AF_L20 Model

#

# Cut-and-Paste Code Below into Window Above and Run

#

# AFL20 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)

#measurementModel(AF_SYS.index) # (Growth-Q) (EG+EF-CO2) (CO2+EG-EF)

#

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

#[1,]         -0.128            0.0769         -0.296        0.42915      0.4243          0.353

#[2,]         -0.412            0.5785          0.350       -0.11453     -0.1126          0.335

#[3,]          0.624            0.4836          0.378        0.00915     -0.0163          0.163

#        KOF     EF    HDI

#[1,]  0.418  0.232  0.416

#[2,] -0.132  0.442 -0.149

#[3,]  0.205 -0.339  0.223

#

# Fraction of Variance 

#[1] 0.559 0.773 0.899 0.955 0.980 0.992 0.998 1.000 1.000

#

f <- matrix( c(1.00000000,  0.00000000,  0.00000000,  0.000000000,

                      0.20868631,  1.06713077,  0.07168718,  0.009686743,

                     -0.05030444, -0.01693492,  0.73834599, -0.227031411,

                     -0.03200948, -0.08154583, -0.20562653,  0.719186457

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

#

# To Stabilize Model, Uncomment Next Line

# f[2,2] <- 0.96

#

h <- matrix(c(0,    1,    0,    0,

              0,    0,    1,    0,

              0,    0,    0,    1

),byrow=TRUE,nrow=3,ncol=4)

h;

k <- (f[1:4,2:4,drop=FALSE])

k;

AF <- SS(F=f,H=h,K=k,z0=c( 1.000000, -3.354772,  1.961430, -1.129931

),

              output.names=c("AF1","AF2","AF3"))

print(AF)

is.SS(AF)

stability(AF)

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

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

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

# tfplot(AF.f)

AIC(m)

AF.fx <- merge.forecast(AF.f)

tfplot(AF.fx)