Marx Collapse

#

# Marx Collapse (Cut and paste code into window above and Run (Cmd-Enter)

#

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 (Growth-R) (i-KU-K-R) (Growth-KU)

#          K     i     LU       R     KU

#[1,]  0.491 0.280 -0.526  0.5588  0.302

#[2,] -0.368 0.629 -0.276  0.0847 -0.621

#[3,]  0.120 0.694  0.232 -0.4852  0.463

#

# Fraction of Variance 

#[1] 0.611 0.964 0.989 0.998 1.000

#

f <- matrix( c(0.99214016,  0.10870884,  0.20308621, -0.03556595,

                    -0.05884381,  0.93788966, -0.05639899, -0.10266980,

                    -0.01817504, -0.01088793,  1.00819194,  0.02899585,

0.00000000,  0.0000000,  0.0000000,  1.0000000000

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

h <- eye(3,4)

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

Marx <- SS(F=f,H=h,K=k,z0=c(-0.03556595, -0.10266980,  0.02899585,  1.0000000000),

              output.names=c("Marx1","Marx2","Marx3"))

print(Marx)

is.SS(Marx)

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

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

Marx.data <- simulate(Marx,sampleT=100,noise=matrix(0,100,3))

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

tfplot(Marx.f)

AIC(l(Marx,Marx.data))