Colombia

# COLUMBIA L20 MODEL 

#

# Cut-and-Paste Code Below Into Window Above

#

require(dse)

require(matlab)

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-Q) (KOF-LU-CO2)

#

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

#[1,]          0.334            0.3401         0.3395         0.3423

#[2,]         -0.224            0.0126        -0.3089        -0.1177

#[3,]         -0.396            0.0640         0.0343         0.0984

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

#[1,]      0.3471             0.297 0.314  0.3413  0.3419

#[2,]     -0.0511             0.800 0.335 -0.2903 -0.0291

#[3,]      0.0203            -0.442 0.786 -0.0734 -0.0941

#

# Fraction of Variance 

# [1] 0.918 0.957 0.982 0.992 0.998 0.999 1.000 1.000 1.000

#

f <- matrix( c(0.99965839, -0.01426713,  0.03417140,  0.17092153,

                     -0.01930647,  1.00581165, -0.03915463, -0.03737075,

                     -0.01633222,  0.22481442,  0.99270033, -0.04213761,

0.00000000,  0.0000000,  0.0000000,  1.0000000000

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

#

# To Stabilize Model with Globalization

# Uncomment Following line

#    f[2,2]<- 0.8 ; f[3,3] <- .4;

h <- eye(3,4)

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

COL20 <- SS(F=f,H=h,K=k,z0=c( 0.17092153, -0.03737075, -0.042137610,  1.0000000000),

              output.names=c("CO1","CO2","CO3"))

print(COL20)

is.SS(COL20)

stability(m0 <- SS(F=f[1:3,1:3,drop=FALSE],H=eye(3),Q=eye(3), R=eye(3)))

# tfplot(simulate(COL20,sampleT=150))

COL20.data <- simulate(COL20,sampleT=150,noise=matrix(0,150,3))

COL20.f <- forecast(m <- l(COL20,COL20.data),horizon=150)

tfplot(COL20.f)

AIC(m)

shockDecomposition(m0)