FR_E20 Model

# Cut-and-Paste code below into window above and Run

#

# FR_E20 BAU Model

#

# Measurement Matrix   (U-N-HOURS), (Q+N+L-XREAL), (X-XREAL-L)

#           Q       N       U    HOURS   XREAL       X       L

#[1,] 0.07936 -0.3177 0.47904 -0.47560  0.3715 0.40091  0.3720

#[2,] 0.71501  0.5396 0.04924 -0.02826 -0.2458 0.09277  0.3541

#[3,] 0.02453 -0.1482 0.05026 -0.14389 -0.6173 0.62087 -0.4332

#

# Fraction of Variance 

#[1] 0.6182 0.8866 0.9925 1.0000 1.0000 1.0000 1.0000

#

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]}

require(dse)

require(matlab)

f <- matrix( c( 0.988380071, -0.0002959452, -0.005083523,  0.13813147,

                      0.069626728,  1.0976612946,  0.148737427,  0.08937210,

                     -0.005407648, -0.1521641534,  1.00680239,1 -0.07212518,

0.00000000,  0.0000000,  0.0000000,  1.0000000000

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

#

# To Stabilize

# Uncomment next line

# f[1,1] <- 0.930540217; f[2,2] <-  1.0334263197; f[3,3] <- 0.947884466

#

h <- eye(3,4)

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

FR_E20 <- SS(F=f,H=h,K=k,z0=c(0.2749981,  0.1863623, -0.15908724,  1.0000000000),

              output.names=c("FR1","FR2","FR3"))

print(FR_E20)

is.SS(FR_E20)

stability(m0 <- SS(F=f[1:3,1:3,drop=FALSE],H=eye(3),Q=eye(3),R=eye(3)))

FR_E20.data <- simulate(FR_E20,sampleT=50,start=1900)

#FR_E20_GD.data <- simulate(FR_E20,sampleT=50,noise=matrix(0,50,3),start=1900)

FR_E20.f <- forecast(m <- l(FR_E20,FR_E20.data),horizon=150)

tfplot(FR_E20.f)

AIC(m)

shockDecomposition(m0)