ES1 Model

#

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

#

# ES1 SYS Model

#

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), (LU-EF), (EF+LU-EG-GDP)

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

#[1,]         0.3527            0.3500         0.3554         0.3315

#[2,]        -0.0307            0.0362        -0.0771        -0.3856

#[3,]        -0.0940           -0.3823        -0.2095         0.0683

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

#[1,]      0.3541          0.243  0.300  0.341  0.355

#[2,]      0.0135          0.778 -0.447  0.153  0.117

#[3,]      0.0817          0.489  0.684 -0.259 -0.127

#

# Fraction of Variance 

#[1] 0.862 0.948 0.983 0.997 0.999 1.000 1.000 1.000 1.000

#

f <- matrix( c(  1.021391, 0.2012487,

                        0.0000000, 1.0000000

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

#

# To Stabilize Model, Uncomment Next Line

# f[1,1] <- 0.99

#

h <- eye(1,2)

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

ES1 <- SS(F=f,H=h,K=k,z0=c( 0.4068021,  1.0000000),

              output.names=c("FR1"))

tfplot(simulate(ES1,sampleT=150,noise=matrix(0,150,1),start=1950))