Update replicaation scripts (#131)

Project.toml

@@ -1,7 +1,7 @@
 name = "ScoreDrivenModels"
 uuid = "4a87933e-d659-11e9-0e65-7f40dedd4a3a"
 authors = ["guilhermebodin <[email protected]>, raphaelsaavedra <[email protected]>"]
-version = "0.1.8"
+version = "0.1.7"
 
 [deps]
 Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"

examples/R_replication_scripts/cpichg.R

@@ -0,0 +1,5 @@
+library("GAS")
+
+GASSpec <- UniGASSpec(Dist = "std", ScalingType = "Identity", GASPar = list(location = TRUE, scale = TRUE, shape = FALSE))
+cpichg <- read.csv("../../test/data/cpichg.csv", header = FALSE)
+Fit <- UniGASFit(GASSpec, cpichg$V1)

examples/R_replication_scripts/garch.R

@@ -0,0 +1,7 @@
+library("rugarch")
+bg96 <- read.csv("./test/data/BG96.csv", header = FALSE)
+
+spec = ugarchspec(mean.model = list(armaOrder = c(0, 0), include.mean = FALSE, archm = FALSE,
+archpow = 1, arfima = FALSE, external.regressors = NULL, archex = FALSE))
+fit = ugarchfit(data = bg96$V1, spec = spec)
+coef(fit)

examples/cpichg.jl

@@ -1,4 +1,4 @@
-using DelimitedFiles, Random, ScoreDrivenModels, Statistics
+using DelimitedFiles, Random, ScoreDrivenModels, Statistics, Plots
 
 # Load historical Consumer Price Index data
 y = vec(readdlm("../test/data/cpichg.csv"))
@@ -39,10 +39,15 @@ forec.parameter_forecast
 # Similarly, we can access the simulated observation scenarios
 forec.observation_scenarios
 
-gas_t = Model(1, 1, TDistLocationScale, 0.0; time_varying_params = [1])
+gas_t = Model(1, 1, TDistLocationScale, 1.0; time_varying_params = [1])
 steps_ahead = 50
 first_idx = 150
 b_t = backtest(gas_t, y, steps_ahead, first_idx)
 plot(b_t, "GAS(1, 1) Student t")
-using Plots
-plot!(b_t, "GAS(1, 1) Student t")
+
+gas_t_1_2 = Model(1, 2, TDistLocationScale, 1.0; time_varying_params = [1])
+steps_ahead = 50
+first_idx = 150
+b_t_1_2 = backtest(gas_t_1_2, y, steps_ahead, first_idx)
+plot(b_t, "GAS(1, 1) Student t");
+plot!(b_t_1_2, "GAS(1, 2) Student t"; legend = :topleft)

examples/introduction.jl

@@ -0,0 +1,7 @@
+using ScoreDrivenModels
+
+Model(1, 2, LogNormal, 0.5)
+
+Model(1, 2, LogNormal, 0.5; time_varying_params = [1])
+
+Model([1, 12], [1, 12], LogNormal, 0.5)

src/prints.jl

@@ -6,7 +6,6 @@ function Base.show(io::IO, est::EstimationStats{D, T}) where {D, T}
     println(io, "Log-likelihood:               ", @sprintf("%.4f", est.loglikelihood))
     println(io, "AIC:                          ", @sprintf("%.4f", est.aic))
     println(io, "BIC:                          ", @sprintf("%.4f", est.bic))
-    println(io, "Jarque Bera p value:          ", @sprintf("%.4f", est.jarquebera_p_value))
     print_coefs_stats(est.coefs_stats)
     return nothing
 end