\name{MultiGASFor}
\alias{MultiGASFor}
\alias{mGASMultiForcast}
\title{
	Forecast with multivariate GAS models
}
\description{
	Forecast with multivariate GAS models. One-step ahead prediction of the conditional density
	is available in closed form. Multistep ahead prediction are performed by simulation as detailed in Blasques et al. (2016).
}
\usage{
MultiGASFor(mGASFit, H = NULL, Roll = FALSE, out = NULL, B = 10000,
                     Bands = c(0.1, 0.15, 0.85, 0.9), ReturnDraws = FALSE)
}
\arguments{
\item{mGASFit}{ An object of the class \link{mGASFit} created using the function \link{MultiGASFit}}
%
\item{H}{ \code{numeric} Forecast horizon. Ignored if \code{Roll = TRUE}}
\item{Roll}{ \code{logical} Forecast should be made using a rolling procedure ? Note that if \code{Roll = TRUE}, then \code{out} has to be specified.}
\item{out}{ \code{matrix} of out of sample observation of dimension H x N for rolling forecast. N refers to the cross sectional dimension.}
\item{B}{ \code{numeric} Number of draws from the iH-step ahead distribution if \code{Roll = FALSE}.}
\item{Bands}{ \code{numeric} Vector of probabilities representing the confidence band levels for multistep ahead parameters forecasts. Only if \code{Roll = FALSE}.}
\item{ReturnDraws}{ \code{logical} Return the draws from the multistep ahead predictive distribution when \code{Roll = FALSE} ?}
%
}
\value{
An object of the class \link{mGASFor}
}
\references{
Blasques F, Koopman SJ, Lasak K, and Lucas, A (2016).
"In-sample Confidence Bands and Out-of-Sample Forecast Bands for Time-Varying Parameters in Observation-Driven Models."
International Journal of Forecasting, 32(3), 875-887.
\doi{10.1016/j.ijforecast.2016.04.002}.
}
\author{Leopoldo Catania}
\examples{
\dontrun{
# Specify a GAS model with multivatiate  Student-t conditional
# distribution and time-varying scales and correlations.

# Stock returns forecast

set.seed(123)

data("StockIndices")

mY = StockIndices[, 1:2]

# Specification mvt
GASSpec = MultiGASSpec(Dist = "mvt", ScalingType = "Identity",
                       GASPar = list(location = FALSE, scale = TRUE,
                                     correlation = TRUE, shape = FALSE))

# Perform H-step ahead forecast with confidence bands

# Estimation
Fit = MultiGASFit(GASSpec, mY)

# Forecast
Forecast  = MultiGASFor(Fit, H = 50)

Forecast

# Perform 1-Step ahead rolling forecast

InSampleData  = mY[1:1000, ]
OutSampleData = mY[1001:2404, ]

# Estimation
Fit = MultiGASFit(GASSpec, InSampleData)

Forecast  = MultiGASFor(Fit, Roll = TRUE, out = OutSampleData)

Forecast
}
}