pre">params</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a></span><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.params" title="Link to this definition">¶</a></dt>
 <dd><p>Model Parameters</p>
 </dd></dl>
 
@@ -1304,7 +1304,7 @@ <h1 id="arch-univariate-base-archmodelresult-params">arch.univariate.base.ARCHMo
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
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diff --git a/devel/univariate/generated/generated/arch.univariate.base.ARCHModelResult.plot.html b/devel/univariate/generated/generated/arch.univariate.base.ARCHModelResult.plot.html
index ac95079a34..05e230dfb7 100644
--- a/devel/univariate/generated/generated/arch.univariate.base.ARCHModelResult.plot.html
+++ b/devel/univariate/generated/generated/arch.univariate.base.ARCHModelResult.plot.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.base.ARCHModelResult.plot - arch v6.2.1 (+21)</title>
+        <title>arch.univariate.base.ARCHModelResult.plot - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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@@ -189,10 +189,10 @@
 
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+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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@@ -1280,7 +1280,7 @@
 <h1 id="arch-univariate-base-archmodelresult-plot">arch.univariate.base.ARCHModelResult.plot<a class="headerlink" href="#arch-univariate-base-archmodelresult-plot" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.base.ARCHModelResult.plot">
-<span class="sig-prename descclassname"><span class="pre">ARCHModelResult.</span></span><span class="sig-name descname"><span class="pre">plot</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelResult.plot.annualize" title="arch.univariate.base.ARCHModelResult.plot.annualize (Python parameter) — String containing frequency of data that indicates plot should contain annualized volatility.  Supported values are 'D' (daily), 'W' (weekly) and 'M' (monthly), which scale variance by 252, 52, and 12, respectively."><span class="n"><span class="pre">annualize</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelResult.plot.scale" title="arch.univariate.base.ARCHModelResult.plot.scale (Python parameter) — Value to use when scaling returns to annualize.  If scale is provided, annualize is ignored and the value in scale is used."><span class="n"><span class="pre">scale</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://matplotlib.org/stable/api/figure_api.html#matplotlib.figure.Figure" title="(in Matplotlib v3.8.0)"><span class="pre">Figure</span></a></span></span></span><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.plot" title="Link to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">ARCHModelResult.</span></span><span class="sig-name descname"><span class="pre">plot</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelResult.plot.annualize" title="arch.univariate.base.ARCHModelResult.plot.annualize (Python parameter) — String containing frequency of data that indicates plot should contain annualized volatility.  Supported values are 'D' (daily), 'W' (weekly) and 'M' (monthly), which scale variance by 252, 52, and 12, respectively."><span class="n"><span class="pre">annualize</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#str" title="(in Python v3.12)"><span class="pre">str</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.base.ARCHModelResult.plot.scale" title="arch.univariate.base.ARCHModelResult.plot.scale (Python parameter) — Value to use when scaling returns to annualize.  If scale is provided, annualize is ignored and the value in scale is used."><span class="n"><span class="pre">scale</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://matplotlib.org/stable/api/figure_api.html#matplotlib.figure.Figure" title="(in Matplotlib v3.8.2)"><span class="pre">Figure</span></a></span></span></span><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.plot" title="Link to this definition">¶</a></dt>
 <dd><p>Plot standardized residuals and conditional volatility</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.base.ARCHModelResult.plot-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.plot-parameters" title="Permalink to this headline">¶</a></dt>
@@ -1436,7 +1436,7 @@ <h1 id="arch-univariate-base-archmodelresult-plot">arch.univariate.base.ARCHMode
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   </body>
diff --git a/devel/univariate/generated/generated/arch.univariate.base.ARCHModelResult.pvalues.html b/devel/univariate/generated/generated/arch.univariate.base.ARCHModelResult.pvalues.html
index 2add08357c..e004a81c05 100644
--- a/devel/univariate/generated/generated/arch.univariate.base.ARCHModelResult.pvalues.html
+++ b/devel/univariate/generated/generated/arch.univariate.base.ARCHModelResult.pvalues.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.base.ARCHModelResult.pvalues - arch v6.2.1 (+21)</title>
+        <title>arch.univariate.base.ARCHModelResult.pvalues - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
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@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
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   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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     <div class="md-nav__source">
@@ -1186,7 +1186,7 @@
 <h1 id="arch-univariate-base-archmodelresult-pvalues">arch.univariate.base.ARCHModelResult.pvalues<a class="headerlink" href="#arch-univariate-base-archmodelresult-pvalues" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.base.ARCHModelResult.pvalues">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARCHModelResult.</span></span><span class="sig-name descname"><span class="pre">pvalues</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.pvalues" title="Link to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARCHModelResult.</span></span><span class="sig-name descname"><span class="pre">pvalues</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a></span><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.pvalues" title="Link to this definition">¶</a></dt>
 <dd><p>Array of p-values for the t-statistics</p>
 </dd></dl>
 
@@ -1304,7 +1304,7 @@ <h1 id="arch-univariate-base-archmodelresult-pvalues">arch.univariate.base.ARCHM
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diff --git a/devel/univariate/generated/generated/arch.univariate.base.ARCHModelResult.resid.html b/devel/univariate/generated/generated/arch.univariate.base.ARCHModelResult.resid.html
index 180f51d39e..3f8986ba28 100644
--- a/devel/univariate/generated/generated/arch.univariate.base.ARCHModelResult.resid.html
+++ b/devel/univariate/generated/generated/arch.univariate.base.ARCHModelResult.resid.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.base.ARCHModelResult.resid - arch v6.2.1 (+21)</title>
+        <title>arch.univariate.base.ARCHModelResult.resid - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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     <div class="md-nav__source">
@@ -1186,7 +1186,7 @@
 <h1 id="arch-univariate-base-archmodelresult-resid">arch.univariate.base.ARCHModelResult.resid<a class="headerlink" href="#arch-univariate-base-archmodelresult-resid" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.base.ARCHModelResult.resid">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARCHModelResult.</span></span><span class="sig-name descname"><span class="pre">resid</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.resid" title="Link to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARCHModelResult.</span></span><span class="sig-name descname"><span class="pre">resid</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a></span><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.resid" title="Link to this definition">¶</a></dt>
 <dd><p>Model residuals</p>
 </dd></dl>
 
@@ -1304,7 +1304,7 @@ <h1 id="arch-univariate-base-archmodelresult-resid">arch.univariate.base.ARCHMod
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
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   </body>
diff --git a/devel/univariate/generated/generated/arch.univariate.base.ARCHModelResult.rsquared.html b/devel/univariate/generated/generated/arch.univariate.base.ARCHModelResult.rsquared.html
index 7e2a21ca12..e2f2a1a93a 100644
--- a/devel/univariate/generated/generated/arch.univariate.base.ARCHModelResult.rsquared.html
+++ b/devel/univariate/generated/generated/arch.univariate.base.ARCHModelResult.rsquared.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.base.ARCHModelResult.rsquared - arch v6.2.1 (+21)</title>
+        <title>arch.univariate.base.ARCHModelResult.rsquared - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -1304,7 +1304,7 @@ <h1 id="arch-univariate-base-archmodelresult-rsquared">arch.univariate.base.ARCH
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
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diff --git a/devel/univariate/generated/generated/arch.univariate.base.ARCHModelResult.rsquared_adj.html b/devel/univariate/generated/generated/arch.univariate.base.ARCHModelResult.rsquared_adj.html
index 2418e7e608..9104f4286b 100644
--- a/devel/univariate/generated/generated/arch.univariate.base.ARCHModelResult.rsquared_adj.html
+++ b/devel/univariate/generated/generated/arch.univariate.base.ARCHModelResult.rsquared_adj.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.base.ARCHModelResult.rsquared_adj - arch v6.2.1 (+21)</title>
+        <title>arch.univariate.base.ARCHModelResult.rsquared_adj - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -1304,7 +1304,7 @@ <h1 id="arch-univariate-base-archmodelresult-rsquared-adj">arch.univariate.base.
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
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   </body>
diff --git a/devel/univariate/generated/generated/arch.univariate.base.ARCHModelResult.scale.html b/devel/univariate/generated/generated/arch.univariate.base.ARCHModelResult.scale.html
index 8dcf9634b9..aaa8d1a372 100644
--- a/devel/univariate/generated/generated/arch.univariate.base.ARCHModelResult.scale.html
+++ b/devel/univariate/generated/generated/arch.univariate.base.ARCHModelResult.scale.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.base.ARCHModelResult.scale - arch v6.2.1 (+21)</title>
+        <title>arch.univariate.base.ARCHModelResult.scale - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -1306,7 +1306,7 @@ <h1 id="arch-univariate-base-archmodelresult-scale">arch.univariate.base.ARCHMod
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
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   </body>
diff --git a/devel/univariate/generated/generated/arch.univariate.base.ARCHModelResult.std_err.html b/devel/univariate/generated/generated/arch.univariate.base.ARCHModelResult.std_err.html
index 9b6d107714..26d7eb0aa2 100644
--- a/devel/univariate/generated/generated/arch.univariate.base.ARCHModelResult.std_err.html
+++ b/devel/univariate/generated/generated/arch.univariate.base.ARCHModelResult.std_err.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.base.ARCHModelResult.std_err - arch v6.2.1 (+21)</title>
+        <title>arch.univariate.base.ARCHModelResult.std_err - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
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@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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     <div class="md-nav__source">
@@ -1186,7 +1186,7 @@
 <h1 id="arch-univariate-base-archmodelresult-std-err">arch.univariate.base.ARCHModelResult.std_err<a class="headerlink" href="#arch-univariate-base-archmodelresult-std-err" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.base.ARCHModelResult.std_err">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARCHModelResult.</span></span><span class="sig-name descname"><span class="pre">std_err</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.std_err" title="Link to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARCHModelResult.</span></span><span class="sig-name descname"><span class="pre">std_err</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a></span><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.std_err" title="Link to this definition">¶</a></dt>
 <dd><p>Array of parameter standard errors</p>
 </dd></dl>
 
@@ -1304,7 +1304,7 @@ <h1 id="arch-univariate-base-archmodelresult-std-err">arch.univariate.base.ARCHM
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diff --git a/devel/univariate/generated/generated/arch.univariate.base.ARCHModelResult.std_resid.html b/devel/univariate/generated/generated/arch.univariate.base.ARCHModelResult.std_resid.html
index 90de7dd17a..3b9c3217ec 100644
--- a/devel/univariate/generated/generated/arch.univariate.base.ARCHModelResult.std_resid.html
+++ b/devel/univariate/generated/generated/arch.univariate.base.ARCHModelResult.std_resid.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.base.ARCHModelResult.std_resid - arch v6.2.1 (+21)</title>
+        <title>arch.univariate.base.ARCHModelResult.std_resid - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
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@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
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-    arch v6.2.1 (+21)
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@@ -1186,7 +1186,7 @@
 <h1 id="arch-univariate-base-archmodelresult-std-resid">arch.univariate.base.ARCHModelResult.std_resid<a class="headerlink" href="#arch-univariate-base-archmodelresult-std-resid" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.base.ARCHModelResult.std_resid">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARCHModelResult.</span></span><span class="sig-name descname"><span class="pre">std_resid</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.std_resid" title="Link to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARCHModelResult.</span></span><span class="sig-name descname"><span class="pre">std_resid</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a></span><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.std_resid" title="Link to this definition">¶</a></dt>
 <dd><p>Residuals standardized by conditional volatility</p>
 </dd></dl>
 
@@ -1304,7 +1304,7 @@ <h1 id="arch-univariate-base-archmodelresult-std-resid">arch.univariate.base.ARC
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diff --git a/devel/univariate/generated/generated/arch.univariate.base.ARCHModelResult.summary.html b/devel/univariate/generated/generated/arch.univariate.base.ARCHModelResult.summary.html
index 800e2a4e36..2b45141f07 100644
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+++ b/devel/univariate/generated/generated/arch.univariate.base.ARCHModelResult.summary.html
@@ -15,7 +15,7 @@
     
   
       
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@@ -47,7 +47,7 @@
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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@@ -102,7 +102,7 @@
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           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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@@ -1226,7 +1226,7 @@
 <h1 id="arch-univariate-base-archmodelresult-summary">arch.univariate.base.ARCHModelResult.summary<a class="headerlink" href="#arch-univariate-base-archmodelresult-summary" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.base.ARCHModelResult.summary">
-<span class="sig-prename descclassname"><span class="pre">ARCHModelResult.</span></span><span class="sig-name descname"><span class="pre">summary</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://www.statsmodels.org/dev/generated/statsmodels.iolib.summary.Summary.html#statsmodels.iolib.summary.Summary" title="(in statsmodels 0.15.0 (+59) v0.15.0 (+59))"><span class="pre">Summary</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/base.html#ARCHModelResult.summary"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.summary" title="Link to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">ARCHModelResult.</span></span><span class="sig-name descname"><span class="pre">summary</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://www.statsmodels.org/dev/generated/statsmodels.iolib.summary.Summary.html#statsmodels.iolib.summary.Summary" title="(in statsmodels 0.15.0 (+132) v0.15.0 (+132))"><span class="pre">Summary</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/base.html#ARCHModelResult.summary"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.summary" title="Link to this definition">¶</a></dt>
 <dd><p>Constructs a summary of the results from a fit model.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.base.ARCHModelResult.summary-returns">Returns<span class="colon">:</span><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.summary-returns" title="Permalink to this headline">¶</a></dt>
@@ -1353,7 +1353,7 @@ <h1 id="arch-univariate-base-archmodelresult-summary">arch.univariate.base.ARCHM
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diff --git a/devel/univariate/generated/generated/arch.univariate.base.ARCHModelResult.tvalues.html b/devel/univariate/generated/generated/arch.univariate.base.ARCHModelResult.tvalues.html
index 5726a2e9f9..b0c80e2719 100644
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+++ b/devel/univariate/generated/generated/arch.univariate.base.ARCHModelResult.tvalues.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.base.ARCHModelResult.tvalues - arch v6.2.1 (+21)</title>
+        <title>arch.univariate.base.ARCHModelResult.tvalues - arch v6.2.1 (+23)</title>
       
     
 
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@@ -92,7 +92,7 @@
 
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+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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@@ -1186,7 +1186,7 @@
 <h1 id="arch-univariate-base-archmodelresult-tvalues">arch.univariate.base.ARCHModelResult.tvalues<a class="headerlink" href="#arch-univariate-base-archmodelresult-tvalues" title="Link to this heading">¶</a></h1>
 <dl class="py property objdesc">
 <dt class="sig sig-object highlight py" id="arch.univariate.base.ARCHModelResult.tvalues">
-<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARCHModelResult.</span></span><span class="sig-name descname"><span class="pre">tvalues</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.tvalues" title="Link to this definition">¶</a></dt>
+<em class="property"><span class="pre">property</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ARCHModelResult.</span></span><span class="sig-name descname"><span class="pre">tvalues</span></span><span class="p"> <span class="pre">:</span> </span><span class="desctype"><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a></span><a class="headerlink" href="#arch.univariate.base.ARCHModelResult.tvalues" title="Link to this definition">¶</a></dt>
 <dd><p>Array of t-statistics testing the null that the coefficient are 0</p>
 </dd></dl>
 
@@ -1304,7 +1304,7 @@ <h1 id="arch-univariate-base-archmodelresult-tvalues">arch.univariate.base.ARCHM
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diff --git a/devel/univariate/generated/generated/arch.univariate.distribution.Distribution.bounds.html b/devel/univariate/generated/generated/arch.univariate.distribution.Distribution.bounds.html
index ad10983374..a476bd8e49 100644
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@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.distribution.Distribution.bounds - arch v6.2.1 (+21)</title>
+        <title>arch.univariate.distribution.Distribution.bounds - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
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diff --git a/devel/univariate/generated/generated/arch.univariate.distribution.Distribution.cdf.html b/devel/univariate/generated/generated/arch.univariate.distribution.Distribution.cdf.html
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 <h1 id="arch-univariate-distribution-distribution-cdf">arch.univariate.distribution.Distribution.cdf<a class="headerlink" href="#arch-univariate-distribution-distribution-cdf" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.distribution.Distribution.cdf">
-<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">Distribution.</span></span><span class="sig-name descname"><span class="pre">cdf</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.distribution.Distribution.cdf.resids" title="arch.univariate.distribution.Distribution.cdf.resids (Python parameter) — Values at which to evaluate the cdf"><span class="n"><span class="pre">resids</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.distribution.Distribution.cdf.parameters" title="arch.univariate.distribution.Distribution.cdf.parameters (Python parameter) — Distribution parameters. Use None for parameterless distributions."><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/distribution.html#Distribution.cdf"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.distribution.Distribution.cdf" title="Link to this definition">¶</a></dt>
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">Distribution.</span></span><span class="sig-name descname"><span class="pre">cdf</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.distribution.Distribution.cdf.resids" title="arch.univariate.distribution.Distribution.cdf.resids (Python parameter) — Values at which to evaluate the cdf"><span class="n"><span class="pre">resids</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.distribution.Distribution.cdf.parameters" title="arch.univariate.distribution.Distribution.cdf.parameters (Python parameter) — Distribution parameters. Use None for parameterless distributions."><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/distribution.html#Distribution.cdf"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.distribution.Distribution.cdf" title="Link to this definition">¶</a></dt>
 <dd><p>Cumulative distribution function</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.distribution.Distribution.cdf-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.distribution.Distribution.cdf-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.distribution.Distribution.cdf.resids"><span class="n sig-name">resids</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.univariate.distribution.Distribution.cdf.resids" title="Permalink to this definition">¶</a></dt><dd><p>Values at which to evaluate the cdf</p>
+<dt id="arch.univariate.distribution.Distribution.cdf.resids"><span class="n sig-name">resids</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a></span></span><a class="headerlink" href="#arch.univariate.distribution.Distribution.cdf.resids" title="Permalink to this definition">¶</a></dt><dd><p>Values at which to evaluate the cdf</p>
 </dd>
-<dt id="arch.univariate.distribution.Distribution.cdf.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.distribution.Distribution.cdf.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Distribution parameters. Use <code class="docutils literal notranslate"><span class="pre">None</span></code> for parameterless
+<dt id="arch.univariate.distribution.Distribution.cdf.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.distribution.Distribution.cdf.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Distribution parameters. Use <code class="docutils literal notranslate"><span class="pre">None</span></code> for parameterless
 distributions.</p>
 </dd>
 </dl>
@@ -1290,7 +1290,7 @@ <h1 id="arch-univariate-distribution-distribution-cdf">arch.univariate.distribut
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diff --git a/devel/univariate/generated/generated/arch.univariate.distribution.Distribution.constraints.html b/devel/univariate/generated/generated/arch.univariate.distribution.Distribution.constraints.html
index e80b805cca..0dcc66bd56 100644
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@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
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+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
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   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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@@ -1216,7 +1216,7 @@ <h1 id="arch-univariate-distribution-distribution-constraints">arch.univariate.d
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diff --git a/devel/univariate/generated/generated/arch.univariate.distribution.Distribution.generator.html b/devel/univariate/generated/generated/arch.univariate.distribution.Distribution.generator.html
index 04ff8dca91..a7906a0aff 100644
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+++ b/devel/univariate/generated/generated/arch.univariate.distribution.Distribution.generator.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.distribution.Distribution.generator - arch v6.2.1 (+21)</title>
+        <title>arch.univariate.distribution.Distribution.generator - arch v6.2.1 (+23)</title>
       
     
 
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
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-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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-            arch v6.2.1 (+21)
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-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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@@ -1179,7 +1179,7 @@ <h1 id="arch-univariate-distribution-distribution-generator">arch.univariate.dis
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
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diff --git a/devel/univariate/generated/generated/arch.univariate.distribution.Distribution.loglikelihood.html b/devel/univariate/generated/generated/arch.univariate.distribution.Distribution.loglikelihood.html
index b48ac5b9fb..761fddf179 100644
--- a/devel/univariate/generated/generated/arch.univariate.distribution.Distribution.loglikelihood.html
+++ b/devel/univariate/generated/generated/arch.univariate.distribution.Distribution.loglikelihood.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.distribution.Distribution.loglikelihood - arch v6.2.1 (+21)</title>
+        <title>arch.univariate.distribution.Distribution.loglikelihood - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.2.1 (+21)
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@@ -189,10 +189,10 @@
 
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+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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 <h1 id="arch-univariate-distribution-distribution-loglikelihood">arch.univariate.distribution.Distribution.loglikelihood<a class="headerlink" href="#arch-univariate-distribution-distribution-loglikelihood" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.distribution.Distribution.loglikelihood">
-<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">Distribution.</span></span><span class="sig-name descname"><span class="pre">loglikelihood</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.distribution.Distribution.loglikelihood.parameters" title="arch.univariate.distribution.Distribution.loglikelihood.parameters (Python parameter) — Distribution shape parameters"><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.distribution.Distribution.loglikelihood.resids" title="arch.univariate.distribution.Distribution.loglikelihood.resids (Python parameter) — nobs array of model residuals"><span class="n"><span class="pre">resids</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.distribution.Distribution.loglikelihood.sigma2" title="arch.univariate.distribution.Distribution.loglikelihood.sigma2 (Python parameter) — nobs array of conditional variances"><span class="n"><span class="pre">sigma2</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.distribution.Distribution.loglikelihood.individual" title="arch.univariate.distribution.Distribution.loglikelihood.individual (Python parameter) — Flag indicating whether to return the vector of individual log likelihoods (True) or the sum (False)"><span class="n"><span class="pre">individual</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/distribution.html#Distribution.loglikelihood"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.distribution.Distribution.loglikelihood" title="Link to this definition">¶</a></dt>
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">Distribution.</span></span><span class="sig-name descname"><span class="pre">loglikelihood</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.distribution.Distribution.loglikelihood.parameters" title="arch.univariate.distribution.Distribution.loglikelihood.parameters (Python parameter) — Distribution shape parameters"><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.distribution.Distribution.loglikelihood.resids" title="arch.univariate.distribution.Distribution.loglikelihood.resids (Python parameter) — nobs array of model residuals"><span class="n"><span class="pre">resids</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.distribution.Distribution.loglikelihood.sigma2" title="arch.univariate.distribution.Distribution.loglikelihood.sigma2 (Python parameter) — nobs array of conditional variances"><span class="n"><span class="pre">sigma2</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.distribution.Distribution.loglikelihood.individual" title="arch.univariate.distribution.Distribution.loglikelihood.individual (Python parameter) — Flag indicating whether to return the vector of individual log likelihoods (True) or the sum (False)"><span class="n"><span class="pre">individual</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/distribution.html#Distribution.loglikelihood"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.distribution.Distribution.loglikelihood" title="Link to this definition">¶</a></dt>
 <dd><p>Loglikelihood evaluation.</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.distribution.Distribution.loglikelihood-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.distribution.Distribution.loglikelihood-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.distribution.Distribution.loglikelihood.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.univariate.distribution.Distribution.loglikelihood.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Distribution shape parameters</p>
+<dt id="arch.univariate.distribution.Distribution.loglikelihood.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a></span></span><a class="headerlink" href="#arch.univariate.distribution.Distribution.loglikelihood.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Distribution shape parameters</p>
 </dd>
-<dt id="arch.univariate.distribution.Distribution.loglikelihood.resids"><span class="n sig-name">resids</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.univariate.distribution.Distribution.loglikelihood.resids" title="Permalink to this definition">¶</a></dt><dd><p>nobs array of model residuals</p>
+<dt id="arch.univariate.distribution.Distribution.loglikelihood.resids"><span class="n sig-name">resids</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a></span></span><a class="headerlink" href="#arch.univariate.distribution.Distribution.loglikelihood.resids" title="Permalink to this definition">¶</a></dt><dd><p>nobs array of model residuals</p>
 </dd>
-<dt id="arch.univariate.distribution.Distribution.loglikelihood.sigma2"><span class="n sig-name">sigma2</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.univariate.distribution.Distribution.loglikelihood.sigma2" title="Permalink to this definition">¶</a></dt><dd><p>nobs array of conditional variances</p>
+<dt id="arch.univariate.distribution.Distribution.loglikelihood.sigma2"><span class="n sig-name">sigma2</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a></span></span><a class="headerlink" href="#arch.univariate.distribution.Distribution.loglikelihood.sigma2" title="Permalink to this definition">¶</a></dt><dd><p>nobs array of conditional variances</p>
 </dd>
 <dt id="arch.univariate.distribution.Distribution.loglikelihood.individual"><span class="n sig-name">individual</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)">bool</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">False</span></code><a class="headerlink" href="#arch.univariate.distribution.Distribution.loglikelihood.individual" title="Permalink to this definition">¶</a></dt><dd><p>Flag indicating whether to return the vector of individual log
 likelihoods (True) or the sum (False)</p>
@@ -1291,7 +1291,7 @@ <h1 id="arch-univariate-distribution-distribution-loglikelihood">arch.univariate
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index cebe3b8fd8..ac1d9451e5 100644
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@@ -1155,14 +1155,14 @@
 <h1 id="arch-univariate-distribution-distribution-moment">arch.univariate.distribution.Distribution.moment<a class="headerlink" href="#arch-univariate-distribution-distribution-moment" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.distribution.Distribution.moment">
-<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">Distribution.</span></span><span class="sig-name descname"><span class="pre">moment</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.distribution.Distribution.moment.n" title="arch.univariate.distribution.Distribution.moment.n (Python parameter) — Order of moment"><span class="n"><span class="pre">n</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.distribution.Distribution.moment.parameters" title="arch.univariate.distribution.Distribution.moment.parameters (Python parameter) — Distribution parameters. Use None for parameterless distributions."><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/distribution.html#Distribution.moment"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.distribution.Distribution.moment" title="Link to this definition">¶</a></dt>
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">Distribution.</span></span><span class="sig-name descname"><span class="pre">moment</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.distribution.Distribution.moment.n" title="arch.univariate.distribution.Distribution.moment.n (Python parameter) — Order of moment"><span class="n"><span class="pre">n</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.distribution.Distribution.moment.parameters" title="arch.univariate.distribution.Distribution.moment.parameters (Python parameter) — Distribution parameters. Use None for parameterless distributions."><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/distribution.html#Distribution.moment"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.distribution.Distribution.moment" title="Link to this definition">¶</a></dt>
 <dd><p>Moment of order n</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.distribution.Distribution.moment-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.distribution.Distribution.moment-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
 <dt id="arch.univariate.distribution.Distribution.moment.n"><span class="n sig-name">n</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a></span></span><a class="headerlink" href="#arch.univariate.distribution.Distribution.moment.n" title="Permalink to this definition">¶</a></dt><dd><p>Order of moment</p>
 </dd>
-<dt id="arch.univariate.distribution.Distribution.moment.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.distribution.Distribution.moment.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Distribution parameters. Use None for parameterless distributions.</p>
+<dt id="arch.univariate.distribution.Distribution.moment.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.distribution.Distribution.moment.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Distribution parameters. Use None for parameterless distributions.</p>
 </dd>
 </dl>
 </dd>
@@ -1289,7 +1289,7 @@ <h1 id="arch-univariate-distribution-distribution-moment">arch.univariate.distri
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diff --git a/devel/univariate/generated/generated/arch.univariate.distribution.Distribution.name.html b/devel/univariate/generated/generated/arch.univariate.distribution.Distribution.name.html
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diff --git a/devel/univariate/generated/generated/arch.univariate.distribution.Distribution.parameter_names.html b/devel/univariate/generated/generated/arch.univariate.distribution.Distribution.parameter_names.html
index 373bd269fb..db4a2cdb08 100644
--- a/devel/univariate/generated/generated/arch.univariate.distribution.Distribution.parameter_names.html
+++ b/devel/univariate/generated/generated/arch.univariate.distribution.Distribution.parameter_names.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.distribution.Distribution.parameter_names - arch v6.2.1 (+21)</title>
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@@ -47,7 +47,7 @@
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+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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@@ -102,7 +102,7 @@
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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@@ -1227,7 +1227,7 @@ <h1 id="arch-univariate-distribution-distribution-parameter-names">arch.univaria
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-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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diff --git a/devel/univariate/generated/generated/arch.univariate.distribution.Distribution.partial_moment.html b/devel/univariate/generated/generated/arch.univariate.distribution.Distribution.partial_moment.html
index 01686ddb2f..3c4c115f2b 100644
--- a/devel/univariate/generated/generated/arch.univariate.distribution.Distribution.partial_moment.html
+++ b/devel/univariate/generated/generated/arch.univariate.distribution.Distribution.partial_moment.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.distribution.Distribution.partial_moment - arch v6.2.1 (+21)</title>
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@@ -47,7 +47,7 @@
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+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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@@ -189,10 +189,10 @@
 
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@@ -1169,7 +1169,7 @@
 <h1 id="arch-univariate-distribution-distribution-partial-moment">arch.univariate.distribution.Distribution.partial_moment<a class="headerlink" href="#arch-univariate-distribution-distribution-partial-moment" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.distribution.Distribution.partial_moment">
-<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">Distribution.</span></span><span class="sig-name descname"><span class="pre">partial_moment</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.distribution.Distribution.partial_moment.n" title="arch.univariate.distribution.Distribution.partial_moment.n (Python parameter) — Order of partial moment"><span class="n"><span class="pre">n</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.distribution.Distribution.partial_moment.z" title="arch.univariate.distribution.Distribution.partial_moment.z (Python parameter) — Upper bound for partial moment integral"><span class="n"><span class="pre">z</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mf">0.0</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.distribution.Distribution.partial_moment.parameters" title="arch.univariate.distribution.Distribution.partial_moment.parameters (Python parameter) — Distribution parameters.  Use None for parameterless distributions."><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/distribution.html#Distribution.partial_moment"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.distribution.Distribution.partial_moment" title="Link to this definition">¶</a></dt>
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">Distribution.</span></span><span class="sig-name descname"><span class="pre">partial_moment</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.distribution.Distribution.partial_moment.n" title="arch.univariate.distribution.Distribution.partial_moment.n (Python parameter) — Order of partial moment"><span class="n"><span class="pre">n</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.distribution.Distribution.partial_moment.z" title="arch.univariate.distribution.Distribution.partial_moment.z (Python parameter) — Upper bound for partial moment integral"><span class="n"><span class="pre">z</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mf">0.0</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.distribution.Distribution.partial_moment.parameters" title="arch.univariate.distribution.Distribution.partial_moment.parameters (Python parameter) — Distribution parameters.  Use None for parameterless distributions."><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/distribution.html#Distribution.partial_moment"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.distribution.Distribution.partial_moment" title="Link to this definition">¶</a></dt>
 <dd><p>Order n lower partial moment from -inf to z</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.distribution.Distribution.partial_moment-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.distribution.Distribution.partial_moment-parameters" title="Permalink to this headline">¶</a></dt>
@@ -1178,7 +1178,7 @@ <h1 id="arch-univariate-distribution-distribution-partial-moment">arch.univariat
 </dd>
 <dt id="arch.univariate.distribution.Distribution.partial_moment.z"><span class="n sig-name">z</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mf">0.0</span></code><a class="headerlink" href="#arch.univariate.distribution.Distribution.partial_moment.z" title="Permalink to this definition">¶</a></dt><dd><p>Upper bound for partial moment integral</p>
 </dd>
-<dt id="arch.univariate.distribution.Distribution.partial_moment.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.distribution.Distribution.partial_moment.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Distribution parameters.  Use None for parameterless distributions.</p>
+<dt id="arch.univariate.distribution.Distribution.partial_moment.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.distribution.Distribution.partial_moment.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Distribution parameters.  Use None for parameterless distributions.</p>
 </dd>
 </dl>
 </dd>
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 <h1 id="arch-univariate-distribution-distribution-ppf">arch.univariate.distribution.Distribution.ppf<a class="headerlink" href="#arch-univariate-distribution-distribution-ppf" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.distribution.Distribution.ppf">
-<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">Distribution.</span></span><span class="sig-name descname"><span class="pre">ppf</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.distribution.Distribution.ppf.pits" title="arch.univariate.distribution.Distribution.ppf.pits (Python parameter) — Probability-integral-transformed values in the interval (0, 1)."><span class="n"><span class="pre">pits</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.distribution.Distribution.ppf.parameters" title="arch.univariate.distribution.Distribution.ppf.parameters (Python parameter) — Distribution parameters. Use None for parameterless distributions."><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/distribution.html#Distribution.ppf"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.distribution.Distribution.ppf" title="Link to this definition">¶</a></dt>
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">Distribution.</span></span><span class="sig-name descname"><span class="pre">ppf</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.distribution.Distribution.ppf.pits" title="arch.univariate.distribution.Distribution.ppf.pits (Python parameter) — Probability-integral-transformed values in the interval (0, 1)."><span class="n"><span class="pre">pits</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.distribution.Distribution.ppf.parameters" title="arch.univariate.distribution.Distribution.ppf.parameters (Python parameter) — Distribution parameters. Use None for parameterless distributions."><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/distribution.html#Distribution.ppf"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.distribution.Distribution.ppf" title="Link to this definition">¶</a></dt>
 <dd><p>Inverse cumulative density function (ICDF)</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.distribution.Distribution.ppf-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.distribution.Distribution.ppf-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.distribution.Distribution.ppf.pits"><span class="n sig-name">pits</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.univariate.distribution.Distribution.ppf.pits" title="Permalink to this definition">¶</a></dt><dd><p>Probability-integral-transformed values in the interval (0, 1).</p>
+<dt id="arch.univariate.distribution.Distribution.ppf.pits"><span class="n sig-name">pits</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a></span></span><a class="headerlink" href="#arch.univariate.distribution.Distribution.ppf.pits" title="Permalink to this definition">¶</a></dt><dd><p>Probability-integral-transformed values in the interval (0, 1).</p>
 </dd>
-<dt id="arch.univariate.distribution.Distribution.ppf.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.distribution.Distribution.ppf.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Distribution parameters. Use <code class="docutils literal notranslate"><span class="pre">None</span></code> for parameterless
+<dt id="arch.univariate.distribution.Distribution.ppf.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.distribution.Distribution.ppf.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Distribution parameters. Use <code class="docutils literal notranslate"><span class="pre">None</span></code> for parameterless
 distributions.</p>
 </dd>
 </dl>
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+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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+            arch v6.2.1 (+23)
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+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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diff --git a/devel/univariate/generated/generated/arch.univariate.distribution.Distribution.simulate.html b/devel/univariate/generated/generated/arch.univariate.distribution.Distribution.simulate.html
index b1b2502694..0795e55c15 100644
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+++ b/devel/univariate/generated/generated/arch.univariate.distribution.Distribution.simulate.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.distribution.Distribution.simulate - arch v6.2.1 (+21)</title>
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@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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+            arch v6.2.1 (+23)
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@@ -189,10 +189,10 @@
 
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-    arch v6.2.1 (+21)
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 <h1 id="arch-univariate-distribution-distribution-simulate">arch.univariate.distribution.Distribution.simulate<a class="headerlink" href="#arch-univariate-distribution-distribution-simulate" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.distribution.Distribution.simulate">
-<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">Distribution.</span></span><span class="sig-name descname"><span class="pre">simulate</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.distribution.Distribution.simulate.parameters" title="arch.univariate.distribution.Distribution.simulate.parameters (Python parameter) — Distribution parameters"><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.12)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/distribution.html#Distribution.simulate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.distribution.Distribution.simulate" title="Link to this definition">¶</a></dt>
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">Distribution.</span></span><span class="sig-name descname"><span class="pre">simulate</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.distribution.Distribution.simulate.parameters" title="arch.univariate.distribution.Distribution.simulate.parameters (Python parameter) — Distribution parameters"><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a></span></span></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.12)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/distribution.html#Distribution.simulate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.distribution.Distribution.simulate" title="Link to this definition">¶</a></dt>
 <dd><p>Simulates i.i.d. draws from the distribution</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.distribution.Distribution.simulate-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.distribution.Distribution.simulate-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.distribution.Distribution.simulate.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.univariate.distribution.Distribution.simulate.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Distribution parameters</p>
+<dt id="arch.univariate.distribution.Distribution.simulate.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a></span></span><a class="headerlink" href="#arch.univariate.distribution.Distribution.simulate.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Distribution parameters</p>
 </dd>
 </dl>
 </dd>
@@ -1274,7 +1274,7 @@ <h1 id="arch-univariate-distribution-distribution-simulate">arch.univariate.dist
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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diff --git a/devel/univariate/generated/generated/arch.univariate.distribution.Distribution.starting_values.html b/devel/univariate/generated/generated/arch.univariate.distribution.Distribution.starting_values.html
index 064b5d624f..130d04e5eb 100644
--- a/devel/univariate/generated/generated/arch.univariate.distribution.Distribution.starting_values.html
+++ b/devel/univariate/generated/generated/arch.univariate.distribution.Distribution.starting_values.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.distribution.Distribution.starting_values - arch v6.2.1 (+21)</title>
+        <title>arch.univariate.distribution.Distribution.starting_values - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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@@ -102,7 +102,7 @@
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-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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@@ -1276,7 +1276,7 @@ <h1 id="arch-univariate-distribution-distribution-starting-values">arch.univaria
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-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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diff --git a/devel/univariate/generated/generated/arch.univariate.recursions_python.VolatilityUpdater.initialize_update.html b/devel/univariate/generated/generated/arch.univariate.recursions_python.VolatilityUpdater.initialize_update.html
index f40fbcbc62..de87080d30 100644
--- a/devel/univariate/generated/generated/arch.univariate.recursions_python.VolatilityUpdater.initialize_update.html
+++ b/devel/univariate/generated/generated/arch.univariate.recursions_python.VolatilityUpdater.initialize_update.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.recursions_python.VolatilityUpdater.initialize_update - arch v6.2.1 (+21)</title>
+        <title>arch.univariate.recursions_python.VolatilityUpdater.initialize_update - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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     <div class="md-nav__source">
@@ -1215,7 +1215,7 @@ <h1 id="arch-univariate-recursions-python-volatilityupdater-initialize-update">a
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-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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diff --git a/devel/univariate/generated/generated/arch.univariate.recursions_python.VolatilityUpdater.update.html b/devel/univariate/generated/generated/arch.univariate.recursions_python.VolatilityUpdater.update.html
index ea7b152b54..0e05e028a4 100644
--- a/devel/univariate/generated/generated/arch.univariate.recursions_python.VolatilityUpdater.update.html
+++ b/devel/univariate/generated/generated/arch.univariate.recursions_python.VolatilityUpdater.update.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.recursions_python.VolatilityUpdater.update - arch v6.2.1 (+21)</title>
+        <title>arch.univariate.recursions_python.VolatilityUpdater.update - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
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@@ -92,7 +92,7 @@
 
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-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
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-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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@@ -1247,7 +1247,7 @@ <h1 id="arch-univariate-recursions-python-volatilityupdater-update">arch.univari
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-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
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diff --git a/devel/univariate/generated/generated/arch.univariate.volatility.VolatilityProcess.backcast.html b/devel/univariate/generated/generated/arch.univariate.volatility.VolatilityProcess.backcast.html
index 1d35d76fac..9ac03fbbb3 100644
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+++ b/devel/univariate/generated/generated/arch.univariate.volatility.VolatilityProcess.backcast.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.volatility.VolatilityProcess.backcast - arch v6.2.1 (+21)</title>
+        <title>arch.univariate.volatility.VolatilityProcess.backcast - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
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-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
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@@ -92,7 +92,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
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-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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@@ -1446,12 +1446,12 @@
 <h1 id="arch-univariate-volatility-volatilityprocess-forecast">arch.univariate.volatility.VolatilityProcess.forecast<a class="headerlink" href="#arch-univariate-volatility-volatilityprocess-forecast" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.volatility.VolatilityProcess.forecast">
-<span class="sig-prename descclassname"><span class="pre">VolatilityProcess.</span></span><span class="sig-name descname"><span class="pre">forecast</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.volatility.VolatilityProcess.forecast.parameters" title="arch.univariate.volatility.VolatilityProcess.forecast.parameters (Python parameter) — Parameters required to forecast the volatility model"><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.volatility.VolatilityProcess.forecast.resids" title="arch.univariate.volatility.VolatilityProcess.forecast.resids (Python parameter) — Residuals to use in the recursion"><span class="n"><span class="pre">resids</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.volatility.VolatilityProcess.forecast.backcast" title="arch.univariate.volatility.VolatilityProcess.forecast.backcast (Python parameter) — Value to use when initializing the recursion"><span class="n"><span class="pre">backcast</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.volatility.VolatilityProcess.forecast.var_bounds" title="arch.univariate.volatility.VolatilityProcess.forecast.var_bounds (Python parameter) — Array containing columns of lower and upper bounds"><span class="n"><span class="pre">var_bounds</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.volatility.VolatilityProcess.forecast.start" title="arch.univariate.volatility.VolatilityProcess.forecast.start (Python parameter) — Index of the first observation to use as the starting point for the forecast.  Default is len(resids)."><span class="n"><span class="pre">start</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.volatility.VolatilityProcess.forecast.horizon" title="arch.univariate.volatility.VolatilityProcess.forecast.horizon (Python parameter) — Forecast horizon.  Must be 1 or larger.  Forecasts are produced for horizons in [1, horizon]."><span class="n"><span class="pre">horizon</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.volatility.VolatilityProcess.forecast.method" title="arch.univariate.volatility.VolatilityProcess.forecast.method (Python parameter) — Method to use when producing the forecast. The default is analytic."><span class="n"><span class="pre">method</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'analytic'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'simulation'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'bootstrap'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;analytic&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.volatility.VolatilityProcess.forecast.simulations" title="arch.univariate.volatility.VolatilityProcess.forecast.simulations (Python parameter) — Number of simulations to run when computing the forecast using either simulation or bootstrap."><span class="n"><span class="pre">simulations</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.volatility.VolatilityProcess.forecast.rng" title="arch.univariate.volatility.VolatilityProcess.forecast.rng (Python parameter) — Callable random number generator required if method is 'simulation'. Must take a single shape input and return random samples numbers with that shape."><span class="n"><span class="pre">rng</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.12)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.volatility.VolatilityProcess.forecast.random_state" title="arch.univariate.volatility.VolatilityProcess.forecast.random_state (Python parameter) — NumPy RandomState instance to use when method is 'bootstrap'"><span class="n"><span class="pre">random_state</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.26)"><span class="pre">RandomState</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><span class="pre">VarianceForecast</span></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#VolatilityProcess.forecast"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.volatility.VolatilityProcess.forecast" title="Link to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">VolatilityProcess.</span></span><span class="sig-name descname"><span class="pre">forecast</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.volatility.VolatilityProcess.forecast.parameters" title="arch.univariate.volatility.VolatilityProcess.forecast.parameters (Python parameter) — Parameters required to forecast the volatility model"><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.volatility.VolatilityProcess.forecast.resids" title="arch.univariate.volatility.VolatilityProcess.forecast.resids (Python parameter) — Residuals to use in the recursion"><span class="n"><span class="pre">resids</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.volatility.VolatilityProcess.forecast.backcast" title="arch.univariate.volatility.VolatilityProcess.forecast.backcast (Python parameter) — Value to use when initializing the recursion"><span class="n"><span class="pre">backcast</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.volatility.VolatilityProcess.forecast.var_bounds" title="arch.univariate.volatility.VolatilityProcess.forecast.var_bounds (Python parameter) — Array containing columns of lower and upper bounds"><span class="n"><span class="pre">var_bounds</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.volatility.VolatilityProcess.forecast.start" title="arch.univariate.volatility.VolatilityProcess.forecast.start (Python parameter) — Index of the first observation to use as the starting point for the forecast.  Default is len(resids)."><span class="n"><span class="pre">start</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.volatility.VolatilityProcess.forecast.horizon" title="arch.univariate.volatility.VolatilityProcess.forecast.horizon (Python parameter) — Forecast horizon.  Must be 1 or larger.  Forecasts are produced for horizons in [1, horizon]."><span class="n"><span class="pre">horizon</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.volatility.VolatilityProcess.forecast.method" title="arch.univariate.volatility.VolatilityProcess.forecast.method (Python parameter) — Method to use when producing the forecast. The default is analytic."><span class="n"><span class="pre">method</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'analytic'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'simulation'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'bootstrap'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;analytic&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.volatility.VolatilityProcess.forecast.simulations" title="arch.univariate.volatility.VolatilityProcess.forecast.simulations (Python parameter) — Number of simulations to run when computing the forecast using either simulation or bootstrap."><span class="n"><span class="pre">simulations</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1000</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.volatility.VolatilityProcess.forecast.rng" title="arch.univariate.volatility.VolatilityProcess.forecast.rng (Python parameter) — Callable random number generator required if method is 'simulation'. Must take a single shape input and return random samples numbers with that shape."><span class="n"><span class="pre">rng</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.12)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.volatility.VolatilityProcess.forecast.random_state" title="arch.univariate.volatility.VolatilityProcess.forecast.random_state (Python parameter) — NumPy RandomState instance to use when method is 'bootstrap'"><span class="n"><span class="pre">random_state</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.26)"><span class="pre">RandomState</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><span class="pre">VarianceForecast</span></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#VolatilityProcess.forecast"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.volatility.VolatilityProcess.forecast" title="Link to this definition">¶</a></dt>
 <dd><p>Forecast volatility from the model</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.volatility.VolatilityProcess.forecast-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.volatility.VolatilityProcess.forecast-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.volatility.VolatilityProcess.forecast.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.univariate.volatility.VolatilityProcess.forecast.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Parameters required to forecast the volatility model</p>
+<dt id="arch.univariate.volatility.VolatilityProcess.forecast.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a></span></span><a class="headerlink" href="#arch.univariate.volatility.VolatilityProcess.forecast.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Parameters required to forecast the volatility model</p>
 </dd>
 <dt id="arch.univariate.volatility.VolatilityProcess.forecast.resids"><span class="n sig-name">resids</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a></span></span><a class="headerlink" href="#arch.univariate.volatility.VolatilityProcess.forecast.resids" title="Permalink to this definition">¶</a></dt><dd><p>Residuals to use in the recursion</p>
 </dd>
@@ -1617,7 +1617,7 @@ <h1 id="arch-univariate-volatility-volatilityprocess-forecast">arch.univariate.v
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diff --git a/devel/univariate/generated/generated/arch.univariate.volatility.VolatilityProcess.num_params.html b/devel/univariate/generated/generated/arch.univariate.volatility.VolatilityProcess.num_params.html
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diff --git a/devel/univariate/generated/generated/arch.univariate.volatility.VolatilityProcess.parameter_names.html b/devel/univariate/generated/generated/arch.univariate.volatility.VolatilityProcess.parameter_names.html
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 <h1 id="arch-univariate-volatility-volatilityprocess-simulate">arch.univariate.volatility.VolatilityProcess.simulate<a class="headerlink" href="#arch-univariate-volatility-volatilityprocess-simulate" title="Link to this heading">¶</a></h1>
 <dl class="py method objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.volatility.VolatilityProcess.simulate">
-<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">VolatilityProcess.</span></span><span class="sig-name descname"><span class="pre">simulate</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.volatility.VolatilityProcess.simulate.parameters" title="arch.univariate.volatility.VolatilityProcess.simulate.parameters (Python parameter) — Parameters required to simulate the volatility model"><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.volatility.VolatilityProcess.simulate.nobs" title="arch.univariate.volatility.VolatilityProcess.simulate.nobs (Python parameter) — Number of data points to simulate"><span class="n"><span class="pre">nobs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.volatility.VolatilityProcess.simulate.rng" title="arch.univariate.volatility.VolatilityProcess.simulate.rng (Python parameter) — Callable function that takes a single integer input and returns a vector of random numbers"><span class="n"><span class="pre">rng</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.12)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.volatility.VolatilityProcess.simulate.burn" title="arch.univariate.volatility.VolatilityProcess.simulate.burn (Python parameter) — Number of additional observations to generate when initializing the simulation"><span class="n"><span class="pre">burn</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">500</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.volatility.VolatilityProcess.simulate.initial_value" title="arch.univariate.volatility.VolatilityProcess.simulate.initial_value (Python parameter) — Scalar or array of initial values to use when initializing the simulation"><span class="n"><span class="pre">initial_value</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#VolatilityProcess.simulate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.volatility.VolatilityProcess.simulate" title="Link to this definition">¶</a></dt>
+<em class="property"><span class="pre">abstract</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">VolatilityProcess.</span></span><span class="sig-name descname"><span class="pre">simulate</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.volatility.VolatilityProcess.simulate.parameters" title="arch.univariate.volatility.VolatilityProcess.simulate.parameters (Python parameter) — Parameters required to simulate the volatility model"><span class="n"><span class="pre">parameters</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)"><span class="pre">Sequence</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.volatility.VolatilityProcess.simulate.nobs" title="arch.univariate.volatility.VolatilityProcess.simulate.nobs (Python parameter) — Number of data points to simulate"><span class="n"><span class="pre">nobs</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.volatility.VolatilityProcess.simulate.rng" title="arch.univariate.volatility.VolatilityProcess.simulate.rng (Python parameter) — Callable function that takes a single integer input and returns a vector of random numbers"><span class="n"><span class="pre">rng</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Callable" title="(in Python v3.12)"><span class="pre">Callable</span></a><span class="p"><span class="pre">[</span></span><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="p"><span class="pre">...</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">]</span></span><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.volatility.VolatilityProcess.simulate.burn" title="arch.univariate.volatility.VolatilityProcess.simulate.burn (Python parameter) — Number of additional observations to generate when initializing the simulation"><span class="n"><span class="pre">burn</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">500</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.volatility.VolatilityProcess.simulate.initial_value" title="arch.univariate.volatility.VolatilityProcess.simulate.initial_value (Python parameter) — Scalar or array of initial values to use when initializing the simulation"><span class="n"><span class="pre">initial_value</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.12)"><span class="pre">tuple</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">,</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="p"><span class="pre">]</span></span></span></span></span><a class="reference internal" href="../../../_modules/arch/univariate/volatility.html#VolatilityProcess.simulate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.volatility.VolatilityProcess.simulate" title="Link to this definition">¶</a></dt>
 <dd><p>Simulate data from the model</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.volatility.VolatilityProcess.simulate-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.volatility.VolatilityProcess.simulate-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.volatility.VolatilityProcess.simulate.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a></span></span><a class="headerlink" href="#arch.univariate.volatility.VolatilityProcess.simulate.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Parameters required to simulate the volatility model</p>
+<dt id="arch.univariate.volatility.VolatilityProcess.simulate.parameters"><span class="n sig-name">parameters</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence" title="(in Python v3.12)">Sequence</a><span class="p">[</span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)">float</a><span class="p">]</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a></span></span><a class="headerlink" href="#arch.univariate.volatility.VolatilityProcess.simulate.parameters" title="Permalink to this definition">¶</a></dt><dd><p>Parameters required to simulate the volatility model</p>
 </dd>
 <dt id="arch.univariate.volatility.VolatilityProcess.simulate.nobs"><span class="n sig-name">nobs</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)">int</a></span></span><a class="headerlink" href="#arch.univariate.volatility.VolatilityProcess.simulate.nobs" title="Permalink to this definition">¶</a></dt><dd><p>Number of data points to simulate</p>
 </dd>
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+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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     <div class="md-nav__source">
@@ -1459,7 +1459,7 @@ <h1 id="arch-univariate-volatility-volatilityprocess-variance-bounds">arch.univa
     <script id="__config" type="application/json">{"base": "../../..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
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diff --git a/devel/univariate/generated/generated/arch.univariate.volatility.VolatilityProcess.volatility_updater.html b/devel/univariate/generated/generated/arch.univariate.volatility.VolatilityProcess.volatility_updater.html
index d7a15736e9..c0aa5e5a8c 100644
--- a/devel/univariate/generated/generated/arch.univariate.volatility.VolatilityProcess.volatility_updater.html
+++ b/devel/univariate/generated/generated/arch.univariate.volatility.VolatilityProcess.volatility_updater.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>arch.univariate.volatility.VolatilityProcess.volatility_updater - arch v6.2.1 (+21)</title>
+        <title>arch.univariate.volatility.VolatilityProcess.volatility_updater - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../../../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../../../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
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+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../../../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../../../_static/bw-logo.svg" alt="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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     <div class="md-nav__source">
@@ -1409,7 +1409,7 @@ <h1 id="arch-univariate-volatility-volatilityprocess-volatility-updater">arch.un
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-        <script src="../../../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../../../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
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   </body>
diff --git a/devel/univariate/introduction.html b/devel/univariate/introduction.html
index 2b745a7fe1..204be58842 100644
--- a/devel/univariate/introduction.html
+++ b/devel/univariate/introduction.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>Introduction to ARCH Models - arch v6.2.1 (+21)</title>
+        <title>Introduction to ARCH Models - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
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         <link rel="stylesheet" type="text/css" href="../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -92,7 +92,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
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         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -189,10 +189,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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     <div class="md-nav__source">
@@ -1017,14 +1017,14 @@ <h2 id="model-constructor">Model Constructor<a class="headerlink" href="#model-c
 using a simple model constructor.</p>
 <dl class="py function objdesc">
 <dt class="sig sig-object highlight sig-wrap py" id="arch.univariate.arch_model">
-<span class="sig-prename descclassname"><span class="pre">arch.univariate.</span></span><span class="sig-name descname"><span class="pre">arch_model</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.arch_model.y" title="arch.univariate.arch_model.y (Python parameter) — The dependent variable"><span class="n"><span class="pre">y</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.arch_model.x" title="arch.univariate.arch_model.x (Python parameter) — Exogenous regressors.  Ignored if model does not permit exogenous regressors."><span class="n"><span class="pre">x</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.arch_model.mean" title="arch.univariate.arch_model.mean (Python parameter) — Name of the mean model.  Currently supported options are: 'Constant', 'Zero', 'LS', 'AR', 'ARX', 'HAR' and  'HARX'"><span class="n"><span class="pre">mean</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'Constant'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'Zero'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'LS'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'AR'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'ARX'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'HAR'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'HARX'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'constant'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'zero'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;Constant&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.arch_model.lags" title="arch.univariate.arch_model.lags (Python parameter) — Either a scalar integer value indicating lag length or a list of integers specifying lag locations."><span class="n"><span class="pre">lags</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">0</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.arch_model.vol" title="arch.univariate.arch_model.vol (Python parameter) — Name of the volatility model.  Currently supported options are: 'GARCH' (default), 'ARCH', 'EGARCH', 'FIGARCH', 'APARCH' and 'HARCH'"><span class="n"><span class="pre">vol</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'GARCH'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'ARCH'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'EGARCH'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'FIGARCH'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'APARCH'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'HARCH'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;GARCH&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.arch_model.p" title="arch.univariate.arch_model.p (Python parameter) — Lag order of the symmetric innovation"><span class="n"><span class="pre">p</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="p"><span class="pre">]</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.arch_model.o" title="arch.univariate.arch_model.o (Python parameter) — Lag order of the asymmetric innovation"><span class="n"><span class="pre">o</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">0</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.arch_model.q" title="arch.univariate.arch_model.q (Python parameter) — Lag order of lagged volatility or equivalent"><span class="n"><span class="pre">q</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.arch_model.power" title="arch.univariate.arch_model.power (Python parameter) — Power to use with GARCH and related models"><span class="n"><span class="pre">power</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mf">2.0</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.arch_model.dist" title="arch.univariate.arch_model.dist (Python parameter) — Name of the error distribution.  Currently supported options are:"><span class="n"><span class="pre">dist</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'normal'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'gaussian'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'t'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'studentst'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'skewstudent'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'skewt'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'ged'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'generalized</span> <span class="pre">error'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;normal&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.arch_model.hold_back" title="arch.univariate.arch_model.hold_back (Python parameter) — Number of observations at the start of the sample to exclude when estimating model parameters.  Used when comparing models with different lag lengths to estimate on the common sample."><span class="n"><span class="pre">hold_back</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.arch_model.rescale" title="arch.univariate.arch_model.rescale (Python parameter) — Flag indicating whether to automatically rescale data if the scale of the data is likely to produce convergence issues when estimating model parameters. If False, the model is estimated on the data without transformation.  If True, than y is rescaled and the new scale is reported in the estimation results."><span class="n"><span class="pre">rescale</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference internal" href="generated/arch.univariate.HARX.html#arch.univariate.HARX" title="arch.univariate.mean.HARX (Python class)"><span class="pre">HARX</span></a></span></span></span><a class="reference internal" href="../_modules/arch/univariate/mean.html#arch_model"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.arch_model" title="Link to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">arch.univariate.</span></span><span class="sig-name descname"><span class="pre">arch_model</span></span><span class="sig-paren">(</span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.arch_model.y" title="arch.univariate.arch_model.y (Python parameter) — The dependent variable"><span class="n"><span class="pre">y</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)"><span class="pre">Series</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.arch_model.x" title="arch.univariate.arch_model.x (Python parameter) — Exogenous regressors.  Ignored if model does not permit exogenous regressors."><span class="n"><span class="pre">x</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)"><span class="pre">DataFrame</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.arch_model.mean" title="arch.univariate.arch_model.mean (Python parameter) — Name of the mean model.  Currently supported options are: 'Constant', 'Zero', 'LS', 'AR', 'ARX', 'HAR' and  'HARX'"><span class="n"><span class="pre">mean</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'Constant'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'Zero'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'LS'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'AR'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'ARX'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'HAR'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'HARX'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'constant'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'zero'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;Constant&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.arch_model.lags" title="arch.univariate.arch_model.lags (Python parameter) — Either a scalar integer value indicating lag length or a list of integers specifying lag locations."><span class="n"><span class="pre">lags</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="p"><span class="pre">]</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)"><span class="pre">ndarray</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">0</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.arch_model.vol" title="arch.univariate.arch_model.vol (Python parameter) — Name of the volatility model.  Currently supported options are: 'GARCH' (default), 'ARCH', 'EGARCH', 'FIGARCH', 'APARCH' and 'HARCH'"><span class="n"><span class="pre">vol</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'GARCH'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'ARCH'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'EGARCH'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'FIGARCH'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'APARCH'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'HARCH'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;GARCH&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.arch_model.p" title="arch.univariate.arch_model.p (Python parameter) — Lag order of the symmetric innovation"><span class="n"><span class="pre">p</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.12)"><span class="pre">list</span></a><span class="p"><span class="pre">[</span></span><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="p"><span class="pre">]</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.arch_model.o" title="arch.univariate.arch_model.o (Python parameter) — Lag order of the asymmetric innovation"><span class="n"><span class="pre">o</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">0</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.arch_model.q" title="arch.univariate.arch_model.q (Python parameter) — Lag order of lagged volatility or equivalent"><span class="n"><span class="pre">q</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mi">1</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.arch_model.power" title="arch.univariate.arch_model.power (Python parameter) — Power to use with GARCH and related models"><span class="n"><span class="pre">power</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.12)"><span class="pre">float</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="mf">2.0</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.arch_model.dist" title="arch.univariate.arch_model.dist (Python parameter) — Name of the error distribution.  Currently supported options are:"><span class="n"><span class="pre">dist</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><span class="s"><span class="pre">'normal'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'gaussian'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'t'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'studentst'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'skewstudent'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'skewt'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'ged'</span></span><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><span class="s"><span class="pre">'generalized</span> <span class="pre">error'</span></span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;normal&#39;</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.arch_model.hold_back" title="arch.univariate.arch_model.hold_back (Python parameter) — Number of observations at the start of the sample to exclude when estimating model parameters.  Used when comparing models with different lag lengths to estimate on the common sample."><span class="n"><span class="pre">hold_back</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.12)"><span class="pre">int</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em>, </span><span class="sig-param-decl"><em class="sig-param"><a class="n reference internal" href="#arch.univariate.arch_model.rescale" title="arch.univariate.arch_model.rescale (Python parameter) — Flag indicating whether to automatically rescale data if the scale of the data is likely to produce convergence issues when estimating model parameters. If False, the model is estimated on the data without transformation.  If True, than y is rescaled and the new scale is reported in the estimation results."><span class="n"><span class="pre">rescale</span></span></a><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.12)"><span class="pre">bool</span></a><span class="w"> </span><span class="p"><span class="pre">|</span></span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)"><span class="pre">None</span></a></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code></em></span><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><span class="desctype"><a class="reference internal" href="generated/arch.univariate.HARX.html#arch.univariate.HARX" title="arch.univariate.mean.HARX (Python class)"><span class="pre">HARX</span></a></span></span></span><a class="reference internal" href="../_modules/arch/univariate/mean.html#arch_model"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#arch.univariate.arch_model" title="Link to this definition">¶</a></dt>
 <dd><p>Initialization of common ARCH model specifications</p>
 <dl class="field-list simple">
 <dt class="field-odd" id="arch.univariate.arch_model-parameters">Parameters<span class="colon">:</span><a class="headerlink" href="#arch.univariate.arch_model-parameters" title="Permalink to this headline">¶</a></dt>
 <dd class="field-odd"><dl class="simple api-field highlight">
-<dt id="arch.univariate.arch_model.y"><span class="n sig-name">y</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.1)">Series</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><a class="headerlink" href="#arch.univariate.arch_model.y" title="Permalink to this definition">¶</a></dt><dd><p>The dependent variable</p>
+<dt id="arch.univariate.arch_model.y"><span class="n sig-name">y</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.Series" title="(in pandas v2.1.4)">Series</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><a class="headerlink" href="#arch.univariate.arch_model.y" title="Permalink to this definition">¶</a></dt><dd><p>The dependent variable</p>
 </dd>
-<dt id="arch.univariate.arch_model.x"><span class="n sig-name">x</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.1)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.arch_model.x" title="Permalink to this definition">¶</a></dt><dd><p>Exogenous regressors.  Ignored if model does not permit exogenous
+<dt id="arch.univariate.arch_model.x"><span class="n sig-name">x</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.26)">ndarray</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html#pandas.DataFrame" title="(in pandas v2.1.4)">DataFrame</a><span class="w"> </span><span class="p">|</span><span class="w"> </span><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.12)">None</a></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="kc">None</span></code><a class="headerlink" href="#arch.univariate.arch_model.x" title="Permalink to this definition">¶</a></dt><dd><p>Exogenous regressors.  Ignored if model does not permit exogenous
 regressors.</p>
 </dd>
 <dt id="arch.univariate.arch_model.mean"><span class="n sig-name">mean</span><span class="p">:</span><span class="w"> </span><span class="n"><span class="desctype"><span class="s">'Constant'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'Zero'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'LS'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'AR'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'ARX'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'HAR'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'HARX'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'constant'</span><span class="w"> </span><span class="p">|</span><span class="w"> </span><span class="s">'zero'</span></span></span><span class="w"> </span><span class="o">=</span><span class="w"> </span><code class="code python default_value docutils literal highlight highlight-python"><span class="s1">&#39;Constant&#39;</span></code><a class="headerlink" href="#arch.univariate.arch_model.mean" title="Permalink to this definition">¶</a></dt><dd><p>Name of the mean model.  Currently supported options are: ‘Constant’,
@@ -1216,7 +1216,7 @@ <h2 id="model-constructor">Model Constructor<a class="headerlink" href="#model-c
     <script id="__config" type="application/json">{"base": "..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
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diff --git a/devel/univariate/mean.html b/devel/univariate/mean.html
index 7bd19616a7..955b840d62 100644
--- a/devel/univariate/mean.html
+++ b/devel/univariate/mean.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>Mean Models - arch v6.2.1 (+21)</title>
+        <title>Mean Models - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
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-        <link rel="stylesheet" type="text/css" href="../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
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@@ -92,7 +92,7 @@
 
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-    <a href="../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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@@ -189,10 +189,10 @@
 
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   <label class="md-nav__title" for="__drawer">
-    <a href="../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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     <div class="md-nav__source">
@@ -1132,7 +1132,7 @@ <h2 id="writing-new-mean-models">Writing New Mean Models<a class="headerlink" hr
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-        <script src="../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/devel/univariate/results.html b/devel/univariate/results.html
index 6493ccd17a..cbbd28fb93 100644
--- a/devel/univariate/results.html
+++ b/devel/univariate/results.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>Model Results - arch v6.2.1 (+21)</title>
+        <title>Model Results - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../_static/css/small_fixes.css?v=64f7a1ef" />
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@@ -87,7 +87,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -97,7 +97,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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@@ -184,10 +184,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -834,7 +834,7 @@ <h1 id="model-results">Model Results<a class="headerlink" href="#model-results"
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-        <script src="../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
         <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     
   </body>
diff --git a/devel/univariate/univariate.html b/devel/univariate/univariate.html
index 18ba819b03..0563d4a77d 100644
--- a/devel/univariate/univariate.html
+++ b/devel/univariate/univariate.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>Univariate Volatility Models - arch v6.2.1 (+21)</title>
+        <title>Univariate Volatility Models - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
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@@ -87,7 +87,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -97,7 +97,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -184,10 +184,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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     <div class="md-nav__source">
@@ -871,7 +871,7 @@ <h1 id="univariate-volatility-models">Univariate Volatility Models<a class="head
     <script id="__config" type="application/json">{"base": "..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
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   </body>
diff --git a/devel/univariate/univariate_forecasting_with_exogenous_variables.html b/devel/univariate/univariate_forecasting_with_exogenous_variables.html
index 8d67f4cffc..6b08bd37e4 100644
--- a/devel/univariate/univariate_forecasting_with_exogenous_variables.html
+++ b/devel/univariate/univariate_forecasting_with_exogenous_variables.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>Forecasting with Exogenous Regressors - arch v6.2.1 (+21)</title>
+        <title>Forecasting with Exogenous Regressors - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
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@@ -93,7 +93,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -103,7 +103,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -190,10 +190,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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     <div class="md-nav__source">
@@ -1590,7 +1590,7 @@ <h3 id="Simulating-the-out-of-sample-x">Simulating the out-of-sample <code class
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-        <script src="../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
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         <script id="MathJax-script" src="../_static/mathjax/tex-mml-chtml.js?v=cadf963e"></script>
diff --git a/devel/univariate/univariate_using_fixed_variance.html b/devel/univariate/univariate_using_fixed_variance.html
index 7d73d69490..421f56c600 100644
--- a/devel/univariate/univariate_using_fixed_variance.html
+++ b/devel/univariate/univariate_using_fixed_variance.html
@@ -15,7 +15,7 @@
     
   
       
-        <title>Using the Fixed Variance process - arch v6.2.1 (+21)</title>
+        <title>Using the Fixed Variance process - arch v6.2.1 (+23)</title>
       
     
 
@@ -47,7 +47,7 @@
         <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
       
     
-        <link rel="stylesheet" type="text/css" href="../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
         <link rel="stylesheet" type="text/css" href="../_static/nbsphinx-code-cells.css?v=2aa19091" />
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     <script>__md_scope=new URL("..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
@@ -93,7 +93,7 @@
 
 <header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../index.html" title="arch v6.2.1 (+21)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
     <label class="md-header__button md-icon" for="__drawer">
@@ -103,7 +103,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            arch v6.2.1 (+21)
+            arch v6.2.1 (+23)
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -190,10 +190,10 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../index.html" title="arch v6.2.1 (+21)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+21)" data-md-component="logo">
+    <a href="../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
       <img src="../_static/bw-logo.svg" alt="logo">
     </a>
-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
   </label>
   
     <div class="md-nav__source">
@@ -836,8 +836,8 @@ <h3 id="Initial-Mean-Model-Estimation">Initial Mean Model Estimation<a class="he
 Distribution:                  Normal   AIC:                           4545.90
 Method:            Maximum Likelihood   BIC:                           4571.50
                                         No. Observations:                 1237
-Date:                Mon, Oct 16 2023   Df Residuals:                     1233
-Time:                        10:28:28   Df Model:                            4
+Date:                Wed, Dec 20 2023   Df Residuals:                     1233
+Time:                        21:47:23   Df Model:                            4
                                    Mean Model
 ================================================================================
                       coef    std err          t      P&gt;|t|     95.0% Conf. Int.
@@ -881,8 +881,8 @@ <h3 id="Initial-Volatility-Model-Estimation">Initial Volatility Model Estimation
 Distribution:                  Normal   AIC:                           3881.86
 Method:            Maximum Likelihood   BIC:                           3902.35
                                         No. Observations:                 1237
-Date:                Mon, Oct 16 2023   Df Residuals:                     1237
-Time:                        10:28:28   Df Model:                            0
+Date:                Wed, Dec 20 2023   Df Residuals:                     1237
+Time:                        21:47:23   Df Model:                            0
                               Volatility Model
 ===========================================================================
                  coef    std err          t      P&gt;|t|     95.0% Conf. Int.
@@ -933,16 +933,16 @@ <h3 id="Re-estimating-the-mean-with-a-FixedVariance">Re-estimating the mean with
 </div>
 <div class="output_area docutils container">
 <div class="highlight"><pre>
-Iteration:      1,   Func. Count:      7,   Neg. LLF: 255813245019.088
-Iteration:      2,   Func. Count:     19,   Neg. LLF: 930291.9264107295
-Iteration:      3,   Func. Count:     28,   Neg. LLF: 3486.6798195388146
-Iteration:      4,   Func. Count:     36,   Neg. LLF: 2885.7035662797234
-Iteration:      5,   Func. Count:     44,   Neg. LLF: 65535916.590182975
-Iteration:      6,   Func. Count:     53,   Neg. LLF: 1935.9527546214572
-Iteration:      7,   Func. Count:     59,   Neg. LLF: 1935.9470520474433
-Iteration:      8,   Func. Count:     65,   Neg. LLF: 1935.9470514368204
+Iteration:      1,   Func. Count:      7,   Neg. LLF: 255806977215.44
+Iteration:      2,   Func. Count:     19,   Neg. LLF: 930291.4462204215
+Iteration:      3,   Func. Count:     28,   Neg. LLF: 3486.7135083370713
+Iteration:      4,   Func. Count:     36,   Neg. LLF: 2885.692473212838
+Iteration:      5,   Func. Count:     44,   Neg. LLF: 65536430.69379793
+Iteration:      6,   Func. Count:     53,   Neg. LLF: 1935.9527543235638
+Iteration:      7,   Func. Count:     59,   Neg. LLF: 1935.9470521680555
+Iteration:      8,   Func. Count:     65,   Neg. LLF: 1935.9470515573437
 Optimization terminated successfully    (Exit mode 0)
-            Current function value: 1935.9470514368204
+            Current function value: 1935.9470515573437
             Iterations: 8
             Function evaluations: 65
             Gradient evaluations: 8
@@ -954,8 +954,8 @@ <h3 id="Re-estimating-the-mean-with-a-FixedVariance">Re-estimating the mean with
 Distribution:                  Normal   AIC:                           3881.89
 Method:            Maximum Likelihood   BIC:                           3907.50
                                         No. Observations:                 1237
-Date:                Mon, Oct 16 2023   Df Residuals:                     1233
-Time:                        10:28:29   Df Model:                            4
+Date:                Wed, Dec 20 2023   Df Residuals:                     1233
+Time:                        21:47:24   Df Model:                            4
                                     Mean Model
 ==================================================================================
                       coef    std err          t      P&gt;|t|       95.0% Conf. Int.
@@ -1010,16 +1010,16 @@ <h3 id="Zig-Zag-estimation">Zig-Zag estimation<a class="headerlink" href="#Zig-Z
 Distribution:                           Normal   AIC:                           3879.48
 Method:                     Maximum Likelihood   BIC:                           3899.96
                                                  No. Observations:                 1237
-Date:                         Mon, Oct 16 2023   Df Residuals:                     1233
-Time:                                 10:28:30   Df Model:                            4
+Date:                         Wed, Dec 20 2023   Df Residuals:                     1233
+Time:                                 21:47:24   Df Model:                            4
                                     Mean Model
 =================================================================================
                       coef    std err          t      P&gt;|t|      95.0% Conf. Int.
 ---------------------------------------------------------------------------------
 Const               0.5602      0.152      3.681  2.323e-04     [  0.262,  0.858]
-VIX Index[0:1]      0.9381  3.616e-02     25.939 2.389e-148     [  0.867,  1.009]
+VIX Index[0:1]      0.9381  3.616e-02     25.940 2.388e-148     [  0.867,  1.009]
 VIX Index[0:5]     -0.0262  3.774e-02     -0.693      0.488   [ -0.100,4.781e-02]
-VIX Index[0:22]     0.0499  2.099e-02      2.380  1.733e-02 [8.809e-03,9.109e-02]
+VIX Index[0:22]     0.0499  2.099e-02      2.380  1.733e-02 [8.810e-03,9.109e-02]
 =================================================================================
 
 Covariance estimator: robust
@@ -1050,8 +1050,8 @@ <h3 id="Direct-Estimation">Direct Estimation<a class="headerlink" href="#Direct-
 Distribution:                  Normal   AIC:                           3881.23
 Method:            Maximum Likelihood   BIC:                           3922.19
                                         No. Observations:                 1237
-Date:                Mon, Oct 16 2023   Df Residuals:                     1233
-Time:                        10:28:30   Df Model:                            4
+Date:                Wed, Dec 20 2023   Df Residuals:                     1233
+Time:                        21:47:25   Df Model:                            4
                                    Mean Model
 ================================================================================
                       coef    std err          t      P&gt;|t|     95.0% Conf. Int.
@@ -1187,7 +1187,7 @@ <h3 id="Direct-Estimation">Direct Estimation<a class="headerlink" href="#Direct-
     <script id="__config" type="application/json">{"base": "..", "features": [], "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}, "version": {"provider": "mike", "staticVersions": [{"aliases": [], "title": "Release", "version": "https://bashtage.github.io/arch/"}, {"aliases": [], "title": "Development", "version": "https://bashtage.github.io/arch/devel/"}, {"aliases": [], "title": "RTD (Release)", "version": "https://arch.readthedocs.io/"}], "versionPath": null}}</script>
     
       
-        <script src="../_static/sphinx_immaterial_theme.1b5b7a2d5891aec19.min.js?v=1e7b0789"></script>
+        <script src="../_static/sphinx_immaterial_theme.f9d9eeeb247ace16c.min.js?v=8ec58cb5"></script>
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diff --git a/devel/univariate/univariate_using_fixed_variance.ipynb b/devel/univariate/univariate_using_fixed_variance.ipynb
index 762df91c3d..31815a4911 100644
--- a/devel/univariate/univariate_using_fixed_variance.ipynb
+++ b/devel/univariate/univariate_using_fixed_variance.ipynb
@@ -23,10 +23,10 @@
    "execution_count": 1,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-10-16T10:28:26.809762Z",
-     "iopub.status.busy": "2023-10-16T10:28:26.809279Z",
-     "iopub.status.idle": "2023-10-16T10:28:27.984279Z",
-     "shell.execute_reply": "2023-10-16T10:28:27.983600Z"
+     "iopub.execute_input": "2023-12-20T21:47:21.897398Z",
+     "iopub.status.busy": "2023-12-20T21:47:21.897123Z",
+     "iopub.status.idle": "2023-12-20T21:47:23.091434Z",
+     "shell.execute_reply": "2023-12-20T21:47:23.090724Z"
     }
    },
    "outputs": [],
@@ -56,10 +56,10 @@
    "execution_count": 2,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-10-16T10:28:27.988049Z",
-     "iopub.status.busy": "2023-10-16T10:28:27.987631Z",
-     "iopub.status.idle": "2023-10-16T10:28:27.992364Z",
-     "shell.execute_reply": "2023-10-16T10:28:27.991682Z"
+     "iopub.execute_input": "2023-12-20T21:47:23.094923Z",
+     "iopub.status.busy": "2023-12-20T21:47:23.094488Z",
+     "iopub.status.idle": "2023-12-20T21:47:23.097587Z",
+     "shell.execute_reply": "2023-12-20T21:47:23.096986Z"
     }
    },
    "outputs": [],
@@ -82,16 +82,16 @@
    "execution_count": 3,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-10-16T10:28:27.996648Z",
-     "iopub.status.busy": "2023-10-16T10:28:27.995576Z",
-     "iopub.status.idle": "2023-10-16T10:28:28.546584Z",
-     "shell.execute_reply": "2023-10-16T10:28:28.545907Z"
+     "iopub.execute_input": "2023-12-20T21:47:23.100361Z",
+     "iopub.status.busy": "2023-12-20T21:47:23.099966Z",
+     "iopub.status.idle": "2023-12-20T21:47:23.728915Z",
+     "shell.execute_reply": "2023-12-20T21:47:23.728156Z"
     }
    },
    "outputs": [
     {
      "data": {
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",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 1600x600 with 1 Axes>"
       ]
@@ -122,10 +122,10 @@
    "execution_count": 4,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-10-16T10:28:28.584014Z",
-     "iopub.status.busy": "2023-10-16T10:28:28.583712Z",
-     "iopub.status.idle": "2023-10-16T10:28:28.600867Z",
-     "shell.execute_reply": "2023-10-16T10:28:28.600228Z"
+     "iopub.execute_input": "2023-12-20T21:47:23.771124Z",
+     "iopub.status.busy": "2023-12-20T21:47:23.770287Z",
+     "iopub.status.idle": "2023-12-20T21:47:23.798443Z",
+     "shell.execute_reply": "2023-12-20T21:47:23.797772Z"
     }
    },
    "outputs": [
@@ -141,8 +141,8 @@
       "Distribution:                  Normal   AIC:                           4545.90\n",
       "Method:            Maximum Likelihood   BIC:                           4571.50\n",
       "                                        No. Observations:                 1237\n",
-      "Date:                Mon, Oct 16 2023   Df Residuals:                     1233\n",
-      "Time:                        10:28:28   Df Model:                            4\n",
+      "Date:                Wed, Dec 20 2023   Df Residuals:                     1233\n",
+      "Time:                        21:47:23   Df Model:                            4\n",
       "                                   Mean Model                                   \n",
       "================================================================================\n",
       "                      coef    std err          t      P>|t|     95.0% Conf. Int.\n",
@@ -184,10 +184,10 @@
    "execution_count": 5,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-10-16T10:28:28.604244Z",
-     "iopub.status.busy": "2023-10-16T10:28:28.604011Z",
-     "iopub.status.idle": "2023-10-16T10:28:28.682467Z",
-     "shell.execute_reply": "2023-10-16T10:28:28.681847Z"
+     "iopub.execute_input": "2023-12-20T21:47:23.804033Z",
+     "iopub.status.busy": "2023-12-20T21:47:23.802404Z",
+     "iopub.status.idle": "2023-12-20T21:47:23.896874Z",
+     "shell.execute_reply": "2023-12-20T21:47:23.896227Z"
     }
    },
    "outputs": [
@@ -203,8 +203,8 @@
       "Distribution:                  Normal   AIC:                           3881.86\n",
       "Method:            Maximum Likelihood   BIC:                           3902.35\n",
       "                                        No. Observations:                 1237\n",
-      "Date:                Mon, Oct 16 2023   Df Residuals:                     1237\n",
-      "Time:                        10:28:28   Df Model:                            0\n",
+      "Date:                Wed, Dec 20 2023   Df Residuals:                     1237\n",
+      "Time:                        21:47:23   Df Model:                            0\n",
       "                              Volatility Model                             \n",
       "===========================================================================\n",
       "                 coef    std err          t      P>|t|     95.0% Conf. Int.\n",
@@ -230,16 +230,16 @@
    "execution_count": 6,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-10-16T10:28:28.685877Z",
-     "iopub.status.busy": "2023-10-16T10:28:28.685651Z",
-     "iopub.status.idle": "2023-10-16T10:28:29.788456Z",
-     "shell.execute_reply": "2023-10-16T10:28:29.787807Z"
+     "iopub.execute_input": "2023-12-20T21:47:23.902546Z",
+     "iopub.status.busy": "2023-12-20T21:47:23.900841Z",
+     "iopub.status.idle": "2023-12-20T21:47:24.787003Z",
+     "shell.execute_reply": "2023-12-20T21:47:24.786225Z"
     }
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1600x600 with 2 Axes>"
       ]
@@ -268,10 +268,10 @@
    "execution_count": 7,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-10-16T10:28:29.793704Z",
-     "iopub.status.busy": "2023-10-16T10:28:29.792500Z",
-     "iopub.status.idle": "2023-10-16T10:28:29.819506Z",
-     "shell.execute_reply": "2023-10-16T10:28:29.818912Z"
+     "iopub.execute_input": "2023-12-20T21:47:24.790198Z",
+     "iopub.status.busy": "2023-12-20T21:47:24.789727Z",
+     "iopub.status.idle": "2023-12-20T21:47:24.818727Z",
+     "shell.execute_reply": "2023-12-20T21:47:24.817920Z"
     }
    },
    "outputs": [
@@ -279,16 +279,16 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Iteration:      1,   Func. Count:      7,   Neg. LLF: 255813245019.088\n",
-      "Iteration:      2,   Func. Count:     19,   Neg. LLF: 930291.9264107295\n",
-      "Iteration:      3,   Func. Count:     28,   Neg. LLF: 3486.6798195388146\n",
-      "Iteration:      4,   Func. Count:     36,   Neg. LLF: 2885.7035662797234\n",
-      "Iteration:      5,   Func. Count:     44,   Neg. LLF: 65535916.590182975\n",
-      "Iteration:      6,   Func. Count:     53,   Neg. LLF: 1935.9527546214572\n",
-      "Iteration:      7,   Func. Count:     59,   Neg. LLF: 1935.9470520474433\n",
-      "Iteration:      8,   Func. Count:     65,   Neg. LLF: 1935.9470514368204\n",
+      "Iteration:      1,   Func. Count:      7,   Neg. LLF: 255806977215.44\n",
+      "Iteration:      2,   Func. Count:     19,   Neg. LLF: 930291.4462204215\n",
+      "Iteration:      3,   Func. Count:     28,   Neg. LLF: 3486.7135083370713\n",
+      "Iteration:      4,   Func. Count:     36,   Neg. LLF: 2885.692473212838\n",
+      "Iteration:      5,   Func. Count:     44,   Neg. LLF: 65536430.69379793\n",
+      "Iteration:      6,   Func. Count:     53,   Neg. LLF: 1935.9527543235638\n",
+      "Iteration:      7,   Func. Count:     59,   Neg. LLF: 1935.9470521680555\n",
+      "Iteration:      8,   Func. Count:     65,   Neg. LLF: 1935.9470515573437\n",
       "Optimization terminated successfully    (Exit mode 0)\n",
-      "            Current function value: 1935.9470514368204\n",
+      "            Current function value: 1935.9470515573437\n",
       "            Iterations: 8\n",
       "            Function evaluations: 65\n",
       "            Gradient evaluations: 8\n",
@@ -300,8 +300,8 @@
       "Distribution:                  Normal   AIC:                           3881.89\n",
       "Method:            Maximum Likelihood   BIC:                           3907.50\n",
       "                                        No. Observations:                 1237\n",
-      "Date:                Mon, Oct 16 2023   Df Residuals:                     1233\n",
-      "Time:                        10:28:29   Df Model:                            4\n",
+      "Date:                Wed, Dec 20 2023   Df Residuals:                     1233\n",
+      "Time:                        21:47:24   Df Model:                            4\n",
       "                                    Mean Model                                    \n",
       "==================================================================================\n",
       "                      coef    std err          t      P>|t|       95.0% Conf. Int.\n",
@@ -344,10 +344,10 @@
    "execution_count": 8,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-10-16T10:28:29.824887Z",
-     "iopub.status.busy": "2023-10-16T10:28:29.823302Z",
-     "iopub.status.idle": "2023-10-16T10:28:30.062678Z",
-     "shell.execute_reply": "2023-10-16T10:28:30.061950Z"
+     "iopub.execute_input": "2023-12-20T21:47:24.822094Z",
+     "iopub.status.busy": "2023-12-20T21:47:24.821589Z",
+     "iopub.status.idle": "2023-12-20T21:47:25.008382Z",
+     "shell.execute_reply": "2023-12-20T21:47:25.007365Z"
     }
    },
    "outputs": [
@@ -358,13 +358,7 @@
       "0\n",
       "1\n",
       "2\n",
-      "3\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      "3\n",
       "4\n"
      ]
     },
@@ -380,16 +374,16 @@
       "Distribution:                           Normal   AIC:                           3879.48\n",
       "Method:                     Maximum Likelihood   BIC:                           3899.96\n",
       "                                                 No. Observations:                 1237\n",
-      "Date:                         Mon, Oct 16 2023   Df Residuals:                     1233\n",
-      "Time:                                 10:28:30   Df Model:                            4\n",
+      "Date:                         Wed, Dec 20 2023   Df Residuals:                     1233\n",
+      "Time:                                 21:47:24   Df Model:                            4\n",
       "                                    Mean Model                                   \n",
       "=================================================================================\n",
       "                      coef    std err          t      P>|t|      95.0% Conf. Int.\n",
       "---------------------------------------------------------------------------------\n",
       "Const               0.5602      0.152      3.681  2.323e-04     [  0.262,  0.858]\n",
-      "VIX Index[0:1]      0.9381  3.616e-02     25.939 2.389e-148     [  0.867,  1.009]\n",
+      "VIX Index[0:1]      0.9381  3.616e-02     25.940 2.388e-148     [  0.867,  1.009]\n",
       "VIX Index[0:5]     -0.0262  3.774e-02     -0.693      0.488   [ -0.100,4.781e-02]\n",
-      "VIX Index[0:22]     0.0499  2.099e-02      2.380  1.733e-02 [8.809e-03,9.109e-02]\n",
+      "VIX Index[0:22]     0.0499  2.099e-02      2.380  1.733e-02 [8.810e-03,9.109e-02]\n",
       "=================================================================================\n",
       "\n",
       "Covariance estimator: robust\n"
@@ -422,10 +416,10 @@
    "execution_count": 9,
    "metadata": {
     "execution": {
-     "iopub.execute_input": "2023-10-16T10:28:30.066358Z",
-     "iopub.status.busy": "2023-10-16T10:28:30.065982Z",
-     "iopub.status.idle": "2023-10-16T10:28:30.169147Z",
-     "shell.execute_reply": "2023-10-16T10:28:30.167836Z"
+     "iopub.execute_input": "2023-12-20T21:47:25.012152Z",
+     "iopub.status.busy": "2023-12-20T21:47:25.011702Z",
+     "iopub.status.idle": "2023-12-20T21:47:25.087904Z",
+     "shell.execute_reply": "2023-12-20T21:47:25.086890Z"
     },
     "tags": []
    },
@@ -442,8 +436,8 @@
       "Distribution:                  Normal   AIC:                           3881.23\n",
       "Method:            Maximum Likelihood   BIC:                           3922.19\n",
       "                                        No. Observations:                 1237\n",
-      "Date:                Mon, Oct 16 2023   Df Residuals:                     1233\n",
-      "Time:                        10:28:30   Df Model:                            4\n",
+      "Date:                Wed, Dec 20 2023   Df Residuals:                     1233\n",
+      "Time:                        21:47:25   Df Model:                            4\n",
       "                                   Mean Model                                   \n",
       "================================================================================\n",
       "                      coef    std err          t      P>|t|     95.0% Conf. Int.\n",
diff --git a/devel/univariate/univariate_volatility_forecasting.html b/devel/univariate/univariate_volatility_forecasting.html
index 0b0d88ed05..9810e0c27d 100644
--- a/devel/univariate/univariate_volatility_forecasting.html
+++ b/devel/univariate/univariate_volatility_forecasting.html
@@ -15,7 +15,7 @@
     
   
       
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diff --git a/devel/univariate/univariate_volatility_modeling.html b/devel/univariate/univariate_volatility_modeling.html
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-        <link rel="stylesheet" type="text/css" href="../_static/sphinx_immaterial_theme.79d3faa9841bcddc6.min.css?v=e88de5f6" />
+        <link rel="stylesheet" type="text/css" href="../_static/sphinx_immaterial_theme.af531f03affe68837.min.css?v=2410bb73" />
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+    <a href="../index.html" title="arch v6.2.1 (+23)" class="md-nav__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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+    arch v6.2.1 (+23)
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diff --git a/devel/univariate/univariate_volatility_scenarios.html b/devel/univariate/univariate_volatility_scenarios.html
index 99c4d6a213..7c34ce96b8 100644
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+    <a href="../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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+            arch v6.2.1 (+23)
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+    arch v6.2.1 (+23)
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+    <a href="../index.html" title="arch v6.2.1 (+23)" class="md-header__button md-logo" aria-label="arch v6.2.1 (+23)" data-md-component="logo">
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diff --git a/devel/univariate/volatility.html b/devel/univariate/volatility.html
index b2cae2314f..4759576978 100644
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@@ -47,7 +47,7 @@
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-    arch v6.2.1 (+21)
+    arch v6.2.1 (+23)
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@@ -1322,7 +1322,7 @@ <h2 id="writing-new-volatility-processes">Writing New Volatility Processes<a cla
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