UKL19D Model

# Cut-and-paste text below into window above and Run

#

# UK19D World Input Model

#

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, X-U-N, XREAL+Q-X-L-U     Export-led growth

#

#          Q      N      U  HOURS XREAL      X      L

#[1,]  0.378  0.378  0.378  0.378 0.379  0.376  0.379

#[2,] -0.265 -0.290 -0.464  0.276 0.213  0.695 -0.162

#[3,]  0.316  0.138 -0.338 -0.261 0.725 -0.245 -0.337

#

# Fraction of Variance 

#[1] 0.995 1.000 1.000 1.000 1.000 1.000 1.000

#

f <- matrix( c(0.9829784746, 0.12195456, -0.07352879,  0.232572955,

                    -0.0143996650, 1.06038401, -0.02118443, -0.006691086,

                     0.0002142174, 0.04862518,  0.95817519, -0.004001837,

0.00000000,  0.0000000,  0.0000000,  1.0000000000

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

#

# To stabilize the Model, uncomment next line

# f[2,2] <- 0.90

#

h <- eye(3,4)

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

UKL19D <- SS(F=f,H=h,K=k,z0=c(0.232572955, -0.006691086, -0.004001837,  1.0000000000),

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

print(UKL19D)

is.SS(UKL19D)

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

shockDecomposition(m)

# tfplot(simulate(UKL19D,sampleT=150))

UKL19D.data <- simulate(UKL19D,sampleT=150,noise=matrix(0,150,3))

UKL19D.f <- forecast(l(UKL19D,UKL19D.data),horizon=150)

tfplot(UKL19D.f)

AIC(l(UKL19D,UKL19D.data))