FR_L17 (1640-1815)

#

# FRL17 World  Model (1640-1815)

#

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   (Q+N-U), U, (Q-N)

#          Q      N      U

#[1,] 0.7047  0.692 -0.156

#[2,] 0.0296  0.191  0.981

#[3,] 0.7089 -0.696  0.114

# Fraction of Variance 

#[1] 0.669 0.998 1.000

#

# Fraction of Variance 

#[1] 0.987 0.995 0.999 1.000

#

f <- matrix( c(1.002716204, -0.0056439659,  0.1624389, 0.021539840,

                      0.018199696,  1.0287901927, -0.6625305, 0.028976135,

                     -0.002063291,  0.0001907678,  0.9864639, 0.000511435,

0.00000000,  0.0000000,  0.0000000,  1.0000000000

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

#

# To Stabilize, Uncomment next line

# f[1,1] <- f[2,2] <- 0.98

#

h <- eye(3,4)

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

FRL17 <- SS(F=f,H=h,K=k,z0=c(0.021539840, 0.028976135, 0.000511435,  1.0000000000),

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

print(FRL17)

is.SS(FRL17)

stability(FRL17)

FRL17.data <- (simulate(FRL17,sampleT=100))

#FRL17.data <- simulate(WL17,sampleT=150,noise=matrix(0,150,3))

FRL17.f <- forecast(l(FRL17,FRL17.data),horizon=150)

tfplot(FRL17.f)

AIC(m <- l(FRL17,FRL17.data))

shockDecomposition(toSSChol(m))