\name{UniGASSim}
\alias{UniGASSim}
\title{
	Simulate Univariate GAS processes
}
\description{
	Simulate Univariate GAS processes.
}
\usage{
UniGASSim(fit = NULL, T.sim = 1000,
          kappa = NULL, A = NULL, B = NULL, Dist = NULL, ScalingType = NULL)
}
\arguments{
\item{fit}{
An estimated object of the class \link{uGASFit}. By default \code{fit = NULL}.
}
%
\item{T.sim}{
\code{numeric} Length of the simulated time series.}
%
\item{kappa}{
\code{numeric} Vector of unconditional level for the reparametrised vector of parameters. Only used if \code{fit = NULL}}
%

\item{A}{
\code{matrix} Of coefficients of dimension K x K that premultiply the conditional
score in the GAS updating recursion, see Details. Only used if \code{fit = NULL}}
%

\item{B}{
\code{matrix} Of autoregressive coefficients of dimension K x K, see Details. Only used if \code{fit = NULL}}
%
\item{Dist}{
\code{character} Label of the conditional distribution, see \link{DistInfo}. Only used if \code{fit = NULL}}
%
\item{ScalingType}{
\code{character} Indicating the scaling mechanism for the conditional score.
Possible choices are \code{"Identity"}, \code{"Inv"}, \code{"InvSqrt"}.
Note that, for some distribution only \code{ScalingType = "Identity"} is supported, see the function \link{DistInfo}.
When \code{ScalingType = "InvSqrt"} the inverse of the Cholesky decomposition of the
information matrix is used. Default value \code{ScalingType = "Identity"}. Only used if \code{fit = NULL}}
%
}
\details{

The function permits to simulate from an estimated \link{uGASFit} object. If \code{fit} is not provided, the user
can specify a GAS model via the additional arguments \code{kappa}, \code{A}, \code{B}, \code{Dist} and \code{ScalingType}.

	All the information regarding the supported univariate conditional distributions can be investigated
	using the \link{DistInfo} function. The model is specified as \deqn{y_{t}\sim p(y|\theta_{t})},
	where \eqn{\theta_{t}} is the vector of parameters for the density \eqn{p(y|.)}. Note that, \eqn{\theta_{t}} includes
	also those parameters that are not time-varying.
	The GAS recursion for \eqn{\theta_{t}} is \deqn{\theta_{t} = \Lambda(\tilde{\theta}_{t})},\deqn{\tilde{\theta}_{t}=\kappa + A*s_{t-1} + B*\tilde{\theta}_{t-1}},
	where \eqn{\Lambda(.)} is the mapping function (see \link{UniMapParameters}) and \eqn{\tilde{\theta}_{t}} is the vector of
	reparametrised parameters. The process is initialized at \eqn{\theta_{1}=(I - B)^{-1}\kappa}, where \eqn{\kappa} is
	the \code{vKappa} vector. The vector \eqn{s_{t}} is the scaled score of \eqn{p(y|.)} with respect to \eqn{\tilde{\theta}_{t}}.
	See Ardia et. al. (2016a) for further details.
}
\value{
An object of the class \link{uGASSim}.
}
\references{
Ardia D, Boudt K and Catania L (2016a).
"Generalized Autoregressive Score Models in R: The GAS Package."
\url{http://ssrn.com/abstract=2825380}.\cr

Creal D, Koopman SJ, Lucas A (2013).
"Generalized Autoregressive Score Models with Applications."
Journal of Applied Econometrics, 28(5), 777-795.
\doi{10.1002/jae.1279}.\cr

Harvey AC (2013).
Dynamic Models for Volatility and Heavy Tails: With Applications to Financial and Economic Time Series.
Cambridge University Press.
}
\author{Leopoldo Catania}
\examples{
# Simulate from a GAS process with Student-t conditional
# distribution, time-varying location, scale and fixed shape parameter.

library(GAS)

set.seed(786)

T.sim  = 1000 # number of observations to simulate
Dist   = "std" # conditional Studen-t distribution


# vector of unconditional reparametrised parameters such that, the unconditional level of
# \eqn{\theta}_{t} is (0, 1.5 ,7), i.e. location = 0, scale = 1.5,
# degrees of freedom = 7.

kappa = c(0.0, log(1.5), log(7-2.01))

# in this way we specify that the shape parameter is constant while the score
# coefficients for the location and the scale
# parameters are 0.001 and 0.01, respectively.

A = matrix(c(0.001 , 0.0  , 0.0 ,
             0.0   , 0.01 , 0.0 ,
             0.0   , 0.0  , 0.0 ), 3, byrow = TRUE)

B = matrix(c(0.7 , 0.0 , 0.0 ,
              0.0 , 0.98, 0.0 ,
              0.0 , 0.0 , 0.0),3,byrow = TRUE) # Matrix of autoregressive parameters.

Sim = UniGASSim(fit = NULL, T.sim, kappa, A, B, Dist, ScalingType = "Identity")

Sim
}