inst/ExtensionTests/run_Simulate.R

In GAS: Generalized Autoregressive Score Models

library("GAS")
## Simulate GAS process
set.seed(123)
################################
#         UNIVARIATE           #
################################

# norm

iT = 1e4

help(UniUnmapParameters)

A = matrix(c(0.1,0.0,
              0.0,0.4),2)
B = matrix(c(0.9,0.0,
              0.0,0.95),2)

Kappa = (diag(2) - B) %*% UniUnmapParameters(c(0, 0.1), "norm")

help(UniGASSim)
Sim = UniGASSim(fit = NULL, iT, Kappa, A, B, Dist = "norm", ScalingType = "Identity")

# snorm

iT = 1e3

A = matrix(c(0.0 , 0.0 , 0.0 ,
              0.0 , 0.1 , 0.0 ,
              0.0 , 0.0 , 0.0),3,byrow = T)

B = matrix(c(0.7 , 0.0 , 0.0 ,
              0.0 , 0.98, 0.0 ,
              0.0 , 0.0 , 0.97),3,byrow = T)

Kappa = (diag(3) - B) %*% UniUnmapParameters(c(0, 0.1, 1.1), "snorm")

Sim = UniGASSim(fit = NULL, iT, Kappa, A, B, Dist = "snorm", ScalingType = "Identity")

# plot(Sim)

# std

iT = 1e3
A = matrix(c(0.001 , 0.0 , 0.0 ,
              0.0 , 0.01 , 0.0 ,
              0.0 , 0.0 , 0.04),3,byrow = T)

B = matrix(c(0.7 , 0.0 , 0.0 ,
              0.0 , 0.98, 0.0 ,
              0.0 , 0.0 , 0.97),3,byrow = T)

Kappa = (diag(3) - B) %*% UniUnmapParameters(c(0, 0.1, 8), "std")

Sim = UniGASSim(fit = NULL, iT, Kappa, A, B, Dist = "std", ScalingType = "Identity")

# plot(Sim)

# sstd

iT = 1e3

A = matrix(c(0.0 , 0.0 , 0.0 , 0.0,
              0.0 , 0.1 , 0.0 , 0.0,
              0.0 , 0.0 , 0.00,  0.0,
              0.0 , 0.0 , 0.00,  0.0),4,byrow = T)

B = matrix(c(0.0 , 0.0 , 0.0 , 0.0,
              0.0 , 0.98, 0.0 , 0.0,
              0.0 , 0.0 , 0.00, 0.0,
              0.0 , 0.0 , 0.00, 0.0),4,byrow = T)

Kappa = (diag(4) - B) %*% UniUnmapParameters(c(0, 0.1, 1.1, 8), "sstd")

Sim = UniGASSim(fit = NULL, iT, Kappa, A, B, Dist = "sstd", ScalingType = "Identity")

# poi

iT = 1e4

A = matrix(c(0.05), 1)
B = matrix(c(0.94), 1)

Kappa = (1 - B) * log(5)

Sim = UniGASSim(fit = NULL, iT, Kappa, A, B, Dist = "poi", ScalingType = "Identity")

# plot(Sim)

# beta

iT = 1e4

A = matrix(c(0.1, 0.0,
              0.0, 0.4), 2) * 0.01

B = matrix(c(0.98, 0.0,
              0.0, 0.99), 2)

Kappa = (diag(2) - B) %*% c(log(0.1), log(0.4))


Sim = UniGASSim(fit = NULL, iT, Kappa, A, B, Dist = "beta", ScalingType = "Identity")

# negative binomial

iT = 1e4

A = matrix(c(7.5, 0.0,
             0.0, 0.0), 2)

B = matrix(c(0.99, 0.0,
             0.0, 0.0), 2)

Kappa = (diag(2) - B) %*% c(log(0.8/0.2), log(2))


Sim = UniGASSim(fit = NULL, iT, Kappa, A, B, Dist = "negbin", ScalingType = "Inv")

# plot(Sim)

################################
#        MULTIVARIATE          #
################################

# Simulate from a GAS process with Multivariate Student-t conditional distribution, time-varying locations, scales, correlations and fixed shape parameter.
library(GAS)

set.seed(786)

iT   = 1000 # number of observations to simulate
N    = 3     # trivariate series
Dist = "mvt" # conditional Multivariate Studen-t distribution

# build unconditional vector of reparametrised parameters

Mu  = c(0.1,0.2,0.3)   # vector of location parameters (this is not transformed)
Phi = c(1.0, 1.2, 0.3) # vector of scale parameters for the firs, second and third variables.

Rho = c(0.1,0.2,0.3)   # This represents vec(R), where R is the correlation matrix.
# Note that is up to the user to ensure that vec(R) implies a proper correlation matrix

Theta = c(Mu, Phi, Rho, 7) # vector of parameters such that the degrees of freedom are 7.

A     = matrix(0, length(Theta), length(Theta))

diag(A) = c(0, 0, 0, 0.05, 0.01, 0.09, 0.01, 0.04, 0.07, 0) # update scales and correlations, do not update locations and shape parameters

B     = matrix(0, length(Theta), length(Theta))

diag(B) = c(0, 0, 0, 0.7, 0.7, 0.5, 0.94, 0.97, 0.92, 0) # update scales and correlations, do not update locations and shape parameters

Kappa = (diag(length(Theta)) - B) %*% MultiUnmapParameters(Theta, Dist, N)

help(MultiGASSim)

Sim = MultiGASSim(fit = NULL, iT, N, Kappa, A, B, Dist, ScalingType = "Identity")

Sim

# plot(Sim)