FR_LM Model

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

#

# France (FR_LM) Model (1950-2000)

#

#

# Measurement Matrix Growth-HOURS), (HOURS+N-X), (L-X-XREAL)

#           Q      N      U  HOURS  XREAL      X     L

#[1,]  0.3847 0.3761  0.386 -0.354  0.387  0.376 0.380

#[2,]  0.2382 0.4172  0.190  0.669 -0.136 -0.407 0.318

#[3,] -0.0996 0.0608 -0.157 -0.561 -0.415 -0.438 0.532

#

 #Fraction of Variance 

#[1] 0.945 0.998 0.999 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( 1.011467560, 0.04201879,  0.0927857,  0.1703822402,

                     -0.025432137, 0.98935295, -0.1283416,  0.0009346911,

                     -0.008087302, 0.01927737,  1.0987168, -0.0064823813,

0.00000000,  0.0000000,  0.0000000,  1.0000000000

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

#

# To stabilize model, uncomment next line

# f[1,1] <- 0.952276630;  f[2,2] <- 0.93145617;  f[3,3] <-   1.0344201

# To create a Steady-State Economy, uncomment next line

# f[1,1] <- 0.952276630;  f[2,2] <- 0.93145617;  f[3,3] <-   0.92

#

h <- eye(3,4)

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

FR_LM <- SS(F=f,H=h,K=k,z0=c(0.1703822402,  0.0009346911, -0.0064823813,  1.0000000000),

              output.names=c("FR1","FR2","FR3"))

print(FR_LM)

is.SS(FR_LM)

stability(FR_LM)

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

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

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

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

tfplot(FR_LM.f)

AIC(m <-  l(FR_LM,FR_LM.data))

FR_LMx <- SS(F=f,H=h,Q=eye(4,3),R=eye(3,3),z0=c(0.1703822402,  0.0009346911, -0.0064823813,  1.0000000000),

              output.names=c("FR1","FR2","FR3"))

shockDecomposition(FR_LMx)

AIC(m <-  l(FR_LM,FR_LM.data))