DE19 W Input

# Cut-and-Paste Code Below

#

# DE19  Model W Input

#

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)

#

# W19 BAU Model

#

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

#

#         Q      N  XREAL      X

#[1,] 0.491  0.504  0.508  0.497

#[2,] 0.746 -0.344  0.158 -0.548

#[3,] 0.346 -0.492 -0.475  0.642

#

#[4,] 0.289  0.621 -0.701 -0.199

#

# Fraction of Variance 

#[1] 0.959 0.991 0.999 1.000

f <- matrix( c(1.017250893, -0.020318992, 0.05992186,  0.077760753,

                     0.017524849,  0.990743759, 0.04984382, -0.002539976,

                    -0.004654088 ,-0.009242275, 1.04850016, -0.004339670,

0.00000000,  0.0000000,  0.0000000,  1.0000000000

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

#

# To Stabilize Model, Uncomment Next Line

# f[1,1] <- 0.8054080; f[2,2] <- 0.78419528; f[3,3] <- 0.90340048

#

h <- eye(3,4)

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

W19 <- SS(F=f,H=h,K=k,z0=c(0.077760753, -0.002539976, -0.004339670,  1.0000000000),

              output.names=c("W1","W2","W3"))

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

W19.data <- simulate(W19,sampleT=100,noise=matrix(0,100,3),start=1800)

W19.f <- forecast(m1<-l(W19,W19.data),horizon=100)

W19.fx <- merge.forecast(W19.f)

#

# DE19 W input

#

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

#

#         Q      N      U  HOURS  XREAL      X      L

#[1,] 0.376  0.388  0.372  0.377  0.378  0.373  0.382

#[2,] 0.409 -0.112 -0.466 -0.408  0.394  0.457 -0.270

#[3,] 0.386 -0.237 -0.553  0.252 -0.138 -0.288  0.569

#

# Fraction of Variance 

#[1] 0.944 0.995 0.999 1.000 1.000 1.000 1.000

#

f <- matrix( c(0.8300617, -0.29328251, -0.04494436,  0.067200145,

                    -0.2584547,  0.80819961,  0.11307130, -0.021565758,

                     0.1529749,  0.04157157,  0.93105371,  0.008174919,

                        0.00000000,  0.000000000,  0.00000000,  1.00000000

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

#

# To Eliminate Feedback Effects, Uncomment Net Line

#       f[1,2] <- f[2,1] <- 0

#

g <- matrix(c(0.1996681,  0.24516375, 0.08854465,

                    0.2863709 ,  0.20489232, 0.02798465,

                    -0.1628984, -0.07189779, 0.03953764,

                      0.000000000,  0.00000000, 0.00000000

),byrow=TRUE,nrow=4,ncol=3)

h <- eye(3,4)

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

DE19 <- SS(F=f,H=h,K=k,G=g,z0=c(-0.1498229, -0.5198287,  0.3479083,  1.0000000000),

              output.names=c("DE1","DE2","DE3"))

print(DE19)

is.SS(DE19)

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

# tfplot(simulate(DE19,sampleT=100))

DE19.data <- simulate(DE19,sampleT=100,noise=matrix(0,100,3),input=W19.fx)

DE19.f <- forecast(m1<-l(DE19,DE19.data),conditioning.inputs=W19.fx)

tfplot(DE19.f)

AIC(m1)