FR18 Model

# Cut-and-Paste Code Below into Window Above and Run

#

# FR18 BAU Model France

#

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)

#

# Measurement Matrix  Growth-U, U, N-L, Q-HOURS, Q-N

#

#          Q      N       U  HOURS      L

#[1,] 0.4719  0.460 -0.3528  0.470  0.470

#[2,] 0.0757  0.315  0.9208  0.153  0.153

#[3,] 0.1284  0.735 -0.1075 -0.465 -0.465

#[4,] 0.4025 -0.179  0.0589 -0.719  0.534

#[5,] 0.7701 -0.342  0.1128  0.150 -0.505

#

# Fraction of Variance 

#[1] 0.895 1.000 1.000 1.000 1.000

#

f <- matrix( c( 0.993145772, -0.021037069, -0.1528638,  0.064937629,

                       0.022886284,  1.049910086,  0.3377458,  0.018390009,

                       0.000623702, -0.004793843,  0.9569441, -0.001744027,

0.00000000,  0.0000000,  0.0000000,  1.0000000000

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

#

# To Stabilize, Uncomment next line

# f[2,2] <- 0.90

#

h <- eye(3,4)

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

FR18 <- SS(F=f,H=h,K=k,z0=c(0.064937629,  0.018390009, -0.001744027,  1.0000000000),

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

print(FR18)

is.SS(FR18)

stability(m <- SS(F=f[1:3,1:3,drop=FALSE],Q=eye(3),R=eye(3),H=eye(3)))

# tfplot(simulate(FR18,sampleT=100))

FR18.data <- simulate(FR18,sampleT=100,noise=matrix(0,100,3),start=1700)

FR18.f <- forecast(l(FR18,FR18.data),horizon=150)

tfplot(FR18.f)

AIC(l(FR18,FR18.data))

shockDecomposition(m)