require(dse)
require(matlab)
#
# USL20W BAU Model
#
# Measurement Matrix
# Q N HOURS XREAL X L
#[1,] 0.4124 0.403 0.413 0.410 0.399 0.412
#[2,] -0.1464 -0.580 -0.122 0.310 0.711 -0.160
#[3,] -0.0825 -0.546 0.137 0.358 -0.466 0.575
#
# Fraction of Variance
#[1] 0.977 0.999 1.000 1.000 1.000 1.000
#
merge.forecast <- function (fx,n=1) {
#
# Merges a forecast with the outputdata
x <- splice(fx$pred,fx$forecast[[n]])
colnames(x) <- seriesNames(fx$data$output)
return(x)
}
AIC <- function(model) {informationTestsCalculations(model)[3]}
f <- matrix( c( 1.026261160, -0.04873236, 0.04995449, 0.166789178,
0.023195483, 1.01292512 ,0.06694359, 0.004293523,
-0.002685722, -0.04074830, 1.04458649, -0.006797188,
0.000000000, 0.00000000, 0.0000000, 1.00000000
),byrow=TRUE,nrow=4,ncol=4)
h <- eye(3,4)
k <- f[1:4,1:3,drop=FALSE]
USL20 <- SS(F=f,H=h,K=k,z0=c( 0.166789178, 0.004293523, -0.006797188,
1.0000000),
output.names=c("US1","US2","US3"))
stability(USL20)
shockDecomposition(toSSChol(USL20))
USL20.data <- simulate(USL20,sampleT=50,start=1950)
m <- l(USL20,USL20.data)
# tfplot(m)
AIC(m)
USL20.f <- forecast(m,horizon=50)
USL20.fx <- merge.forecast(USL20.f)
tfplot(USL20.f)
#
# USL20c Growth Controlled
# by stabilizing growth rates
#
f[1,1] <- 1.02
f[2,2] <- 0.97
f[3,3] <- 0.97
#
USL20c <- SS(F=f,H=h,K=k,z0=c( 0.166789178, 0.004293523, -0.006797188,
1.0000000),
output.names=c("US1","US2","US3"))
stability(USL20c)
shockDecomposition(toSSChol(USL20c))
USL20c.data <- simulate(USL20c,sampleT=50,start=1950)
m <- l(USL20c,USL20c.data)
# tfplot(m)
AIC(m)
USL20c.f <- forecast(m,horizon=50)
USL20c.fx <- merge.forecast(USL20c.f)
tfplot(USL20c.f)