US_GD Model

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

#

# US_GD Model

#

# Measurement Matrix (Growth) (Hours+XREAL-X-Q) (Q+HOURS-XREAL-X)

#           Q        N  HOURS   XREAL       X        L

#[1,]  0.4041 0.414833 0.3981  0.4123  0.4053  0.41454

#[2,] -0.4774 0.009769 0.7037  0.2063 -0.4818  0.04558

#[3,]  0.7410 0.035540 0.2377 -0.2841 -0.5259 -0.18943

#

# Fraction of Variance 

#[1] 0.9684 0.9939 0.9988 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( 1.008952716, -0.04339303, 0.01050584, 0.17033656,

                        -0.016091116,  0.93613443, 0.17244557, 0.01340404,

                        0.009518643, -0.08083774, 1.13854519, 0.01724704,

0.00000000,  0.0000000,  0.0000000,  1.0000000000

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

#

# To Stabilize

# Uncomment next line

# f[1,1] <- 0.959503994; f[2,2] <- 0.89025453; f[3,3] <- 1.08274514

#

h <- eye(3,4)

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

US_GD <- SS(F=f,H=h,K=k,z0=c(0.17033656, 0.01340404, 0.01724704,  1.0000000000),

              output.names=c("US1","US2","US3"))

print(US_GD)

is.SS(US_GD)

stability(US_GD)

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

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

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

tfplot(US_GD.f)

AIC(m)

US_GDx <- SS(F=f,H=h,Q=eye(4,3),R=eye(3,3),z0=c(0.17033656, 0.01340404, 0.01724704,1.0000000000),

              output.names=c("US1","US2","US3"))

shockDecomposition(US_GDx,horizon=10,shock=rep(-1,10))