DE18 Model

# Cut-and-Paste Code Below into Window Above and Run

#

# DE18 BAU Model Germany

#

merge.forecast <- function (fx,n=1) {

x <- splice(fx$pred,fx$forecast[[n]])

colnames(x) <- seriesNames(fx$data$output)

return(x)

}

AIC <- function(model) {informationTestsCalculations(model)[3]}

require(dse)

require(matlab)

#

# Measurement Matrix # overall-U, N+U, Q-N

#           Q      N      U

#[1,]  0.7160  0.673 -0.185

#[2,] -0.0762  0.338  0.938

#[3,]  0.6939 -0.657  0.294

#

# Fraction of Variance 

#[1] 0.646 1.000 1.000

#

f <- matrix( c( 0.9890205004, -0.018085499,  0.2686420, 0.039158333,

                      0.0498847020,  1.042672779, -0.3701197, 0.021842806,

                     -0.0003267551,  0.001305037 , 0.9683067, 0.000669039,

            0.00000000,  0.0000000,  0.0000000,  1.0000000000

),byrow=TRUE,nrow=4,ncol=4)

#

# To Stabilize, Uncomment next line

# f[2,2] <- 0.90

#

h <- eye(3,4)

k <- (f[,1:3,drop=FALSE])

DE18 <- SS(F=f,H=h,K=k,z0=c(0.039158333, 0.021842806, 0.000669039,  1.0000000000),

              output.names=c("DE1","DE2","DE3"))

print(DE18)

is.SS(DE18)

stability(m <- SS(F=f[1:3,1:3,drop=FALSE],Q=eye(3),R=eye(3),H=eye(3)))

# tfplot(simulate(DE18,sampleT=100))

DE18.data <- simulate(DE18,sampleT=100,noise=matrix(0,100,3),start=1700)

DE18.f <- forecast(l(DE18,DE18.data),horizon=150)

tfplot(DE18.f)

AIC(l(DE18,DE18.data))

shockDecomposition(m)