\name{UniGASFor}
\alias{UniGASFor}
\alias{uGASMultiForcast}
\title{
	Forecast with univariate GAS models
}
\description{
Forecast with univariate GAS models. The one-step ahead prediction of the conditional
density is available in closed form. The multi-step ahead prediction is performed by
simulation as detailed in Blasques et al. (2016).
}
\usage{
UniGASFor(uGASFit, H = NULL, Roll = FALSE, out = NULL, B = 10000,
                     Bands = c(0.1, 0.15, 0.85, 0.9), ReturnDraws = FALSE)
}
\arguments{
\item{uGASFit}{An object of the class \link{uGASFit} created using the function \link{UniGASFit}.}
%
\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{numeric} Vector of out-of-sample observation for rolling forecast.}
\item{B}{ \code{numeric} Number of draws from the H-step ahead distribution if \code{Roll = FALSE}.}
\item{Bands}{ \code{numeric} Vector of probabilities representing the confidence band levels for
multi-step ahead parameters forecasts. Only if \code{Roll = FALSE}.}
\item{ReturnDraws}{\code{logical} Return the draws from the multi-step ahead predictive distribution when \code{Roll = FALSE} ?}
%
}
\value{
An object of the class \link{uGASFor}.
}
\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{
# Specify an univariate GAS model with Student-t
# conditional distribution and time-varying location, scale and shape parameter

# Inflation Forecast

set.seed(123)

data("cpichg")

GASSpec = UniGASSpec(Dist = "std", ScalingType = "Identity",
                     GASPar = list(location = TRUE, scale = TRUE, shape = FALSE))

# Perform H-step ahead forecast with confidence bands

Fit = UniGASFit(GASSpec, cpichg)
Forecast = UniGASFor(Fit, H = 12)

Forecast

# Perform 1-Step ahead rolling forecast

InsampleData = cpichg[1:250]
OutSampleData = cpichg[251:276]

Fit = UniGASFit(GASSpec, InsampleData)

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

Forecast
}