ES18 BAU Model

#

# ES18 BAU Model Spain

#

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 # overall-X, Q-U, U-HOURS, Q-N, N-U, X-XREAL

#

#            Q        N       U     HOURS   XREAL       X

#[1,]  0.40525  0.44332  0.3813  3.53e-01 -0.4297 -0.4297

#[2,]  0.44612 -0.00431 -0.5123 -6.23e-01 -0.2748 -0.2748

#[3,] -0.07459  0.19228  0.6831 -6.91e-01  0.0834  0.0834

#

#[4,]  0.79330 -0.25152  0.1743  1.05e-01  0.3647  0.3647

#[5,]  0.04260  0.82894 -0.3050 -1.67e-16  0.4193  0.2055

#[6,]  0.00652  0.12678 -0.0466  1.94e-16 -0.6512  0.7468

#

# Fraction of Variance 

#[1] 0.845 0.984 1.000 1.000 1.000 1.000

#

f <- matrix( c( 1.021908444, -0.006660552,  0.008169387,  0.08305786,

                     -0.034558543,  1.021383495, -0.024500402, -0.01036956,

                     -0.002627521,  0.027285222,  0.991456560, -0.01076334,

            0.00000000,  0.0000000,  0.0000000,  1.0000000000

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

#

# To Stabilize, Uncomment next line

# f[1,1] <- f[2,2] <- 0.90

#

h <- eye(3,4)

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

ES18 <- SS(F=f,H=h,K=k,z0=c(0.08305786, -0.01036956, -0.01076334,  1.0000000000),

              output.names=c("ES1","ES2","ES3"))

print(ES18)

is.SS(ES18)

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

# tfplot(simulate(ES18,sampleT=100))

ES18.data <- simulate(ES18,sampleT=100,noise=matrix(0,100,3),start=1700)

ES18.f <- forecast(l(ES18,ES18.data),horizon=150)

tfplot(ES18.f)

AIC(l(ES18,ES18.data))

shockDecomposition(m)