JP19 Model BAU

#

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

#

# JP19 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), (Q-L-HOURS-U), (Q-N)   MARX-MALTHUS

#          Q       N       U   HOURS       L

#[1,] 0.3648  0.4663  0.4653  0.4648  0.4657

#[2,] 0.8617  0.1588 -0.2794 -0.2832 -0.2722

#[3,] 0.3523 -0.8690  0.1601  0.2347  0.1999

#

# Fraction of Variance 

#[1] 0.8833 0.9932 1.0000 1.0000 1.0000

#

f <- matrix( c( 1.011355376,  0.007412169, 0.07951812,  0.075736431,

                      0.018129715,  0.994002927, 0.14393299, -0.001908968,

                     -0.004141524, -0.028884237, 1.02660256, -0.009851419,

                      0.000000000,  0.000000000, 0.00000000,  1.000000000

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

#

# To Stabilize Model, Uncomment Next Line

#       f[1,1] <- f[3,3] <- f[2,2]

#

h <- eye(3,4)

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

#

JP19 <- SS(F=f,H=h,K=k,z0=c(0.075736431, -0.001908968, -0.009851419,  1.0000000000),

              output.names=c("JP1","JP2","JP3"))

print(JP19)

#

is.SS(JP19)

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

# tfplot(simulate(JP19,sampleT=100))

JP19.data <- simulate(JP19,sampleT=150,noise=matrix(0,150,3),start=1800)

JP19.f <- forecast(m1<-l(JP19,JP19.data),horizon=150)

tfplot(JP19.f)

AIC(m1)