# The data used is contained in Splus
#help(policy.dat)

#Let's look at a few basic plots
pol.mat = as.matrix(seriesData(policy.dat))
polsub.mat<-cbind(pol.mat[,3],pol.mat[,6])
tsplot(polsub.mat)
title("TS Plot of Federal Fund Rate and Civilian Unemployment Rate")
acf(polsub.mat)

#Now let's model the series with an VAR of order 6
pol.mod = VAR(cbind(FFR,U)~ar(6),data=policy.dat)
summary.VAR(pol.mod)
plot.VAR(pol.mod,ask=F)

# I would like to see the cross-correlation structure as well.
pol.res<-as.matrix(residuals.VAR(pol.mod))
acf(pol.res)
tsplot(pol.res)
title("TS plot of Residuals from fitted VAR")

# Let's see the confidence bands
pol.forecast<-predict(pol.mod,12)
plot.forecast(pol.forecast, policy.dat[,c("FFR","U")],n.old=20, hgrid=T, vgrid=T)
title("12 Step Ahead Forecast - Confidence Bands at 1 Std. Error")
plot(pol.forecast, policy.dat[,c("FFR","U")],n.old=50, hgrid=T, vgrid=T)
title("12 Step Ahead Forecast - Confidence Bands at 1 Std. Error")

# simulation forecasts from the fitted VAR
sim.pred = simulate(pol.mod, n=10, n.rep=1000)
apply(sim.pred, 2, rowMeans)