NEU_LM Model

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

#

# Northern Europe (NEU_LM_TECH) Model (1950-2000)

#

#

# Measurement Matrix Overall, (X-N-L), (N-L)

#          Q      N  XREAL     X      L

#[1,]  0.458  0.446  0.452 0.431  0.448

#[2,] -0.104 -0.469  0.335 0.689 -0.428

#[3,] -0.180  0.734 -0.262 0.266 -0.538

# Fraction of Variance 

#[1] 0.95 1.00 1.00 1.00 1.00

#

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( 1.0046421227, -0.021958534,  1.0373546, 0.144854912,

                       0.0312022556,  1.00473308, -2.1137973, 0.003654146,

                      -0.0001939729,  0.002444952,  0.8655436, 0.000553031,

                       0.0000000000,  0.000000000,  0.0000000, 1.000000000

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

#

# To Create a Steady State, uncomment Next Line

#

# f[1,1] <- 0.9945957015; f[2,2] <-  0.994685754; f[3,3] <- 0.8568882

#

h <- eye(3,4)

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

NEU_LM  <- SS(F=f,H=h,K=k,z0=c( 0.144854912, 0.003654146, 0.000553031, 1.000000000),

              output.names=c("NEU_LM1","NEU_LM2","NEU_LM3"))

print(NEU_LM)

is.SS(NEU_LM)

stability(NEU_LM)

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

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

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

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

tfplot(NEU_LM.f)

AIC(m <-  l(NEU_LM,NEU_LM.data))

NEU_LMx <- SS(F=f,H=h,Q=eye(4,3),R=eye(3,3),z0=c( 0.144854912, 0.003654146, 0.000553031,  1.0000000000),

              output.names=c("NEU_LM1","NEU_LM2","NEU_LM3"))

shockDecomposition(NEU_LMx)

AIC(m <-  l(NEU_LM,NEU_LM.data))