DE1 Model

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

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# DE1 SYS Model

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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)

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# Measurement Matrix (Growth)  (EF+EG+CO2 -Q - LU) (N + KOF + EF - CO2)

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# Measurement Matrix 

#     EN.ATM.CO2E.KT EG.USE.COMM.KT.OE NY.GDP.MKTP.KD SL.TLF.TOTL.IN

#[1,]          0.333            0.3207         0.3460          0.360

#[2,]          0.286            0.4125        -0.2792         -0.119

#[3,]         -0.603            0.0753        -0.0969         -0.198

#     SP.POP.TOTL SL.UEM.TOTL.ZS    KOF    EF    HDI

#[1,]       0.344          0.348  0.361 0.206  0.353

#[2,]      -0.192         -0.226 -0.048 0.722 -0.213

#[3,]       0.620         -0.105  0.324 0.230 -0.168

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# Fraction of Variance 

#[1] 0.827 0.968 0.987 0.994 0.997 0.999 1.000 1.000 1.000

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f <- matrix( c(  0.95352, 0.1653579,

                        0.0000000, 1.0000000

),byrow=TRUE,nrow=2,ncol=2)

h <- eye(1,2)

k <- f[1:2,1,drop=FALSE]

DE1 <- SS(F=f,H=h,K=k,z0=c( 0.1653579,  1.0000000),

              output.names=c("DE1"))

tfplot(simulate(DE1,sampleT=150,noise=matrix(0,150,1),start=1950))

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