UKL19 Model

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

#

# UK19 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 (Growth), (XREAL-L), (X-XREAL)

#

#          Q       N       U  HOURS  XREAL     X      L

#[1,]  0.384  0.3864  0.3823  0.375  0.371 0.370  0.376

#[2,]  0.198 -0.0571 -0.2698 -0.442  0.502 0.507 -0.425

#[3,] -0.387 -0.2090 -0.0438  0.196 -0.407 0.767  0.106

#

# Fraction of Variance 

#[1] 0.956 0.999 1.000 1.000 1.000 1.000 1.000

#

f <- matrix( c(1.013050980, -0.016807108, 0.1068859,  0.086172579,

                      0.011893936,  0.989544519, 0.0923792, -0.002781924,

                     -0.002394937, -0.007515403, 1.0284871, -0.002150958,

0.00000000,  0.0000000,  0.0000000,  1.0000000000

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

#

# To Stabilize Model, Uncomment Next Line

# f[1,1] <- f[3,3] <- 0.90

#

h <- eye(3,4)

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

UK19 <- SS(F=f,H=h,K=k,z0=c( 0.086172579, -0.002781924, -0.002150958,  1.0000000000),

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

print(UK19)

is.SS(UK19)

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

# tfplot(simulate(UK19,sampleT=100))

UK19.data <- simulate(UK19,sampleT=100,noise=matrix(0,100,3),start=1800)

UK19.f <- forecast(m1<-l(UK19,UK19.data),horizon=100)

tfplot(UK19.f)

AIC(m1)