WE19 Model

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

#

# WE19 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), (Q+XREAL-N-HOURS), (N-HOURS-L),   ( Q-X ), (Q-XREAL), (L-HOURS)

#

#          Q       N        U   HOURS     XREAL       X      L

#[1,]  0.378  0.3887  0.38394  0.3745  3.75e-01  0.3698  0.375

#[2,]  0.372 -0.1350 -0.27344 -0.4299  4.20e-01  0.4806 -0.420

#[3,]  0.570  0.2639  0.22082 -0.2465  3.85e-02 -0.6731 -0.203

#

#[4,]  0.399  0.1304  0.00652 -0.0552 -8.25e-01  0.3740 -0.033

#[5,]  0.345 -0.0313 -0.79250  0.2691  3.68e-02 -0.1896  0.377

#[6,] -0.321  0.7587 -0.25074 -0.4673  1.24e-02  0.0575  0.191

#[7,] -0.109  0.4092 -0.19516  0.5659  4.12e-05  0.0011 -0.680

#

# Fraction of Variance 

#[1] 0.937 0.999 1.000 1.000 1.000 1.000 1.000

#

f <- matrix( c(1.014919414, -0.0163225653, 0.02126275,  0.0856707663,

                      0.014061639,  0.9914806789, 0.04256366, -0.0025942872,

                      0.002171782,  0.0008180604, 1.00676880,  0.0009502395,

0.00000000,  0.0000000,  0.0000000,  1.0000000000

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

#

# To Stabilize the Model, Uncomment next line

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

#

h <- eye(3,4)

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

WE19 <- SS(F=f,H=h,K=k,z0=c(0.0856707663, -0.0025942872,  0.0009502395,  1.0000000000),

              output.names=c("WE1","WE2","WE3"))

print(WE19)

is.SS(WE19)

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

# tfplot(simulate(WE19,sampleT=150))

WE19.data <- simulate(WE19,sampleT=100,noise=matrix(0,150,3),start=1800)

WE19.f <- forecast(m1<-l(WE19,WE19.data),horizon=150)

tfplot(WE19.f)