CH_LM Model

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

#

# Switzerland (CH) Model (1950-2000)

#

#

# Measurement Matrix  (Growth), (X-HOURS), (U-HOURS-X)

#

#          Q      N     U  HOURS  XREAL      X       L

#[1,]  0.389  0.388 0.388  0.354  0.387  0.345  0.3906

#[2,] -0.145 -0.199 0.180 -0.618  0.210  0.687 -0.0917

#[3,]  0.212  0.275 0.547 -0.566 -0.224 -0.446  0.1031

#

# Fraction of Variance 

#[1] 0.933 1.000 1.000 1.000 1.000 1.000 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( 0.986091451, -0.02842443,  0.4585358, 0.165226628,

                      0.034833406,  1.02993562, -0.5527579, 0.014032953,

                     -0.001291216,  0.01073976,  0.8885394, 0.003541658,

0.00000000,  0.0000000,  0.0000000,  1.0000000000

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

#

# To stabilize model, uncomment next line

# f[2,2] <- .90

#

h <- eye(3,4)

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

CH_LM <- SS(F=f,H=h,K=k,z0=c(0.165226628, 0.014032953 , 0.003541658,  1.0000000000),

              output.names=c("CH1","CH2","CH3"))

print(CH_LM)

is.SS(CH_LM)

stability(CH_LM)

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

CH_LM.data <- simulate(CH_LM,sampleT=100,noise=matrix(0,100,3),start=1950)

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

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

tfplot(CH_LM.f)

AIC(m <-  l(CH_LM,CH_LM.data))

CH_LMx <- SS(F=f,H=h,Q=eye(4,3),R=eye(3,3),z0=c(00.165226628, 0.014032953 , 0.003541658,  1.0000000000),

              output.names=c("CH1","CH2","CH3"))

     shockDecomposition(CH_LMx)

#

# RANDOM WALK
#     

CH_LM_RW <- SS(F=eye(4,4),H=h,Q=eye(4,3),R=eye(3,3),z0=c(0.165226628, 0.014032953 , 0.003541658,  1.0000000000),

              output.names=c("CH1","CH2","CH3"))

for (i in 1:5) {

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

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

  tfplot(m)

}