CH_LM Model Switzerland

#

# CH_LM Model (Switzerland)

#

AIC <- function(model) {informationTestsCalculations(model)[3]}

require(dse)

require(matlab)

#

# MEASUREMENT MATRIX

#

#          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

#

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,

                      0.000000000,  0.00000000,  0.0000000, 1.000000000

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

#

# To Stabilize System Uncomment Next Line

# f[1,1] <- 0.976230536; f[2,2] <- 1.01963627; f[3,3] <- 0.8796540

#

h <- eye(3,4)

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

CH_LM <- SS(F=f,H=h,K=k,z0=c(0.329380291, 0.032283718, 0.006625928,  1.0000000000),

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

print(CH_LM)

is.SS(CH_LM)

stability(CH_LM)

# tfplot(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(m <- l(CH_LM,CH_LM.data),horizon=100)

tfplot(CH_LM.f)

AIC(m)

shockDecomposition(toSSChol(CH_LM))