UK_LM Model

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

#

# United Kingdom (UK) Model (1950-2000)

#

#

 #Measurement Matrix   (Growth) (HOURS) (X-N)

 #

#           Q       N       U   HOURS   XREAL        X      L

#[1,]  0.4121  0.3976  0.4107  0.1339  0.4055  0.39230  0.409

#[2,] -0.0721 -0.0962 -0.0936  0.9871 -0.0476 -0.00396 -0.012

#[3,] -0.0455 -0.5402 -0.1348 -0.0507  0.3950  0.66143 -0.303

#

# Fraction of Variance 

#[1] 0.837 0.968 1.000 1.000 1.000 1.000 1.000

#

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)

f <- matrix( c(   1.00544958, 0.006285156, -0.09808932,  0.149479839,

                       -0.02516952, 1.050391146, -0.44954346, -0.099299692,

                         0.02889187, 0.005627003,  1.01925177,  0.007017427,

0.00000000,  0.0000000,  0.0000000,  1.0000000000

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

#

# To stabilize model, uncomment next line

# f[1,1] <- f[2,2] <- f[3,3] <-   .98

#

h <- eye(3,4)

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

UK_LM <- SS(F=f,H=h,K=k,z0=c(0.149479839, -0.099299692,  0.007017427,  1.0000000000),

              output.names=c("UK1","UK2","UK3"))

print(UK_LM)

is.SS(UK_LM)

stability(UK_LM)

UK_LM.data <- simulate(UK_LM,sampleT=100,start=1950)

#UK_LM.data <- simulate(UK_LM,sampleT=100,noise=matrix(0,100,3),start=1950)

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

UK_LM.fx <- merge.forecast(UK_LM.f)

tfplot(UK_LM.f)

AIC(m <-  l(UK_LM,UK_LM.data))

UK_LMx <- SS(F=f,H=h,Q=eye(4,3),R=eye(3,3),z0=c(0.149479839, -0.099299692,  0.007017427,  1.0000000000),

              output.names=c("UK1","UK2","UK3"))

     shockDecomposition(UK_LMx)