UK18 Model

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

#

# UK18 BAU Model United Kingdom

#

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)

#

# measurementModel overall-XREAL, HOURS+XREAL-N, U+XREAL-HOURS, Q-N

#

# 

#          Q      N      U  HOURS  XREAL

#[1,]  0.473  0.463  0.465  0.375 -0.452

#[2,] -0.155 -0.315  0.200  0.821  0.403

#[3,]  0.186 -0.145  0.744 -0.426  0.457

#[4,]  0.528  0.421 -0.398  0.054  0.618

#

# Fraction of Variance 

#[1] 0.881 0.991 1.000 1.000 1.000

f <- matrix( c( 1.030137537,  0.01728729, 0.04818602,  0.083378905,

                       0.035599341,  1.04593172, 0.07941981,  0.016958610,

                      -0.002368278, -0.02175487, 0.99175520, -0.005868245,

0.00000000,  0.0000000,  0.0000000,  1.0000000000

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

#

# To Stabilize, Uncomment next line

# f[1,1] <- f[2,2] <- 0.90

#

h <- eye(3,4)

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

UK18 <- SS(F=f,H=h,K=k,z0=c(0.083378905,  0.016958610, -0.005868245,  1.0000000000),

              output.names=c("UK1","UK2","UK3"))

print(UK18)

is.SS(UK18)

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

# tfplot(simulate(UK18,sampleT=100))

UK18.data <- simulate(UK18,sampleT=100,noise=matrix(0,100,3),start=1700)

UK18.f <- forecast(l(UK18,UK18.data),horizon=150)

tfplot(UK18.f)

AIC(l(UK18,UK18.data))

shockDecomposition(m)