FRL20 TECH

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# FR TECH MODELS

#

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

#

#

# FRL20 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 (Q+L+EG-CO2) (CO2+E+Q-L) (L+CO2-EG)

#

#        CO2 ENERGY    GDP      L

#[1,] -0.390  0.502  0.571  0.520

#[2,]  0.763  0.541  0.262 -0.237

#[3,]  0.506 -0.317 -0.100  0.796

#

# Fraction of Variance 

#[1] 0.717 0.927 0.998 1.000

#

# Fraction of Variance 

#[1] 0.985 0.994 0.998 1.000

#

f <- matrix( c( 1.00131161,  0.04449442, -0.07097867, 0.08711843,

                     -0.05137184,  0.87266880, -0.01075242, 0.03163489,

                       0.02075841, -0.05766706,  0.96359190, 0.02414442,

0.00000000,  0.0000000,  0.0000000,  1.0000000000

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

h <- eye(3,4)

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

FR_TECHP <- SS(F=f,H=h,K=k,z0=c( 0.08711843, 0.03163489, 0.02414442,  1.0000000000),

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

print(FR_TECHP)

is.SS(FR_TECHP)

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

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

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

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

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

tfplot(FR_TECHP.f)

AIC(l(FR_TECHP,FR_TECHP.data))

#

#    FRL20 TECHE Model

#

#

# Measurement Matrix (GDP+L-EG-CO2) (L+CO2-Q-EG) (EG+L+Q)

#

#        CO2 ENERGY    GDP      L

#[1,] -0.5039 -0.505  0.516 0.474

#[2,]  0.5317 -0.307 -0.404 0.678

#[3,] -0.0739  0.805  0.212 0.549

#

# Fraction of Variance 

#[1] 0.861 0.963 0.995 1.000

#

f <- matrix( c( 1.0036722797, -0.06557970, 0.13593529, 0.104894812,

                      0.0371672735,  0.97305553, 0.05352367, 0.005908280,

                     -0.0001591622,  0.01872759, 0.67418737, 0.007143925,

0.00000000,  0.0000000,  0.0000000,  1.0000000000

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

h <- eye(3,4)

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

FR_TECHE <- SS(F=f,H=h,K=k,z0=c(  0.104894812, 0.005908280, 0.007143925,  1.0000000000),

              output.names=c("TECHE1","TECHE2","TECHE3"))

print(FR_TECHE)

is.SS(FR_TECHE)

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

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

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

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

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

tfplot(FR_TECHE.f)

AIC(l(FR_TECHE,FR_TECHE.data))