\name{uc.forecast}
\alias{uc.forecast}
%\alias{uc.forecast.VAR}
%\alias{uc.forecast.BVAR}
%\alias{uc.forecast.BSVAR}
%- Also NEED an '\alias' for EACH other topic documented here.
\title{ Forecast density estimation unconditional forecasts for VAR/BVAR/BSVAR
  models via MCMC }
\description{
  Implements unconditional forecast density estimator for VAR/BVAR/BSVAR models
  described in Waggoner and Zha (1999).  The unconditional forecasts
  place no restriction on the paths of the forecasts
  (cf. \code{hc.forecast}).  The forecast densities are
  estimated as the posterior sample for the VAR/BVAR/BSVAR model using Markov Chain
  Monte Carlo with data augmentation to account for the uncertainty of
  the forecasts and the parameters.  This function DOES account for
  parameter uncertainty in the MCMC algorithm.
}
\usage{
uc.forecast(varobj, nsteps, burnin, gibbs, exog = NULL)
}
%- maybe also 'usage' for other objects documented here.
\arguments{
  \item{varobj}{ VAR or BVAR object produced for a VAR or BVAR
    using \code{szbvar} or  \code{reduced.form.var}}
  \item{nsteps}{ Number of periods in the forecast horizon } 
  \item{burnin}{ Burnin cycles for the MCMC algorithm }
  \item{gibbs}{ Number of cycles of the Gibbs sampler after the
    \code{burnin} that are returned in the output}
  \item{exog}{ num.exog x nsteps matrix of the exogenous variable values
    for the forecast horizon.  If left at the \code{NULL} default, they
    are set to zero.}
}
\details{
  Produces a posterior sample of unconstrained VAR/BVAR/BSVAR forecasts via MCMC.
  This function accounts for the uncertainty of the VAR/BVAR/BSVAR parameters by
  sampling from them in the computation of the VAR/BVAR/BSVAR forecasts and then
  regenerating the forecasts.  Data augmentation is used to account for
  the impact of the forecast uncertainty on the parameters.
}
\value{
  A list with three components:

  \item{yforc }{Forecast sample}
  \item{orig.y }{Original endogenous variables time series}
  \item{hyperp }{values of the hyperparameters used in the BVAR
    estimation / MCMC}
}
\references{ Brandt, Patrick T. and John R. Freeman. 2006. "Advances in
  Bayesian  Time Series Modeling and the Study of Politics: Theory
  Testing, Forecasting, and Policy Analysis"
  \emph{Political Analysis} 14(1):1-36.

  Waggoner, Daniel F. and Tao Zha. 1999. "Conditional
  Forecasts in Dynamic Multivariate Models" \emph{Review of Economics and
  Statistics}, 81(4):639-651.}
\author{ Patrick T. Brandt}
%\note{ }

\seealso{ \code{\link{hc.forecast}}}
\examples{
\dontrun{
## Uses the example from Brandt and Freeman 2006.  Will not run unless
## you have their data from http://yule.utdallas.edu or the Politcal
## Analysis website!
library(MSBVAR)   # Brandt's package for Bayesian VAR models

# Read the data and set up as a time series
data <- read.dta("levant.weekly.79-03.dta") 
attach(data)

# Set up KEDS data
KEDS.data <- ts(cbind(a2i,a2p,i2a,p2a,i2p,p2i),
                start=c(1979,15),
                freq=52,
                names=c("A2I","A2P","I2A","P2A","I2P","P2I"))

# Select the sample we want to use.
KEDS <- window(KEDS.data, end=c(1988,50))


################################################################################
# Estimate the BVAR models 
################################################################################

# Fit a flat prior model
KEDS.BVAR.flat <- szbvar(KEDS, p=6, z=NULL, lambda0=1,
                         lambda1=1, lambda3=1, lambda4=1, lambda5=0,
                         mu5=0, mu6=0, nu=0, qm=4, prior=2,
                         posterior.fit=F)

# Reference prior model -- Normal-IW prior pdf
KEDS.BVAR.informed <- szbvar(KEDS, p=6, z=NULL, lambda0=0.6,
                             lambda1=0.1, lambda3=2, lambda4=0.5, lambda5=0,
                             mu5=0, mu6=0, nu=ncol(KEDS)+1, qm=4, prior=0,
                             posterior.fit=F)

# Set up conditional forecast matrix conditions
nsteps <- 12
a2i.condition <- rep(mean(KEDS[,1]) + sqrt(var(KEDS[,1])) , nsteps)

yhat<-matrix(c(a2i.condition,rep(0, nsteps*5)), ncol=6)

# Set the random number seed so we can replicate the results.
set.seed(11023)

# Conditional forecasts
conditional.forcs.ref <- hc.forecast(KEDS.BVAR.informed, yhat, nsteps,
                            burnin=3000, gibbs=5000, exog=NULL)

conditional.forcs.flat <- hc.forecast(KEDS.BVAR.flat, yhat, nsteps,
                             burnin=3000, gibbs=5000, exog=NULL)

# Unconditional forecasts
unconditional.forcs.ref <-uc.forecast(KEDS.BVAR.informed, nsteps,
                                          burnin=3000, gibbs=5000)

unconditional.forcs.flat <- uc.forecast(KEDS.BVAR.flat, nsteps,
                                            burnin=3000, gibbs=5000)

# Set-up and plot the unconditional and conditional forecasts.  This
# code pulls for the forecasts for I2P and P2I and puts them into the
# appropriate array for the figures we want to generate.
uc.flat <- NULL
hc.flat <- NULL
uc.ref <- NULL
hc.ref <- NULL

uc.flat$forecast <- unconditional.forcs.flat$forecast[,,5:6]
hc.flat$forecast <- conditional.forcs.flat$forecast[,,5:6]
uc.ref$forecast <- unconditional.forcs.ref$forecast[,,5:6]
hc.ref$forecast <- conditional.forcs.ref$forecast[,,5:6]

par(mfrow=c(2,2), omi=c(0.25,0.5,0.25,0.25)) 
plot(uc.flat,hc.flat, probs=c(0.16, 0.84), varnames=c("I2P", "P2I"),
     compare.level=KEDS[nrow(KEDS),5:6], lwd=2)
plot(hc.ref,hc.flat, probs=c(0.16, 0.84), varnames=c("I2P", "P2I"),
     compare.level=KEDS[nrow(KEDS),5:6], lwd=2)

}

}
\keyword{ ts}
\keyword{ models}