JP1 Model

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

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# JP1 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), (LU-CO2), (CO2+LU-Q-L)

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

#[1,]          0.404             0.416        0.41333         0.4141

#[2,]         -0.472            -0.175        0.00738        -0.0346

#[3,]          0.594             0.163       -0.51541        -0.4879

#     SP.POP.TOTL SL.UEM.TOTL.ZS

#[1,]      0.4142          0.387

#[2,]     -0.1325          0.853

#[3,]     -0.0563          0.338

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# Fraction of Variance 

# [1] 0.951 0.982 0.994 0.999 1.000 1.000

f <- matrix( c(  0.9761515, 0.188116,

                        0.0000000, 1.0000000

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

h <- eye(1,2)

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

JP1 <- SS(F=f,H=h,K=k,z0=c( 0.188116,  1.0000000),

              output.names=c("JP1"))

tfplot(simulate(JP1,sampleT=150,noise=matrix(0,150,1),start=1950))

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