FR_LM_TECH Model

# Cut-and-paste code below into window above and Run

#

# France (FR_LM_TECH) Model (1950-2000)

#

#

#Measurement Matrix 

#      TECH  TECHH

#[1,] 0.707 -0.707

#[2,] 0.707  0.707

#

# Fraction of Variance 

#[1] 0.999 1.000

#

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)

f <- matrix( c( 0.988395552, 0.04526893, 0.0962767001,

                       0.001913753, 0.98461615, 0.0004648648,

                       0.000000000, 0.00000000, 1.0000000000

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

#

#

h <- eye(2,3)

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

FR_LM_TECH  <- SS(F=f,H=h,K=k,z0=c( 0.0962767001, 0.0004648648, 1.0000000000),

              output.names=c("FR_TECH1","FR_TECH2"))

print(FR_LM_TECH)

is.SS(FR_LM_TECH)

stability(FR_LM_TECH)

#FR_LM_TECH.data <- simulate(FR_LM_TECH,sampleT=100,start=1950)

FR_LM_TECH.data <- simulate(FR_LM_TECH,sampleT=100,noise=matrix(0,100,2),start=1950)

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

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

tfplot(FR_LM_TECH.f)

AIC(m <-  l(FR_LM_TECH,FR_LM_TECH.data))

FR_LM_TECHx <- SS(F=f,H=h,Q=eye(3,2),R=eye(2,2),z0=c(.0962767001, 0.000464864,  1.0000000000),

              output.names=c("FR_TECH1","FR_TECH2"))

shockDecomposition(FR_LM_TECHx)