LAC_TECHP Model

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

#

#

# LA20 TECHP Model

#

merge.forecast <- function (fx,n=1) {

#

# Merges a forecast with the outputdata

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) (L-CO2-Q) (Q-CO2)

#        CO2  ENERGY    GDP      L

#[1,]  0.500  0.5023  0.501  0.497

#[2,] -0.478 -0.1153 -0.239  0.837

#[3,] -0.629  0.0028  0.765 -0.140

#

# Fraction of Variance 

#[1] 0.985 0.994 0.998 1.000

#

f <- matrix( c( 0.982054832, 0.2697719,  0.5844705,  0.142124856,

                      -0.009729417, 0.8757318, -0.3389483,  0.002128448,

                      -0.008366848, 0.1630589,  0.7409413, -0.007691149,

0.00000000,  0.0000000,  0.0000000,  1.0000000000

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

h <- eye(3,4)

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

LA_TECHP <- SS(F=f,H=h,K=k,z0=c( 0.142124856,  0.002128448, -0.007691149,  1.0000000000),

              output.names=c("TECHP1","TECHP2","TECHP3"))

print(LA_TECHP)

is.SS(LA_TECHP)

stability(LA_TECHP)

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

LA_TECHP.data <- simulate(LA_TECHP,sampleT=100,noise=matrix(0,100,3))

LA_TECHP.f <- forecast(l(LA_TECHP,LA_TECHP.data),horizon=100)

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

tfplot(LA_TECHP.f)

AIC(l(LA_TECHP,LA_TECHP.data))

#

#

#

# LAC_TECHP Input

#

# Measurement Matrix (Growth) (LU-EG-CO2) (N+L-CO2-Q)

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

#[1,]          0.412             0.412          0.411

#[2,]         -0.190            -0.239         -0.272

#[3,]         -0.532            -0.169         -0.252

#     SL.TLF.TOTL.IN SP.POP.TOTL SL.UEM.TOTL.ZS

#[1,]          0.413     0.41317          0.388

#[2,]         -0.144    -0.00377          0.901

#[3,]          0.497     0.59551         -0.152

#

# Fraction of Variance 

#[1] 0.972 0.997 0.999 1.000 1.000 1.000

#

f <- matrix( c( 0.997934675, -0.04333051,  0.5178460,  0.16294074,

                     -0.021285477,  0.92797742, -0.1762071, -0.01029681,

                     -0.002839782,  0.06006718,  0.6009844, -0.00569632,

0.00000000,  0.0000000,  0.0000000,  1.0000000000

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

g <- matrix( c( 0.023604896,  0.03881311,  0.05509400,

                        0.004775344, -0.04757128,  0.10122475,

                       -0.015304583, -0.03810903, -0.04934033,

                        0.000000000,  0.00000000,  0.00000000

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

h <- eye(3,4)

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

LAC_TECHP <- SS(F=f,H=h,K=k,G=g,z0=c( 0.2505234, -0.1123855, -0.10424173,  1.0000000000),

              output.names=c("LAC1","LAC2","LAC3"))

print(LAC_TECHP)

is.SS(LAC_TECHP)

stability(LAC_TECHP)

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

LAC_TECHP.data <- simulate(LAC_TECHP,sampleT=100,noise=matrix(0,100,3),input=LA_TECHP.fx)

LAC_TECHP.f <- forecast(l(LAC_TECHP,LAC_TECHP.data),conditioning.inputs=LA_TECHP.fx)

tfplot(LAC_TECHP.f)

AIC(l(LAC_TECHP,LAC_TECHP.data))