RU1 Model

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

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# RU1 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) (KOF+ LU  - CO2 - EF ) (Q - EF LU- N)

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#     EN.ATM.CO2E.KT EG.USE.COMM.KT.OE NY.GDP.MKTP.KD

#[1,]         0.2821             0.406          0.334

#[2,]        -0.4399            -0.220         -0.136

#[3,]         0.0643            -0.145          0.594

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

#[1,]         0.4343       0.372          0.189 0.4340  0.235 0.195

#[2,]         0.1328       0.227          0.497 0.0341 -0.395 0.516

#[3,]         0.0494      -0.394         -0.337 0.2254 -0.488 0.247

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# Fraction of Variance 

#[1] 0.549 0.848 0.968 0.981 0.992 0.997 0.999 1.000 1.000

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f <- matrix( c(  0.9685498, 0.1510723,

                        0.0000000, 1.0000000

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

h <- eye(1,2)

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

RU1 <- SS(F=f,H=h,K=k,z0=c(  0.1510723,  1.0000000),

              output.names=c("RU1"))

tfplot(simulate(RU1,sampleT=150,noise=matrix(0,150,1),start=1950))

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