UK_E20 Model

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

#

# UK_E20 Model

#

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

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#         Q      N      U   HOURS   XREAL       X      L

#[1,]  0.393  0.3980  0.4139 -0.389 -0.234 0.382  0.4044

#[2,]  0.332 -0.0935 -0.0245 -0.236  0.886 0.177 -0.0875

#[3,] -0.133 -0.4756 -0.1471 -0.440 -0.273 0.571 -0.3739

#

 #Fraction of Variance 

#[1]  0.828 0.947 0.993 1.000 1.000 1.000 1.000

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AIC <- function(model) {informationTestsCalculations(model)[3]}

require(dse)

require(matlab)

f <- matrix( c(1.003480735, -0.03861603, -0.008671156,  0.14766638,

                      0.039874097,  1.06097693,  0.162696069,  0.04785732,

                      0.005711577, -0.11211210,  0.938690394, -0.04842753,

0.00000000,  0.0000000,  0.0000000,  1.0000000000

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

#

# To create RWf model, uncomment following line

# f[1,1] <- f[2,2] <- f[3,3] <- 1.0

#

# To Stabilize, uncomment next line

# f[1,1] <-  f[3,3];  f[2,2] <- f[3,3];

#

h <- eye(3,4)

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

UK_E20 <- SS(F=f,H=h,K=k,z0=c( 0.12654495, 0.10806276, 0.02649331,  1.0000000000),

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

print(UK_E20)

is.SS(UK_E20)

stability(UK_E20)

UK_E20.data <- simulate(UK_E20,sampleT=50,start=1900)

# UK_E20.data <- simulate(UK_E20,sampleT=20,noise=matrix(0,20,3))

UK_E20.f <- forecast(l(UK_E20,UK_E20.data),horizon=50)

tfplot(UK_E20.f)

AIC(l(UK_E20,UK_E20.data))

shockDecomposition(toSSChol(UK_E20),horizon=10,shock=rep(-1,10))