UK17_Model

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

#

# UK17 World  Model (1600-1700)

#

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-XREAL, N+XREAL-Q, L-Q-N, Q+XREAL-N

#

#          Q      N  XREAL

#[1,]  0.580  0.575 -0.577

#[2,] -0.163  0.776  0.609

#[3,]  0.798 -0.259  0.544

#

 #Fraction of Variance 

#[1] 0.989 1.000 1.000

f <- matrix( c(1.008476e+00,  0.018813271, 0.1236848,  0.0609753391,

                      7.994512e-03,  1.023957617, 0.1461305,  0.0022298776,

                      3.808761e-05, -0.006870376, 0.9675668, -0.0005602419,

                     0.000000e+00,   0.000000000,  0.0000000,   1.0000000000

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

#

# To stabilize, uncomment next line

# f[1,1] <- f[2,2] <- 0.96

#

h <- eye(3,4)

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

UK17 <- SS(F=f,H=h,K=k,z0=c(0.0609753391,  0.0022298776, -0.0005602419,  1.0000000000),

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

print(UK17)

is.SS(UK17)

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

# tfplot(simulate(UK17,sampleT=100))

UK17.data <- simulate(UK17,sampleT=150,noise=matrix(0,150,3))

UK17.f <- forecast(l(UK17,UK17.data),horizon=150)

tfplot(UK17.f)

AIC(m2 <- l(UK17,UK17.data))

shockDecomposition(m)