ITL20 Italy

#

# ITL20 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) (KOF+HDI-LU-CO2-EF)   (LU+KOF-N-CO2-EG)

#

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

#[1,]          0.339            0.3443         0.3484         0.3405       0.334

#[2,]         -0.294           -0.0659         0.1960         0.0237      -0.127

#[3,]         -0.227           -0.2070         0.0294        -0.1931      -0.398

#     SL.UEM.TOTL.ZS   KOF      EF   HDI

#[1,]          0.302 0.311  0.3345 0.343

#[2,]         -0.438 0.705 -0.2886 0.287

#[3,]          0.795 0.208 -0.0757 0.167

#

# Fraction of Variance 

#[1] 0.897 0.941 0.973 0.992 0.997 0.999 0.999 1.000 1.000

#

f <- matrix( c(0.968655232, -0.02598538, 0.03516609,  0.179538123,

                      0.024010353,  0.97990001, 0.04937189,  0.002069449,

                      0.008391537, -0.09476080, 0.9427935,3 -0.024306816,

0.00000000,  0.0000000,  0.0000000,  1.0000000000

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

h <- eye(3,4)

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

ITL20 <- SS(F=f,H=h,K=k,z0=c( 0.179538123,  0.002069449, -0.024306816,  1.0000000000),

              output.names=c("IT1","IT2","IT3"))

print(ITL20)

is.SS(ITL20)

stability(m1<-SS(F=f[1:3,1:3,drop=FALSE],Q=eye(3),R=eye(3),H=eye(3)))

shockDecomposition(m1)

# tfplot(simulate(ITL20,sampleT=100))

ITL20.data <- simulate(ITL20,sampleT=150,noise=matrix(0,150,3),start=1960)

ITL20.f <- forecast(m <- l(ITL20,ITL20.data),horizon=150)

tfplot(ITL20.f)

AIC(m)

# Bootstrap 98% Confidence Intervals on System Matrix

#

# [1,]  0.9509829  0.968655  0.98376    0.44    0.56  0.013122  0.004095

# [2,]  0.0203420  0.024010  0.02901    0.61    0.39  0.003766  0.943208

# [3,]  0.0007377  0.008392  0.01722    0.41    0.59  0.006715  0.961059

# [4,] -0.0753380 -0.025985  0.02028    0.57    0.43  0.053785  0.989819

# [5,]  0.9603155  0.979900  1.00068    0.33    0.67  0.016208 -0.002817

# [6,] -0.1262214 -0.094761 -0.07080    0.28    0.72  0.021919  1.079435

# [7,] -0.0348785  0.035166  0.09374    0.52    0.48  0.053800  0.933050

# [8,]  0.0198501  0.049372  0.07780    0.61    0.39  0.021761  0.912290

# [9,]  0.9113850  0.942794  0.98247    0.28    0.72  0.029367  0.039541

#[10,]  0.1574308  0.179538  0.20413    0.68    0.32  0.020658  0.777594

#[11,] -0.0154832  0.002069  0.01767    0.44    0.56  0.012102  0.967430

#[12,] -0.0396002 -0.024307 -0.01254    0.42    0.58  0.013663  0.996519

#